commit fae2db8cff25c4d4573c45f7d39a649fbf9ee0f0 Author: BattleTag Date: Thu May 7 20:47:30 2026 +0800 Initial commit: code, paper, small artifacts diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..420009f --- /dev/null +++ b/.gitignore @@ -0,0 +1,25 @@ +.venv/ +venv/ +env/ +__pycache__/ +*.pyc +*.pyo +*.pyd +*.egg-info/ +.pytest_cache/ +.mypy_cache/ +.ruff_cache/ + +.DS_Store +Thumbs.db +.idea/ +.vscode/ +.claude/ +*.swp +*.swo + +/datasets/ + +/baselines/ + +*.tmp diff --git a/CLAUDE.md b/CLAUDE.md new file mode 100644 index 0000000..599b991 --- /dev/null +++ b/CLAUDE.md @@ -0,0 +1,172 @@ +# CLAUDE.md + +This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository. + +## Repo shape + +This is a **workspace-style repo with three sibling model packages** plus a +shared data contract. The root intentionally keeps only workspace-level +files; all model/training/eval code lives under one of the three packages. + +- `common/data_contract.py` — **single source of truth** for the canonical + 9-d packet schema (`PACKET_FEATURE_NAMES`) and 20-d packet-derived flow + schema (`CANONICAL_FLOW_FEATURE_NAMES`), label normalization, canonical + 5-tuple, packet preprocessing helpers, and `compute_flow_features_from_packets`. + All three packages import from here. +- `Packet_CFM/` — packet-sequence OT-CFM with explicit σ-band benign + distribution learning. Has its own `CLAUDE.md` for internal details. +- `Flow_CFM/` — flow-level CFM on the workspace-canonical 20-d packet-derived + `flow_features.parquet`. Legacy 61-d CICFlowMeter CSV caches are still + available only for reproduction via the `--legacy-csv-features` flag. +- `Unified_CFM/` — **current SOTA model**. Unified token CFM over + `[FLOW_TOKEN, PACKET_1, ..., PACKET_T]` with masked-prediction consistency + loss (Phase 2). All within-dataset SOTAs (ISCXTor2016 / CICIDS2017 / + CICDDoS2019) come from here. +- `scripts/` — **workspace-level** scripts shared across all packages: + - `download/` — UNB/CIC dataset downloaders (Token-cookie + `cic_download.py` + recursive crawler). See `scripts/download/README.md` before touching. + - `extract_.py` + `extract_lib.py` — pcap→artifact drivers that write + `datasets//processed/{packets.npz, flows.parquet, flow_features.parquet}`, + all row-aligned by `flow_id = arange(N)`. + - `generate_flow_features.py` — one-shot tool to upgrade an existing + `packets.npz` + `flows.parquet` pair to a canonical `flow_features.parquet` + without re-extracting pcap. Supports `--source-store` for sharded stores. + - `csv_adapter.py`, `convert_npz_splits_to_store.py`, `eval_cross_dataset_protocol.py`, + `merge_*.py`, `auto_transfer_*.sh` — cross-package tooling. +- `datasets//raw/` and `datasets//processed/` — shared dataset store. +- `artifacts/{runs,phase0_*,phase1_*,phase25_*,verify_*}/` — **all outputs go + here**, not `runs/` at root. Phase summary reports live in `artifacts/phase*/`. +- `paper/` — paper PDFs we compare against (Shafir 2026 NF, ConMD 2026, + TIPSO-GAN 2026, Lipman 2210.02747). + +There is no `archive_v1/` at root; old flow-stat v1 code has been removed. +`Flow_CFM/checkpoints_archive/` retains historical checkpoints for reproduction. + +## Data contract (read this before touching data code) + +Every processed dataset under `datasets//processed/` ships an aligned +triple, all with the same row order (`flow_id = arange(N)`): + +``` +packets.npz # packet_tokens [N, T_full, 9], packet_lengths [N], flow_id [N] + # OR full_store/ (PacketShardStore directory) for large datasets +flows.parquet # flow_id + label + 5-tuple metadata (src_ip, dst_ip, ports, protocol) +flow_features.parquet # flow_id + label + 20 canonical packet-derived features +``` + +Optional / legacy: +- `flow_features_csv.parquet` — Flow_CFM's 61-d CICFlowMeter cache (paper + reproduction only; not row-aligned with packets in general) + +The 20 canonical flow features are computed by +`common.data_contract.compute_flow_features_from_packets(packet_tokens, lens)` +and cover Shafir 2026's top-SHAP categories (size/IAT/active-idle/rate/flags) +in a packet-derivable way. + +## Python env + +- `requires-python = ">=3.14"`; PyTorch pinned to the `pytorch-cu128` index + (`torch>=2.9.1`), plus `mamba-ssm`, `causal-conv1d`, `scapy`, `dpkt`, `pyarrow`. +- Two `pyproject.toml` files: root (`/pyproject.toml`) and `Packet_CFM/pyproject.toml`. + They are **not declared as a uv workspace** — each resolves independently. + Run `uv run ...` from whichever directory owns the entry point you are invoking. +- `Flow_CFM/` and `Unified_CFM/` have no `pyproject.toml`; they use the root + venv (`uv run --no-sync python `). +- Scripts under `scripts/download/` are pure stdlib — invoke with `python3`. + +## Running things + +**Unified_CFM** (SOTA model, run from `Unified_CFM/`): + +```bash +cd Unified_CFM +uv run --no-sync python train.py --config configs/cicids2017_baseline.yaml +# Phase 2 with consistency loss: +uv run --no-sync python train.py --config configs/cicids2017_consistency.yaml +``` + +Best hyperparameters from the σ × λ sweeps: +- `lambda_flow = lambda_packet = 0.3` +- `sigma = 0.6` for cross-dataset transfer +- `sigma = 0.1` is fine for within-dataset (and marginally better on ISCXTor2016) + +**Phase 1 / 2 evaluation**: + +```bash +# Per-attack-class AUROC over 34 scores (terminal_norm primary, plus curvature, +# Jacobian-Hutchinson, time-profile velocity, flow_consistency diagnostics). +uv run --no-sync python artifacts/verify_2026_04_24/eval_phase1_unified.py \ + --model-dir --out-dir \ + --batch-size 256 --jacobian-n-eps 4 \ + --n-val-cap 10000 --n-atk-cap 30000 + +# Cross-dataset CICIDS2017 → CICDDoS2019: +uv run --no-sync python artifacts/verify_2026_04_24/eval_phase2_cross_cicddos2019.py \ + --model-dir --out \ + --n-benign 10000 --n-attack 10000 --seed 42 +``` + +**Packet_CFM entry points** (run from `Packet_CFM/`): + +```bash +cd Packet_CFM +uv run python -m train --config configs/n10k.yaml +uv run python -m detect --save-dir ../artifacts/runs/ +uv run python -m eval.per_class --save-dir ../artifacts/runs/ +uv run python -m run_phase1 --sigmas 0.0 0.1 0.2 0.3 +``` + +**Flow_CFM entry points** (run from `Flow_CFM/`): see `Flow_CFM/README_migration.md`. + +**Tests**: + +```bash +uv run --no-sync python -m pytest Packet_CFM/tests/ tests/common/ Unified_CFM/tests/ +``` + +(43 passing — common data contract + Unified_CFM Phase 1/2 score functions ++ Packet_CFM existing tests.) + +## Adding a new dataset + +Write one driver at `scripts/extract_.py` that calls +`extract_lib.extract_dataset(...)` (see `scripts/extract_cicids2017.py` as +the reference template). The driver hardcodes CSV column names, timestamp +formats, benign aliases, and drop patterns as module constants, then feeds +`extract_lib` a per-day `(canonical_key → [(row_idx, ts_epoch)])` mapping +and a per-day pcap file map. No YAML is needed. + +The extract pipeline writes all three artifacts (packets.npz, flows.parquet, +flow_features.parquet) row-aligned. To upgrade an existing artifact pair +that lacks `flow_features.parquet`, run +`scripts/generate_flow_features.py --packets-npz ... --flows-parquet ... --out ...` +(or `--source-store` for sharded stores). + +Common gotcha: if CSV timestamps and pcap epochs are in different time zones, +`extract_lib` prints a diagnostic with the recommended `--time-offset`; rerun +with that value. + +## Conventions worth preserving + +- Do not create a new `runs/` at repo root — outputs belong under `artifacts/`. +- `scripts/download/` stays at the root (shared by all packages). +- When adding new cross-package tooling, put it in root `scripts/`. Only move + it into `Packet_CFM/scripts/` if it depends on that package's imports. +- Phase reports live in `artifacts/phase*/` — keep the timestamp suffix + (`_2026_04_25`) so future runs don't overwrite history. +- The 9-d packet schema and 20-d canonical flow schema are FIXED in + `common/data_contract.py`. Do not extend them ad-hoc; if you need new + features, propose them with evidence (Shafir-style SHAP analysis or + Phase 1-style per-attack ablation). + +## Current state of the work (2026-04-25) + +- Phase 0 baselines + Shafir-protocol verification: ✓ +- Phase 1 (34-score expansion + per-attack-class table): ✓ +- Phase 2 (masked-prediction consistency loss): ✓ — multi-seed at λ=0.3 +- Phase 2.5 (σ × λ sweep + multi-seed at σ=0.6): ✓ +- Cross-dataset multi-seed: ✓ — also SOTA after baseline lock +- Shafir baselines locked from PDF: ✓ — `artifacts/locked_baselines.md` +- 4 of 4 reported tasks beat Shafir SOTA (final table: `RESULTS.md`) +- Architecture is finalized; remaining work is paper writing + (P1 skeleton, P2 thresholded F1/Precision/Recall metrics). diff --git a/Mixed_CFM/__init__.py b/Mixed_CFM/__init__.py new file mode 100644 index 0000000..2ae2839 --- /dev/null +++ b/Mixed_CFM/__init__.py @@ -0,0 +1 @@ +pass diff --git a/Mixed_CFM/configs/cicddos2019_ac_combo_seed42.yaml b/Mixed_CFM/configs/cicddos2019_ac_combo_seed42.yaml new file mode 100644 index 0000000..fac97e9 --- /dev/null +++ b/Mixed_CFM/configs/cicddos2019_ac_combo_seed42.yaml @@ -0,0 +1,42 @@ + +save_dir: /home/chy/JANUS/artifacts/route_comparison/route_ac_combo_cicddos2019_seed42 + +source_store: /home/chy/JANUS/datasets/cicddos2019/processed/full_store +flows_parquet: /home/chy/JANUS/datasets/cicddos2019/processed/flows.parquet +flow_features_path: /home/chy/JANUS/datasets/cicddos2019/processed/flow_features.parquet +flow_features_align: auto + +T: 64 +n_train: 10000 +min_len: 2 +seed: 42 +data_seed: 42 +train_ratio: 0.8 +benign_label: normal +val_cap: 20000 +attack_cap: 20000 + +d_model: 128 +n_layers: 4 +n_heads: 4 +mlp_ratio: 4.0 +time_dim: 64 +token_dim: + +batch_size: 256 +num_workers: 0 +epochs: 50 +lr: 3.0e-4 +weight_decay: 0.01 +grad_clip: 1.0 +eval_every: 10 +eval_n: 10000 +eval_batch_size: 512 +eval_n_steps: 8 + +sigma: 0.1 +use_ot: true +lambda_disc: 1.0 +reference_mode: causal_packets + +device: auto diff --git a/Mixed_CFM/configs/cicddos2019_ac_combo_seed43.yaml b/Mixed_CFM/configs/cicddos2019_ac_combo_seed43.yaml new file mode 100644 index 0000000..b8d1707 --- /dev/null +++ b/Mixed_CFM/configs/cicddos2019_ac_combo_seed43.yaml @@ -0,0 +1,42 @@ + +save_dir: /home/chy/JANUS/artifacts/route_comparison/route_ac_combo_cicddos2019_seed43 + +source_store: /home/chy/JANUS/datasets/cicddos2019/processed/full_store +flows_parquet: /home/chy/JANUS/datasets/cicddos2019/processed/flows.parquet +flow_features_path: /home/chy/JANUS/datasets/cicddos2019/processed/flow_features.parquet +flow_features_align: auto + +T: 64 +n_train: 10000 +min_len: 2 +seed: 43 +data_seed: 43 +train_ratio: 0.8 +benign_label: normal +val_cap: 20000 +attack_cap: 20000 + +d_model: 128 +n_layers: 4 +n_heads: 4 +mlp_ratio: 4.0 +time_dim: 64 +token_dim: + +batch_size: 256 +num_workers: 0 +epochs: 50 +lr: 3.0e-4 +weight_decay: 0.01 +grad_clip: 1.0 +eval_every: 10 +eval_n: 10000 +eval_batch_size: 512 +eval_n_steps: 8 + +sigma: 0.1 +use_ot: true +lambda_disc: 1.0 +reference_mode: causal_packets + +device: auto diff --git a/Mixed_CFM/configs/cicddos2019_ac_combo_seed44.yaml b/Mixed_CFM/configs/cicddos2019_ac_combo_seed44.yaml new file mode 100644 index 0000000..204506a --- /dev/null +++ b/Mixed_CFM/configs/cicddos2019_ac_combo_seed44.yaml @@ -0,0 +1,42 @@ + +save_dir: /home/chy/JANUS/artifacts/route_comparison/route_ac_combo_cicddos2019_seed44 + +source_store: /home/chy/JANUS/datasets/cicddos2019/processed/full_store +flows_parquet: /home/chy/JANUS/datasets/cicddos2019/processed/flows.parquet +flow_features_path: /home/chy/JANUS/datasets/cicddos2019/processed/flow_features.parquet +flow_features_align: auto + +T: 64 +n_train: 10000 +min_len: 2 +seed: 44 +data_seed: 44 +train_ratio: 0.8 +benign_label: normal +val_cap: 20000 +attack_cap: 20000 + +d_model: 128 +n_layers: 4 +n_heads: 4 +mlp_ratio: 4.0 +time_dim: 64 +token_dim: + +batch_size: 256 +num_workers: 0 +epochs: 50 +lr: 3.0e-4 +weight_decay: 0.01 +grad_clip: 1.0 +eval_every: 10 +eval_n: 10000 +eval_batch_size: 512 +eval_n_steps: 8 + +sigma: 0.1 +use_ot: true +lambda_disc: 1.0 +reference_mode: causal_packets + +device: auto diff --git a/Mixed_CFM/configs/cicids2017_ac_combo_seed42.yaml b/Mixed_CFM/configs/cicids2017_ac_combo_seed42.yaml new file mode 100644 index 0000000..899c8bd --- /dev/null +++ b/Mixed_CFM/configs/cicids2017_ac_combo_seed42.yaml @@ -0,0 +1,40 @@ + +save_dir: /home/chy/JANUS/artifacts/route_comparison/route_ac_combo_cicids2017_seed42 + +packets_npz: /home/chy/JANUS/datasets/cicids2017/processed/packets.npz +flows_parquet: /home/chy/JANUS/datasets/cicids2017/processed/flows.parquet +flow_features_path: /home/chy/JANUS/datasets/cicids2017/processed/flow_features.parquet +flow_features_align: auto + +T: 64 +n_train: 10000 +min_len: 2 +seed: 42 +data_seed: 42 +train_ratio: 0.8 +benign_label: normal + +d_model: 128 +n_layers: 4 +n_heads: 4 +mlp_ratio: 4.0 +time_dim: 64 +token_dim: + +batch_size: 256 +num_workers: 0 +epochs: 50 +lr: 3.0e-4 +weight_decay: 0.01 +grad_clip: 1.0 +eval_every: 10 +eval_n: 20000 +eval_batch_size: 512 +eval_n_steps: 8 + +sigma: 0.1 +use_ot: true +lambda_disc: 1.0 +reference_mode: causal_packets + +device: auto diff --git a/Mixed_CFM/configs/cicids2017_ac_combo_seed43.yaml b/Mixed_CFM/configs/cicids2017_ac_combo_seed43.yaml new file mode 100644 index 0000000..9ef38cb --- /dev/null +++ b/Mixed_CFM/configs/cicids2017_ac_combo_seed43.yaml @@ -0,0 +1,40 @@ + +save_dir: /home/chy/JANUS/artifacts/route_comparison/route_ac_combo_cicids2017_seed43 + +packets_npz: /home/chy/JANUS/datasets/cicids2017/processed/packets.npz +flows_parquet: /home/chy/JANUS/datasets/cicids2017/processed/flows.parquet +flow_features_path: /home/chy/JANUS/datasets/cicids2017/processed/flow_features.parquet +flow_features_align: auto + +T: 64 +n_train: 10000 +min_len: 2 +seed: 43 +data_seed: 43 +train_ratio: 0.8 +benign_label: normal + +d_model: 128 +n_layers: 4 +n_heads: 4 +mlp_ratio: 4.0 +time_dim: 64 +token_dim: + +batch_size: 256 +num_workers: 0 +epochs: 50 +lr: 3.0e-4 +weight_decay: 0.01 +grad_clip: 1.0 +eval_every: 10 +eval_n: 20000 +eval_batch_size: 512 +eval_n_steps: 8 + +sigma: 0.1 +use_ot: true +lambda_disc: 1.0 +reference_mode: causal_packets + +device: auto diff --git a/Mixed_CFM/configs/cicids2017_ac_combo_seed44.yaml b/Mixed_CFM/configs/cicids2017_ac_combo_seed44.yaml new file mode 100644 index 0000000..5f8dba8 --- /dev/null +++ b/Mixed_CFM/configs/cicids2017_ac_combo_seed44.yaml @@ -0,0 +1,40 @@ + +save_dir: /home/chy/JANUS/artifacts/route_comparison/route_ac_combo_cicids2017_seed44 + +packets_npz: /home/chy/JANUS/datasets/cicids2017/processed/packets.npz +flows_parquet: /home/chy/JANUS/datasets/cicids2017/processed/flows.parquet +flow_features_path: /home/chy/JANUS/datasets/cicids2017/processed/flow_features.parquet +flow_features_align: auto + +T: 64 +n_train: 10000 +min_len: 2 +seed: 44 +data_seed: 44 +train_ratio: 0.8 +benign_label: normal + +d_model: 128 +n_layers: 4 +n_heads: 4 +mlp_ratio: 4.0 +time_dim: 64 +token_dim: + +batch_size: 256 +num_workers: 0 +epochs: 50 +lr: 3.0e-4 +weight_decay: 0.01 +grad_clip: 1.0 +eval_every: 10 +eval_n: 20000 +eval_batch_size: 512 +eval_n_steps: 8 + +sigma: 0.1 +use_ot: true +lambda_disc: 1.0 +reference_mode: causal_packets + +device: auto diff --git a/Mixed_CFM/configs/ciciot2023_ac_combo_seed42.yaml b/Mixed_CFM/configs/ciciot2023_ac_combo_seed42.yaml new file mode 100644 index 0000000..4ba011f --- /dev/null +++ b/Mixed_CFM/configs/ciciot2023_ac_combo_seed42.yaml @@ -0,0 +1,44 @@ + +save_dir: /home/chy/JANUS/artifacts/route_comparison/route_ac_combo_ciciot2023_seed42 + +source_store: /home/chy/JANUS/datasets/ciciot2023/processed/full_store +flows_parquet: /home/chy/JANUS/datasets/ciciot2023/processed/full_store/flows.parquet +flow_features_path: /home/chy/JANUS/datasets/ciciot2023/processed/flow_features.parquet +flow_features_align: auto + +T: 64 +n_train: 10000 +min_len: 2 +seed: 42 +data_seed: 42 +train_ratio: 0.8 +benign_label: normal +val_cap: 10000 +attack_cap: 20000 + +d_model: 128 +n_layers: 4 +n_heads: 4 +mlp_ratio: 4.0 +time_dim: 64 +token_dim: + +batch_size: 256 +num_workers: 0 +epochs: 50 +lr: 3.0e-4 +weight_decay: 0.01 +grad_clip: 1.0 +eval_every: 10 +eval_n: 20000 +eval_batch_size: 512 +eval_n_steps: 8 + +sigma: 0.1 +use_ot: true + +lambda_disc: 1.0 + +device: auto + +reference_mode: causal_packets diff --git a/Mixed_CFM/configs/ciciot2023_ac_combo_seed43.yaml b/Mixed_CFM/configs/ciciot2023_ac_combo_seed43.yaml new file mode 100644 index 0000000..ad57179 --- /dev/null +++ b/Mixed_CFM/configs/ciciot2023_ac_combo_seed43.yaml @@ -0,0 +1,44 @@ + +save_dir: /home/chy/JANUS/artifacts/route_comparison/route_ac_combo_ciciot2023_seed43 + +source_store: /home/chy/JANUS/datasets/ciciot2023/processed/full_store +flows_parquet: /home/chy/JANUS/datasets/ciciot2023/processed/full_store/flows.parquet +flow_features_path: /home/chy/JANUS/datasets/ciciot2023/processed/flow_features.parquet +flow_features_align: auto + +T: 64 +n_train: 10000 +min_len: 2 +seed: 43 +data_seed: 43 +train_ratio: 0.8 +benign_label: normal +val_cap: 10000 +attack_cap: 20000 + +d_model: 128 +n_layers: 4 +n_heads: 4 +mlp_ratio: 4.0 +time_dim: 64 +token_dim: + +batch_size: 256 +num_workers: 0 +epochs: 50 +lr: 3.0e-4 +weight_decay: 0.01 +grad_clip: 1.0 +eval_every: 10 +eval_n: 20000 +eval_batch_size: 512 +eval_n_steps: 8 + +sigma: 0.1 +use_ot: true + +lambda_disc: 1.0 + +device: auto + +reference_mode: causal_packets diff --git a/Mixed_CFM/configs/ciciot2023_ac_combo_seed44.yaml b/Mixed_CFM/configs/ciciot2023_ac_combo_seed44.yaml new file mode 100644 index 0000000..648be07 --- /dev/null +++ b/Mixed_CFM/configs/ciciot2023_ac_combo_seed44.yaml @@ -0,0 +1,44 @@ + +save_dir: /home/chy/JANUS/artifacts/route_comparison/route_ac_combo_ciciot2023_seed44 + +source_store: /home/chy/JANUS/datasets/ciciot2023/processed/full_store +flows_parquet: /home/chy/JANUS/datasets/ciciot2023/processed/full_store/flows.parquet +flow_features_path: /home/chy/JANUS/datasets/ciciot2023/processed/flow_features.parquet +flow_features_align: auto + +T: 64 +n_train: 10000 +min_len: 2 +seed: 44 +data_seed: 44 +train_ratio: 0.8 +benign_label: normal +val_cap: 10000 +attack_cap: 20000 + +d_model: 128 +n_layers: 4 +n_heads: 4 +mlp_ratio: 4.0 +time_dim: 64 +token_dim: + +batch_size: 256 +num_workers: 0 +epochs: 50 +lr: 3.0e-4 +weight_decay: 0.01 +grad_clip: 1.0 +eval_every: 10 +eval_n: 20000 +eval_batch_size: 512 +eval_n_steps: 8 + +sigma: 0.1 +use_ot: true + +lambda_disc: 1.0 + +device: auto + +reference_mode: causal_packets diff --git a/Mixed_CFM/configs/ciciot2023_seed42.yaml b/Mixed_CFM/configs/ciciot2023_seed42.yaml new file mode 100644 index 0000000..d5d9976 --- /dev/null +++ b/Mixed_CFM/configs/ciciot2023_seed42.yaml @@ -0,0 +1,42 @@ + +save_dir: /home/chy/JANUS/artifacts/route_comparison/route_c_mixed_ciciot2023_seed42 + +source_store: /home/chy/JANUS/datasets/ciciot2023/processed/full_store +flows_parquet: /home/chy/JANUS/datasets/ciciot2023/processed/full_store/flows.parquet +flow_features_path: /home/chy/JANUS/datasets/ciciot2023/processed/flow_features.parquet +flow_features_align: auto + +T: 64 +n_train: 10000 +min_len: 2 +seed: 42 +data_seed: 42 +train_ratio: 0.8 +benign_label: normal +val_cap: 10000 +attack_cap: 20000 + +d_model: 128 +n_layers: 4 +n_heads: 4 +mlp_ratio: 4.0 +time_dim: 64 +token_dim: + +batch_size: 256 +num_workers: 0 +epochs: 50 +lr: 3.0e-4 +weight_decay: 0.01 +grad_clip: 1.0 +eval_every: 10 +eval_n: 20000 +eval_batch_size: 512 +eval_n_steps: 8 + +sigma: 0.1 +use_ot: true + +lambda_disc: 1.0 + +device: auto diff --git a/Mixed_CFM/configs/ciciot2023_seed43.yaml b/Mixed_CFM/configs/ciciot2023_seed43.yaml new file mode 100644 index 0000000..72cac8b --- /dev/null +++ b/Mixed_CFM/configs/ciciot2023_seed43.yaml @@ -0,0 +1,42 @@ + +save_dir: /home/chy/JANUS/artifacts/route_comparison/route_c_mixed_ciciot2023_seed43 + +source_store: /home/chy/JANUS/datasets/ciciot2023/processed/full_store +flows_parquet: /home/chy/JANUS/datasets/ciciot2023/processed/full_store/flows.parquet +flow_features_path: /home/chy/JANUS/datasets/ciciot2023/processed/flow_features.parquet +flow_features_align: auto + +T: 64 +n_train: 10000 +min_len: 2 +seed: 43 +data_seed: 43 +train_ratio: 0.8 +benign_label: normal +val_cap: 10000 +attack_cap: 20000 + +d_model: 128 +n_layers: 4 +n_heads: 4 +mlp_ratio: 4.0 +time_dim: 64 +token_dim: + +batch_size: 256 +num_workers: 0 +epochs: 50 +lr: 3.0e-4 +weight_decay: 0.01 +grad_clip: 1.0 +eval_every: 10 +eval_n: 20000 +eval_batch_size: 512 +eval_n_steps: 8 + +sigma: 0.1 +use_ot: true + +lambda_disc: 1.0 + +device: auto diff --git a/Mixed_CFM/configs/ciciot2023_seed44.yaml b/Mixed_CFM/configs/ciciot2023_seed44.yaml new file mode 100644 index 0000000..db93328 --- /dev/null +++ b/Mixed_CFM/configs/ciciot2023_seed44.yaml @@ -0,0 +1,42 @@ + +save_dir: /home/chy/JANUS/artifacts/route_comparison/route_c_mixed_ciciot2023_seed44 + +source_store: /home/chy/JANUS/datasets/ciciot2023/processed/full_store +flows_parquet: /home/chy/JANUS/datasets/ciciot2023/processed/full_store/flows.parquet +flow_features_path: /home/chy/JANUS/datasets/ciciot2023/processed/flow_features.parquet +flow_features_align: auto + +T: 64 +n_train: 10000 +min_len: 2 +seed: 44 +data_seed: 44 +train_ratio: 0.8 +benign_label: normal +val_cap: 10000 +attack_cap: 20000 + +d_model: 128 +n_layers: 4 +n_heads: 4 +mlp_ratio: 4.0 +time_dim: 64 +token_dim: + +batch_size: 256 +num_workers: 0 +epochs: 50 +lr: 3.0e-4 +weight_decay: 0.01 +grad_clip: 1.0 +eval_every: 10 +eval_n: 20000 +eval_batch_size: 512 +eval_n_steps: 8 + +sigma: 0.1 +use_ot: true + +lambda_disc: 1.0 + +device: auto diff --git a/Mixed_CFM/configs/iscxtor2016_ac_combo_seed42.yaml b/Mixed_CFM/configs/iscxtor2016_ac_combo_seed42.yaml new file mode 100644 index 0000000..542378b --- /dev/null +++ b/Mixed_CFM/configs/iscxtor2016_ac_combo_seed42.yaml @@ -0,0 +1,40 @@ + +save_dir: /home/chy/JANUS/artifacts/route_comparison/route_ac_combo_iscxtor2016_seed42 + +packets_npz: /home/chy/JANUS/datasets/iscxtor2016/processed/packets.npz +flows_parquet: /home/chy/JANUS/datasets/iscxtor2016/processed/flows.parquet +flow_features_path: /home/chy/JANUS/datasets/iscxtor2016/processed/flow_features.parquet +flow_features_align: auto + +T: 64 +n_train: 10000 +min_len: 2 +seed: 42 +data_seed: 42 +train_ratio: 0.8 +benign_label: nontor + +d_model: 128 +n_layers: 4 +n_heads: 4 +mlp_ratio: 4.0 +time_dim: 64 +token_dim: + +batch_size: 256 +num_workers: 0 +epochs: 50 +lr: 3.0e-4 +weight_decay: 0.01 +grad_clip: 1.0 +eval_every: 10 +eval_n: 20000 +eval_batch_size: 512 +eval_n_steps: 8 + +sigma: 0.1 +use_ot: true +lambda_disc: 1.0 +reference_mode: causal_packets + +device: auto diff --git a/Mixed_CFM/configs/iscxtor2016_ac_combo_seed43.yaml b/Mixed_CFM/configs/iscxtor2016_ac_combo_seed43.yaml new file mode 100644 index 0000000..c243684 --- /dev/null +++ b/Mixed_CFM/configs/iscxtor2016_ac_combo_seed43.yaml @@ -0,0 +1,40 @@ + +save_dir: /home/chy/JANUS/artifacts/route_comparison/route_ac_combo_iscxtor2016_seed43 + +packets_npz: /home/chy/JANUS/datasets/iscxtor2016/processed/packets.npz +flows_parquet: /home/chy/JANUS/datasets/iscxtor2016/processed/flows.parquet +flow_features_path: /home/chy/JANUS/datasets/iscxtor2016/processed/flow_features.parquet +flow_features_align: auto + +T: 64 +n_train: 10000 +min_len: 2 +seed: 43 +data_seed: 43 +train_ratio: 0.8 +benign_label: nontor + +d_model: 128 +n_layers: 4 +n_heads: 4 +mlp_ratio: 4.0 +time_dim: 64 +token_dim: + +batch_size: 256 +num_workers: 0 +epochs: 50 +lr: 3.0e-4 +weight_decay: 0.01 +grad_clip: 1.0 +eval_every: 10 +eval_n: 20000 +eval_batch_size: 512 +eval_n_steps: 8 + +sigma: 0.1 +use_ot: true +lambda_disc: 1.0 +reference_mode: causal_packets + +device: auto diff --git a/Mixed_CFM/configs/iscxtor2016_ac_combo_seed44.yaml b/Mixed_CFM/configs/iscxtor2016_ac_combo_seed44.yaml new file mode 100644 index 0000000..6b83be8 --- /dev/null +++ b/Mixed_CFM/configs/iscxtor2016_ac_combo_seed44.yaml @@ -0,0 +1,40 @@ + +save_dir: /home/chy/JANUS/artifacts/route_comparison/route_ac_combo_iscxtor2016_seed44 + +packets_npz: /home/chy/JANUS/datasets/iscxtor2016/processed/packets.npz +flows_parquet: /home/chy/JANUS/datasets/iscxtor2016/processed/flows.parquet +flow_features_path: /home/chy/JANUS/datasets/iscxtor2016/processed/flow_features.parquet +flow_features_align: auto + +T: 64 +n_train: 10000 +min_len: 2 +seed: 44 +data_seed: 44 +train_ratio: 0.8 +benign_label: nontor + +d_model: 128 +n_layers: 4 +n_heads: 4 +mlp_ratio: 4.0 +time_dim: 64 +token_dim: + +batch_size: 256 +num_workers: 0 +epochs: 50 +lr: 3.0e-4 +weight_decay: 0.01 +grad_clip: 1.0 +eval_every: 10 +eval_n: 20000 +eval_batch_size: 512 +eval_n_steps: 8 + +sigma: 0.1 +use_ot: true +lambda_disc: 1.0 +reference_mode: causal_packets + +device: auto diff --git a/Mixed_CFM/data.py b/Mixed_CFM/data.py new file mode 100644 index 0000000..5013b92 --- /dev/null +++ b/Mixed_CFM/data.py @@ -0,0 +1,155 @@ +from __future__ import annotations +from dataclasses import dataclass +from pathlib import Path +from typing import Optional +import numpy as np +import pandas as pd +import sys as _sys +from pathlib import Path as _Path +_sys.path.insert(0, str(_Path(__file__).resolve().parents[1])) +from common.data_contract import PACKET_FEATURE_NAMES, PACKET_CONTINUOUS_CHANNEL_IDX, PACKET_BINARY_CHANNEL_IDX, fit_packet_stats as _fit_packet_stats, zscore as _zscore +import importlib.util as _ilu +_UDATA_NAME = 'unified_cfm_data' +if _UDATA_NAME not in _sys.modules: + _udata_spec = _ilu.spec_from_file_location(_UDATA_NAME, _Path(__file__).resolve().parents[1] / 'Unified_CFM' / 'data.py') + _udata = _ilu.module_from_spec(_udata_spec) + _sys.modules[_UDATA_NAME] = _udata + _udata_spec.loader.exec_module(_udata) +else: + _udata = _sys.modules[_UDATA_NAME] +DEFAULT_FLOW_META_COLUMNS = _udata.DEFAULT_FLOW_META_COLUMNS +_read_aligned_flow_features = _udata._read_aligned_flow_features +_preprocess_flow = _udata._preprocess_flow + +@dataclass +class MixedData: + train_cont: np.ndarray + val_cont: np.ndarray + attack_cont: np.ndarray + train_disc: np.ndarray + val_disc: np.ndarray + attack_disc: np.ndarray + train_flow: np.ndarray + val_flow: np.ndarray + attack_flow: np.ndarray + train_len: np.ndarray + val_len: np.ndarray + attack_len: np.ndarray + attack_labels: np.ndarray + cont_mean: np.ndarray + cont_std: np.ndarray + flow_mean: np.ndarray + flow_std: np.ndarray + flow_feature_names: tuple[str, ...] + packet_feature_names: tuple[str, ...] = PACKET_FEATURE_NAMES + + @property + def T(self) -> int: + return int(self.train_cont.shape[1]) + + @property + def n_cont(self) -> int: + return int(self.train_cont.shape[2]) + + @property + def n_disc(self) -> int: + return int(self.train_disc.shape[2]) + + @property + def flow_dim(self) -> int: + return int(self.train_flow.shape[1]) + +def _zscore_cont(train_x: np.ndarray, val_x: np.ndarray, attack_x: np.ndarray, train_l: np.ndarray, val_l: np.ndarray, attack_l: np.ndarray) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray]: + (mean, std) = _fit_packet_stats(train_x, train_l) + + def prep(x: np.ndarray, l: np.ndarray) -> np.ndarray: + z = _zscore(x, mean, std) + T = x.shape[1] + m = np.arange(T)[None, :] < l[:, None] + return (z * m[:, :, None]).astype(np.float32) + return (prep(train_x, train_l), prep(val_x, val_l), prep(attack_x, attack_l), mean, std) + +def load_mixed_data(*, packets_npz: Path | None=None, source_store: Path | None=None, flows_parquet: Path, flow_features_path: Path, flow_feature_columns: Optional[list[str]]=None, flow_features_align: str='auto', T: int=64, split_seed: int=42, train_ratio: float=0.8, benign_label: str='normal', min_len: int=2, attack_cap: int | None=None, val_cap: int | None=None) -> MixedData: + if (packets_npz is None) == (source_store is None): + raise ValueError('pass exactly one of packets_npz or source_store') + flows_parquet = Path(flows_parquet) + print(f'[data] flows={flows_parquet} packets={(packets_npz if packets_npz else source_store)}') + flow_cols = ['flow_id', 'label', 'src_ip', 'src_port', 'dst_ip', 'dst_port', 'protocol'] + flows = pd.read_parquet(flows_parquet, columns=flow_cols) + labels_full = flows['label'].to_numpy().astype(str) + flow_id = flows['flow_id'].to_numpy() + tokens_full: np.ndarray | None = None + store = None + if packets_npz is not None: + pz = np.load(Path(packets_npz)) + tokens_full = pz['packet_tokens'].astype(np.float32) + lens_full = pz['packet_lengths'].astype(np.int32) + if T > tokens_full.shape[1]: + raise ValueError(f'requested T={T} > stored {tokens_full.shape[1]}') + tokens_full = tokens_full[:, :T].copy() + lens_full = np.minimum(lens_full, T).astype(np.int32) + if 'flow_id' in pz.files and (not np.array_equal(pz['flow_id'], flow_id)): + raise ValueError('packets_npz / flows_parquet not row-aligned') + else: + from common.packet_store import PacketShardStore + store = PacketShardStore.open(Path(source_store)) + store_id = store.read_flows(columns=['flow_id'])['flow_id'].to_numpy() + if not np.array_equal(store_id, flow_id): + raise ValueError('source_store / flows_parquet not row-aligned') + lens_full = np.minimum(store.manifest['packet_length'].to_numpy(dtype=np.int32), T) + (flow_features, flow_names) = _read_aligned_flow_features(Path(flow_features_path), flows, feature_columns=flow_feature_columns, align=flow_features_align) + keep = lens_full >= min_len + labels = labels_full[keep] + flow_features = flow_features[keep] + lens = lens_full[keep] + global_idx = np.flatnonzero(keep).astype(np.int64) + materialized = tokens_full[keep] if tokens_full is not None else None + print(f'[data] kept {keep.sum():,} of {len(keep):,} (min_len={min_len})') + benign = np.where(labels == benign_label)[0] + attack = np.where(labels != benign_label)[0] + rng = np.random.default_rng(split_seed) + rng.shuffle(benign) + n_train = int(len(benign) * train_ratio) + train_local = benign[:n_train] + val_local = benign[n_train:] + if val_cap is not None and len(val_local) > val_cap: + val_local = np.sort(rng.choice(val_local, size=val_cap, replace=False)) + if attack_cap is not None and len(attack) > attack_cap: + attack = np.sort(rng.choice(attack, size=attack_cap, replace=False)) + print(f'[data] train={len(train_local):,} val={len(val_local):,} attack={len(attack):,}') + + def _materialize(idx_local: np.ndarray) -> np.ndarray: + if materialized is not None: + return materialized[idx_local].astype(np.float32, copy=False) + assert store is not None + g = global_idx[idx_local] + (tok, _) = store.read_packets(g.astype(np.int64), T=T) + return tok.astype(np.float32, copy=False) + tr_p = _materialize(train_local) + va_p = _materialize(val_local) + at_p = _materialize(attack) + tr_l = lens[train_local] + va_l = lens[val_local] + at_l = lens[attack] + tr_f = flow_features[train_local] + va_f = flow_features[val_local] + at_f = flow_features[attack] + cont_idx = list(PACKET_CONTINUOUS_CHANNEL_IDX) + disc_idx = list(PACKET_BINARY_CHANNEL_IDX) + tr_cont = tr_p[..., cont_idx] + va_cont = va_p[..., cont_idx] + at_cont = at_p[..., cont_idx] + tr_disc = tr_p[..., disc_idx].astype(np.int8) + va_disc = va_p[..., disc_idx].astype(np.int8) + at_disc = at_p[..., disc_idx].astype(np.int8) + (tr_cont, va_cont, at_cont, c_mean, c_std) = _zscore_cont(tr_cont, va_cont, at_cont, tr_l, va_l, at_l) + (tr_flow, va_flow, at_flow, f_mean, f_std) = _preprocess_flow(tr_f, va_f, at_f) + return MixedData(train_cont=tr_cont, val_cont=va_cont, attack_cont=at_cont, train_disc=tr_disc, val_disc=va_disc, attack_disc=at_disc, train_flow=tr_flow, val_flow=va_flow, attack_flow=at_flow, train_len=tr_l, val_len=va_l, attack_len=at_l, attack_labels=labels[attack], cont_mean=c_mean, cont_std=c_std, flow_mean=f_mean, flow_std=f_std, flow_feature_names=tuple(flow_names)) + +def subsample_train(data: MixedData, n: int, seed: int) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]: + if n <= 0 or n >= len(data.train_cont): + return (data.train_flow, data.train_cont, data.train_disc, data.train_len) + rng = np.random.default_rng(seed) + idx = rng.choice(len(data.train_cont), n, replace=False) + idx.sort() + return (data.train_flow[idx], data.train_cont[idx], data.train_disc[idx], data.train_len[idx]) diff --git a/Mixed_CFM/eval_cross.py b/Mixed_CFM/eval_cross.py new file mode 100644 index 0000000..ad86ac1 --- /dev/null +++ b/Mixed_CFM/eval_cross.py @@ -0,0 +1,180 @@ +from __future__ import annotations +import argparse +import json +import sys as _sys +import time +from pathlib import Path +import numpy as np +import pandas as pd +import torch +from sklearn.metrics import average_precision_score, roc_auc_score +REPO = Path(__file__).resolve().parents[1] +_sys.path.insert(0, str(REPO)) +from common.data_contract import PACKET_CONTINUOUS_CHANNEL_IDX, PACKET_BINARY_CHANNEL_IDX, zscore as _zscore +from common.packet_store import PacketShardStore +_sys.path.insert(0, str(Path(__file__).resolve().parent)) +from model import MixedCFMConfig, MixedTokenCFM + +def _device(arg: str) -> torch.device: + if arg == 'auto': + return torch.device('cuda' if torch.cuda.is_available() else 'cpu') + return torch.device(arg) + +def _score_batch(model, flow_z, cont_z, disc_int, lens, device, batch_size=256, n_steps=16): + out: dict[str, list[np.ndarray]] = {} + for start in range(0, len(flow_z), batch_size): + sl = slice(start, start + batch_size) + f = torch.from_numpy(flow_z[sl]).float().to(device) + c = torch.from_numpy(cont_z[sl]).float().to(device) + d = torch.from_numpy(disc_int[sl]).long().to(device) + l = torch.from_numpy(lens[sl]).long().to(device) + with torch.no_grad(): + traj = model.trajectory_metrics(f, c, d, l, n_steps=n_steps) + nll = model.disc_nll_score(f, c, d, l) + for src in (traj, nll): + for (k, v) in src.items(): + out.setdefault(k, []).append(v.detach().cpu().numpy()) + if start // batch_size % 20 == 0: + print(f'[score] {min(start + batch_size, len(flow_z)):,}/{len(flow_z):,}', flush=True) + return {k: np.concatenate(v) for (k, v) in out.items()} + +def main() -> None: + p = argparse.ArgumentParser() + p.add_argument('--model-dir', type=Path, required=True) + p.add_argument('--target-store', type=Path, default=None, help='Sharded packet store (mutually exclusive with --target-packets-npz).') + p.add_argument('--target-packets-npz', type=Path, default=None, help='Monolithic packets.npz (for datasets without full_store).') + p.add_argument('--target-flows', type=Path, required=True) + p.add_argument('--target-flow-features', type=Path, required=True) + p.add_argument('--out', type=Path, required=True) + p.add_argument('--n-benign', type=int, default=10000) + p.add_argument('--n-attack', type=int, default=10000) + p.add_argument('--benign-label', type=str, default='normal', help="Label string of benign class in target dataset (e.g. 'nontor' for ISCXTor2016).") + p.add_argument('--seed', type=int, default=42) + p.add_argument('--T', type=int, default=64) + p.add_argument('--batch-size', type=int, default=256) + p.add_argument('--n-steps', type=int, default=16) + p.add_argument('--device', type=str, default='auto') + args = p.parse_args() + if (args.target_store is None) == (args.target_packets_npz is None): + p.error('pass exactly one of --target-store or --target-packets-npz') + device = _device(args.device) + ckpt = torch.load(args.model_dir / 'model.pt', map_location='cpu', weights_only=False) + model_cfg = MixedCFMConfig(**ckpt['model_cfg']) + model = MixedTokenCFM(model_cfg).to(device) + model.load_state_dict(ckpt['model_state_dict']) + model.eval() + cont_mean = np.asarray(ckpt['cont_mean'], dtype=np.float32) + cont_std = np.asarray(ckpt['cont_std'], dtype=np.float32) + flow_mean = np.asarray(ckpt['flow_mean'], dtype=np.float32) + flow_std = np.asarray(ckpt['flow_std'], dtype=np.float32) + flow_names = [str(n) for n in ckpt['flow_feature_names']] + print(f'[model] T={args.T} flow_dim={model_cfg.flow_dim}') + flows = pd.read_parquet(args.target_flows, columns=['flow_id', 'label']) + ff = pd.read_parquet(args.target_flow_features) + if not np.array_equal(flows['flow_id'].to_numpy(dtype=np.uint64), ff['flow_id'].to_numpy(dtype=np.uint64)): + raise ValueError('flows and flow_features not row-aligned') + labels = flows['label'].astype(str).to_numpy() + print(f'[data] {len(flows):,} target rows') + rng = np.random.default_rng(args.seed) + benign_idx = np.flatnonzero(labels == args.benign_label) + attack_idx = np.flatnonzero(labels != args.benign_label) + n_benign = min(args.n_benign, len(benign_idx)) + if n_benign < args.n_benign: + print(f'[warn] only {len(benign_idx)} benign rows available (asked {args.n_benign})') + b_sel = np.sort(rng.choice(benign_idx, size=n_benign, replace=False)) + atk_classes = sorted(set(labels[attack_idx])) + per_class = max(1, args.n_attack // len(atk_classes)) + a_sel_chunks = [] + for cls in atk_classes: + pool = attack_idx[labels[attack_idx] == cls] + k = min(per_class, len(pool)) + if k: + a_sel_chunks.append(rng.choice(pool, size=k, replace=False)) + a_sel = np.sort(np.concatenate(a_sel_chunks)) + if len(a_sel) > args.n_attack: + a_sel = np.sort(rng.choice(a_sel, size=args.n_attack, replace=False)) + print(f'[sample] benign={len(b_sel):,} attack={len(a_sel):,} ({len(atk_classes)} classes)') + cont_idx = list(PACKET_CONTINUOUS_CHANNEL_IDX) + disc_idx = list(PACKET_BINARY_CHANNEL_IDX) + if args.target_store is not None: + store = PacketShardStore.open(args.target_store) + npz_tokens = None + npz_lens = None + else: + store = None + pz = np.load(args.target_packets_npz) + npz_tokens = pz['packet_tokens'][:, :args.T].astype(np.float32) + npz_lens = np.minimum(pz['packet_lengths'], args.T).astype(np.int32) + + def _materialize(idx: np.ndarray): + if store is not None: + (tok, l) = store.read_packets(idx, T=args.T) + else: + tok = npz_tokens[idx] + l = npz_lens[idx] + l = np.minimum(l, args.T).astype(np.int32) + f = ff.iloc[idx][flow_names].to_numpy(dtype=np.float64) + f = np.nan_to_num(f, nan=0.0, posinf=0.0, neginf=0.0).astype(np.float32) + return (tok.astype(np.float32), l, f) + print('[read] benign...') + (b_tok, b_len, b_flow) = _materialize(b_sel) + print('[read] attack...') + (a_tok, a_len, a_flow) = _materialize(a_sel) + if cont_mean.shape == (9,): + cm = cont_mean[cont_idx] + cs = cont_std[cont_idx] + else: + cm = cont_mean + cs = cont_std + + def _prep(tok, lens): + cont = tok[..., cont_idx] + disc = tok[..., disc_idx].astype(np.int8) + z = _zscore(cont, cm, cs) + m = np.arange(args.T)[None, :] < lens[:, None] + cont_z = (z * m[:, :, None]).astype(np.float32) + return (cont_z, disc) + (b_cont, b_disc) = _prep(b_tok, b_len) + (a_cont, a_disc) = _prep(a_tok, a_len) + b_flow_z = ((b_flow - flow_mean) / np.maximum(flow_std, 1e-06)).astype(np.float32) + a_flow_z = ((a_flow - flow_mean) / np.maximum(flow_std, 1e-06)).astype(np.float32) + t0 = time.time() + print('[eval] benign...') + b_scores = _score_batch(model, b_flow_z, b_cont, b_disc, b_len, device, batch_size=args.batch_size, n_steps=args.n_steps) + print(f'[eval] benign done {time.time() - t0:.1f}s') + t0 = time.time() + print('[eval] attack...') + a_scores = _score_batch(model, a_flow_z, a_cont, a_disc, a_len, device, batch_size=args.batch_size, n_steps=args.n_steps) + print(f'[eval] attack done {time.time() - t0:.1f}s') + keys = sorted(set(b_scores) & set(a_scores)) + overall = {} + for k in keys: + y = np.r_[np.zeros(len(b_scores[k])), np.ones(len(a_scores[k]))] + s = np.r_[b_scores[k], a_scores[k]] + s = np.nan_to_num(s, nan=0.0, posinf=1000000000000.0, neginf=-1000000000000.0) + overall[k] = {'auroc': float(roc_auc_score(y, s)), 'auprc': float(average_precision_score(y, s))} + a_labels = labels[a_sel] + per_cls = {} + for cls in sorted(set(a_labels)): + m = a_labels == cls + per_cls[cls] = {'_n': float(m.sum())} + for k in keys: + y = np.r_[np.zeros(len(b_scores[k])), np.ones(int(m.sum()))] + s = np.r_[b_scores[k], a_scores[k][m]] + s = np.nan_to_num(s, nan=0.0, posinf=1000000000000.0, neginf=-1000000000000.0) + try: + per_cls[cls][k] = float(roc_auc_score(y, s)) + except ValueError: + per_cls[cls][k] = float('nan') + out = {'model_dir': str(args.model_dir), 'target_store': str(args.target_store), 'n_benign': len(b_sel), 'n_attack': len(a_sel), 'n_score_keys': len(keys), 'overall': overall, 'per_class': per_cls} + args.out.parent.mkdir(parents=True, exist_ok=True) + args.out.write_text(json.dumps(out, indent=2)) + npz = args.out.with_suffix('.npz') + save = {'a_labels': a_labels.astype(str)} + for k in keys: + save[f'b_{k}'] = b_scores[k].astype(np.float32) + save[f'a_{k}'] = a_scores[k].astype(np.float32) + np.savez(npz, **save) + print(f'[saved] {args.out}') +if __name__ == '__main__': + main() diff --git a/Mixed_CFM/eval_phase1.py b/Mixed_CFM/eval_phase1.py new file mode 100644 index 0000000..6dc07d4 --- /dev/null +++ b/Mixed_CFM/eval_phase1.py @@ -0,0 +1,109 @@ +from __future__ import annotations +import argparse +import json +import sys as _sys +import time +from pathlib import Path +from pathlib import Path as _Path +import numpy as np +import torch +import yaml +from sklearn.metrics import average_precision_score, roc_auc_score +_sys.path.insert(0, str(_Path(__file__).resolve().parent)) +from data import load_mixed_data +from model import MixedCFMConfig, MixedTokenCFM + +def _device(arg: str) -> torch.device: + if arg == 'auto': + return torch.device('cuda' if torch.cuda.is_available() else 'cpu') + return torch.device(arg) + +def _score_batch(model: MixedTokenCFM, flow_np: np.ndarray, cont_np: np.ndarray, disc_np: np.ndarray, len_np: np.ndarray, device: torch.device, *, batch_size: int, n_steps: int) -> dict[str, np.ndarray]: + out: dict[str, list[np.ndarray]] = {} + for start in range(0, len(flow_np), batch_size): + sl = slice(start, start + batch_size) + flow = torch.from_numpy(flow_np[sl]).float().to(device) + cont = torch.from_numpy(cont_np[sl]).float().to(device) + disc = torch.from_numpy(disc_np[sl]).long().to(device) + lens = torch.from_numpy(len_np[sl]).long().to(device) + with torch.no_grad(): + traj = model.trajectory_metrics(flow, cont, disc, lens, n_steps=n_steps) + nll = model.disc_nll_score(flow, cont, disc, lens) + for d in (traj, nll): + for (k, v) in d.items(): + out.setdefault(k, []).append(v.detach().cpu().numpy()) + print(f'[score] {min(start + batch_size, len(flow_np)):,}/{len(flow_np):,}', flush=True) + return {k: np.concatenate(v, axis=0) for (k, v) in out.items()} + +def _auroc_safe(y, s) -> float: + try: + return float(roc_auc_score(y, s)) + except ValueError: + return float('nan') + +def _auprc_safe(y, s) -> float: + try: + return float(average_precision_score(y, s)) + except ValueError: + return float('nan') + +def main() -> None: + p = argparse.ArgumentParser() + p.add_argument('--model-dir', type=Path, required=True) + p.add_argument('--out-dir', type=Path, required=True) + p.add_argument('--n-val-cap', type=int, default=None) + p.add_argument('--n-atk-cap', type=int, default=None) + p.add_argument('--batch-size', type=int, default=256) + p.add_argument('--n-steps', type=int, default=16) + p.add_argument('--device', type=str, default='auto') + args = p.parse_args() + device = _device(args.device) + args.out_dir.mkdir(parents=True, exist_ok=True) + cfg = yaml.safe_load((args.model_dir / 'config.yaml').read_text()) + ckpt = torch.load(args.model_dir / 'model.pt', map_location='cpu', weights_only=False) + model_cfg = MixedCFMConfig(**ckpt['model_cfg']) + model = MixedTokenCFM(model_cfg).to(device) + model.load_state_dict(ckpt['model_state_dict']) + model.eval() + print(f'[model] T={model_cfg.T} flow_dim={model_cfg.flow_dim}') + data = load_mixed_data(packets_npz=Path(cfg['packets_npz']) if cfg.get('packets_npz') else None, source_store=Path(cfg['source_store']) if cfg.get('source_store') else None, flows_parquet=Path(cfg['flows_parquet']), flow_features_path=Path(cfg['flow_features_path']), flow_features_align=str(cfg.get('flow_features_align', 'auto')), T=int(cfg['T']), split_seed=int(cfg.get('data_seed', cfg.get('seed', 42))), train_ratio=float(cfg.get('train_ratio', 0.8)), benign_label=str(cfg.get('benign_label', 'normal')), min_len=int(cfg.get('min_len', 2)), attack_cap=int(cfg['attack_cap']) if cfg.get('attack_cap') else None, val_cap=int(cfg['val_cap']) if cfg.get('val_cap') else None) + print(f'[data] val={len(data.val_flow):,} attack={len(data.attack_flow):,}') + rng = np.random.default_rng(0) + (val_flow, val_cont, val_disc, val_len) = (data.val_flow, data.val_cont, data.val_disc, data.val_len) + (atk_flow, atk_cont, atk_disc, atk_len) = (data.attack_flow, data.attack_cont, data.attack_disc, data.attack_len) + atk_labels = data.attack_labels + if args.n_val_cap is not None and len(val_flow) > args.n_val_cap: + idx = np.sort(rng.choice(len(val_flow), size=args.n_val_cap, replace=False)) + (val_flow, val_cont, val_disc, val_len) = (val_flow[idx], val_cont[idx], val_disc[idx], val_len[idx]) + if args.n_atk_cap is not None and len(atk_flow) > args.n_atk_cap: + idx = np.sort(rng.choice(len(atk_flow), size=args.n_atk_cap, replace=False)) + (atk_flow, atk_cont, atk_disc, atk_len) = (atk_flow[idx], atk_cont[idx], atk_disc[idx], atk_len[idx]) + atk_labels = atk_labels[idx] + print(f'[eval] scoring val={len(val_flow):,} atk={len(atk_flow):,}') + t0 = time.time() + val = _score_batch(model, val_flow, val_cont, val_disc, val_len, device, batch_size=args.batch_size, n_steps=args.n_steps) + print(f'[eval] val done {time.time() - t0:.1f}s') + t0 = time.time() + atk = _score_batch(model, atk_flow, atk_cont, atk_disc, atk_len, device, batch_size=args.batch_size, n_steps=args.n_steps) + print(f'[eval] atk done {time.time() - t0:.1f}s') + keys = sorted(set(val) & set(atk)) + overall: dict[str, dict[str, float]] = {} + for k in keys: + y = np.r_[np.zeros(len(val[k])), np.ones(len(atk[k]))] + s = np.r_[val[k], atk[k]] + s = np.nan_to_num(s, nan=0.0, posinf=1000000000000.0, neginf=-1000000000000.0) + overall[k] = {'auroc': _auroc_safe(y, s), 'auprc': _auprc_safe(y, s)} + per_class: dict[str, dict[str, float]] = {} + for c in sorted(set(atk_labels.tolist())): + m = atk_labels == c + per_class[c] = {'_n': float(m.sum())} + for k in keys: + y = np.r_[np.zeros(len(val[k])), np.ones(int(m.sum()))] + s = np.r_[val[k], atk[k][m]] + s = np.nan_to_num(s, nan=0.0, posinf=1000000000000.0, neginf=-1000000000000.0) + per_class[c][k] = _auroc_safe(y, s) + np.savez(args.out_dir / 'phase1_scores.npz', val_labels=np.array(['normal'] * len(val_flow)), atk_labels=atk_labels.astype(str), **{f'val_{k}': val[k] for k in keys}, **{f'atk_{k}': atk[k] for k in keys}) + json.dump({'overall': overall, 'per_class': per_class}, open(args.out_dir / 'phase1_summary.json', 'w'), indent=2) + print(f'[wrote] {args.out_dir}/phase1_summary.json keys={len(keys)}') +if __name__ == '__main__': + main() diff --git a/Mixed_CFM/model.py b/Mixed_CFM/model.py new file mode 100644 index 0000000..8898562 --- /dev/null +++ b/Mixed_CFM/model.py @@ -0,0 +1,244 @@ +from __future__ import annotations +import math +from dataclasses import dataclass, field +import torch +import torch.nn as nn +import torch.nn.functional as F +import importlib.util as _ilu +import sys as _sys +from pathlib import Path as _Path +_UNIFIED_NAME = 'unified_cfm_model' +if _UNIFIED_NAME not in _sys.modules: + _unified_spec = _ilu.spec_from_file_location(_UNIFIED_NAME, _Path(__file__).resolve().parents[1] / 'Unified_CFM' / 'model.py') + _unified = _ilu.module_from_spec(_unified_spec) + _sys.modules[_UNIFIED_NAME] = _unified + _unified_spec.loader.exec_module(_unified) +else: + _unified = _sys.modules[_UNIFIED_NAME] +AdaLNBlock = _unified.AdaLNBlock +SinusoidalTimeEmb = _unified.SinusoidalTimeEmb +_sinkhorn_coupling = _unified._sinkhorn_coupling + +@dataclass +class MixedCFMConfig: + T: int = 64 + flow_dim: int = 20 + n_cont_pkt: int = 3 + n_disc_pkt: int = 6 + cont_pkt_idx: tuple[int, ...] = (0, 1, 8) + disc_pkt_idx: tuple[int, ...] = (2, 3, 4, 5, 6, 7) + n_disc_classes: int = 2 + token_dim: int | None = None + d_model: int = 128 + n_layers: int = 4 + n_heads: int = 4 + mlp_ratio: float = 4.0 + time_dim: int = 64 + sigma: float = 0.1 + use_ot: bool = False + reference_mode: str | None = None + lambda_disc: float = 1.0 + disc_path: str = 'uniform' + disc_embed_scale: float = 1.0 + + def __post_init__(self) -> None: + if len(self.cont_pkt_idx) != self.n_cont_pkt: + raise ValueError('cont_pkt_idx length mismatch n_cont_pkt') + if len(self.disc_pkt_idx) != self.n_disc_pkt: + raise ValueError('disc_pkt_idx length mismatch n_disc_pkt') + if self.disc_path != 'uniform': + raise NotImplementedError(f'disc_path={self.disc_path}') + +class MixedVelocity(nn.Module): + + def __init__(self, token_dim: int, seq_len: int, n_disc: int, n_classes: int, d_model: int=128, n_layers: int=4, n_heads: int=4, mlp_ratio: float=4.0, time_dim: int=64, reference_mode: str | None=None) -> None: + super().__init__() + if reference_mode not in (None, 'causal_packets', 'causal_all'): + raise ValueError(f'reference_mode={reference_mode!r}') + self.token_dim = token_dim + self.seq_len = seq_len + self.n_disc = n_disc + self.n_classes = n_classes + self.reference_mode = reference_mode + self.input_proj = nn.Linear(token_dim, d_model) + self.pos_emb = nn.Parameter(torch.zeros(1, seq_len, d_model)) + self.type_emb = nn.Embedding(2, d_model) + nn.init.trunc_normal_(self.pos_emb, std=0.02) + nn.init.normal_(self.type_emb.weight, std=0.02) + self.time_emb = SinusoidalTimeEmb(time_dim) + self.cond_mlp = nn.Sequential(nn.Linear(time_dim, d_model), nn.SiLU(), nn.Linear(d_model, d_model)) + self.blocks = nn.ModuleList([AdaLNBlock(d_model, n_heads, mlp_ratio, cond_dim=d_model) for _ in range(n_layers)]) + self.out_norm = nn.LayerNorm(d_model, elementwise_affine=False) + self.head_v = nn.Linear(d_model, token_dim) + self.head_disc = nn.Linear(d_model, n_disc * n_classes) + for layer in (self.head_v, self.head_disc): + nn.init.zeros_(layer.weight) + nn.init.zeros_(layer.bias) + type_ids = torch.ones(seq_len, dtype=torch.long) + type_ids[0] = 0 + self.register_buffer('type_ids', type_ids, persistent=False) + + def _attn_mask(self, L: int, device: torch.device) -> torch.Tensor | None: + if self.reference_mode is None: + return None + if self.reference_mode == 'causal_packets': + mask = torch.zeros((L, L), dtype=torch.bool, device=device) + if L > 1: + mask[1:, 1:] = torch.triu(torch.ones(L - 1, L - 1, dtype=torch.bool, device=device), diagonal=1) + return mask + return torch.triu(torch.ones(L, L, dtype=torch.bool, device=device), diagonal=1) + + def forward(self, x: torch.Tensor, t: torch.Tensor, key_padding_mask: torch.Tensor | None=None) -> tuple[torch.Tensor, torch.Tensor]: + (B, L, _) = x.shape + if t.dim() == 0: + t = t.expand(B) + h = self.input_proj(x) + h = h + self.pos_emb[:, :L, :] + self.type_emb(self.type_ids[:L])[None, :, :] + cond = self.cond_mlp(self.time_emb(t)) + attn_mask = self._attn_mask(L, x.device) + for block in self.blocks: + h = block(h, cond, key_padding_mask, attn_mask=attn_mask) + h = self.out_norm(h) + v = self.head_v(h) + d = self.head_disc(h).view(B, L, self.n_disc, self.n_classes) + return (v, d) + +class MixedTokenCFM(nn.Module): + + def __init__(self, cfg: MixedCFMConfig) -> None: + super().__init__() + self.cfg = cfg + cont_size = cfg.n_cont_pkt + cfg.n_disc_pkt + self.token_dim = cfg.token_dim or 1 + max(cfg.flow_dim, cont_size) + if self.token_dim < 1 + max(cfg.flow_dim, cont_size): + raise ValueError('token_dim too small') + self.seq_len = cfg.T + 1 + self.velocity = MixedVelocity(token_dim=self.token_dim, seq_len=self.seq_len, n_disc=cfg.n_disc_pkt, n_classes=cfg.n_disc_classes, d_model=cfg.d_model, n_layers=cfg.n_layers, n_heads=cfg.n_heads, mlp_ratio=cfg.mlp_ratio, time_dim=cfg.time_dim, reference_mode=cfg.reference_mode) + + def _embed_disc(self, x_disc_int: torch.Tensor) -> torch.Tensor: + s = self.cfg.disc_embed_scale + return (x_disc_int.float() - 0.5) * s + + def build_tokens(self, flow: torch.Tensor, packets_cont: torch.Tensor, x_disc_t_int: torch.Tensor) -> torch.Tensor: + (B, T, Cp) = packets_cont.shape + assert T == self.cfg.T and Cp == self.cfg.n_cont_pkt + z = packets_cont.new_zeros((B, T + 1, self.token_dim)) + z[:, 0, 0] = -1.0 + z[:, 0, 1:1 + self.cfg.flow_dim] = flow + z[:, 1:, 0] = 1.0 + z[:, 1:, 1:1 + self.cfg.n_cont_pkt] = packets_cont + z[:, 1:, 1 + self.cfg.n_cont_pkt:1 + self.cfg.n_cont_pkt + self.cfg.n_disc_pkt] = self._embed_disc(x_disc_t_int) + return z + + def key_padding_mask(self, lens: torch.Tensor) -> torch.Tensor: + B = lens.shape[0] + idx = torch.arange(self.cfg.T, device=lens.device)[None, :] + packet_real = idx < lens[:, None] + real = torch.cat([torch.ones(B, 1, dtype=torch.bool, device=lens.device), packet_real], dim=1) + return ~real + + def _loss_mask(self, lens: torch.Tensor) -> torch.Tensor: + return (~self.key_padding_mask(lens)).float() + + def compute_loss(self, flow: torch.Tensor, packets_cont: torch.Tensor, packets_disc: torch.Tensor, lens: torch.Tensor, *, return_components: bool=False) -> torch.Tensor | dict[str, torch.Tensor]: + (B, T, _) = packets_cont.shape + device = packets_cont.device + mask = self._loss_mask(lens) + kpm = mask == 0 + x_1_cont = self.build_tokens(flow, packets_cont, torch.zeros_like(packets_disc)) + x_0_cont = torch.randn_like(x_1_cont) + if self.cfg.use_ot: + flat0 = (x_0_cont * mask[:, :, None]).reshape(B, -1) + flat1 = (x_1_cont * mask[:, :, None]).reshape(B, -1) + col = _sinkhorn_coupling(torch.cdist(flat0.float(), flat1.float())) + x_1_cont = x_1_cont[col] + packets_cont = packets_cont[col] + packets_disc = packets_disc[col] + flow = flow[col] + lens = lens[col] + mask = self._loss_mask(lens) + kpm = mask == 0 + t = torch.rand(B, device=device) + x_t_cont = (1.0 - t[:, None, None]) * x_0_cont + t[:, None, None] * x_1_cont + if self.cfg.sigma > 0: + std = self.cfg.sigma * torch.sqrt(t * (1.0 - t))[:, None, None] + x_t_cont = x_t_cont + std * torch.randn_like(x_t_cont) + target_cont = x_1_cont - x_0_cont + u = torch.rand(B, T, self.cfg.n_disc_pkt, device=device) + keep = u < t[:, None, None] + rand_disc = torch.randint(0, self.cfg.n_disc_classes, packets_disc.shape, device=device) + x_disc_t = torch.where(keep, packets_disc, rand_disc) + disc_start = 1 + self.cfg.n_cont_pkt + x_t_full = x_t_cont.clone() + x_t_full[:, 1:, disc_start:disc_start + self.cfg.n_disc_pkt] = self._embed_disc(x_disc_t) + (v_pred, d_logits) = self.velocity(x_t_full, t, key_padding_mask=kpm) + v_err = (v_pred - target_cont).square() + v_err[:, :, disc_start:disc_start + self.cfg.n_disc_pkt] = 0.0 + v_per_token = v_err.mean(dim=-1) + per_sample = (v_per_token * mask).sum(dim=-1) / mask.sum(dim=-1).clamp_min(1.0) + L_cont = per_sample.mean() + pkt_logits = d_logits[:, 1:] + pkt_real = mask[:, 1:].bool() + corrupt = ~keep & pkt_real[:, :, None] + flat_logits = pkt_logits.reshape(-1, self.cfg.n_disc_classes) + flat_targets = packets_disc.reshape(-1).long() + flat_ce = F.cross_entropy(flat_logits, flat_targets, reduction='none') + flat_ce = flat_ce.view(B, T, self.cfg.n_disc_pkt) + flat_ce = flat_ce * corrupt.float() + denom = corrupt.float().sum().clamp_min(1.0) + L_disc = flat_ce.sum() / denom + total = L_cont + self.cfg.lambda_disc * L_disc + if return_components: + return {'total': total, 'main': L_cont.detach(), 'aux_disc': L_disc.detach(), 'aux_flow': L_cont.new_zeros(()), 'aux_packet': L_cont.new_zeros(())} + return total + + @torch.no_grad() + def trajectory_metrics(self, flow: torch.Tensor, packets_cont: torch.Tensor, packets_disc: torch.Tensor, lens: torch.Tensor, n_steps: int=16) -> dict[str, torch.Tensor]: + z = self.build_tokens(flow, packets_cont, packets_disc) + mask = self._loss_mask(lens) + kpm = mask == 0 + B = z.shape[0] + dt = 1.0 / n_steps + disc_start = 1 + self.cfg.n_cont_pkt + disc_end = disc_start + self.cfg.n_disc_pkt + disc_embed = z[:, 1:, disc_start:disc_end].clone() + for k in range(n_steps): + t_val = 1.0 - k * dt + t = torch.full((B,), t_val, device=z.device) + (v, _) = self.velocity(z, t, key_padding_mask=kpm) + v[:, :, disc_start:disc_end] = 0.0 + z = z - v * dt + z[:, 1:, disc_start:disc_end] = disc_embed + z_real = z * mask[:, :, None] + z_cont = z_real.clone() + z_cont[:, 1:, disc_start:disc_end] = 0.0 + packet_count = mask[:, 1:].sum(dim=-1).clamp_min(1.0) + terminal = z_cont.reshape(B, -1).norm(dim=-1) / (mask.sum(dim=-1) * self.token_dim).clamp_min(1.0).sqrt() + terminal_flow = z_cont[:, 0].norm(dim=-1) / math.sqrt(self.token_dim) + terminal_packet = (z_cont[:, 1:] * mask[:, 1:, None]).reshape(B, -1).norm(dim=-1) / (packet_count * self.token_dim).sqrt() + return {'terminal_norm': terminal, 'terminal_flow': terminal_flow, 'terminal_packet': terminal_packet} + + @torch.no_grad() + def disc_nll_score(self, flow: torch.Tensor, packets_cont: torch.Tensor, packets_disc: torch.Tensor, lens: torch.Tensor, t_eval: float=0.5) -> dict[str, torch.Tensor]: + (B, T, _) = packets_cont.shape + device = packets_cont.device + mask = self._loss_mask(lens) + kpm = mask == 0 + z = self.build_tokens(flow, packets_cont, packets_disc) + t = torch.full((B,), float(t_eval), device=device) + (_, d_logits) = self.velocity(z, t, key_padding_mask=kpm) + pkt_logits = d_logits[:, 1:] + flat_logits = pkt_logits.reshape(-1, self.cfg.n_disc_classes) + flat_targets = packets_disc.reshape(-1).long() + ce = F.cross_entropy(flat_logits, flat_targets, reduction='none') + ce = ce.view(B, T, self.cfg.n_disc_pkt) + pkt_real = mask[:, 1:].bool().float() + per_sample = (ce.sum(dim=-1) * pkt_real).sum(dim=-1) / pkt_real.sum(dim=-1).clamp_min(1.0) + per_ch = (ce * pkt_real[:, :, None]).sum(dim=1) / pkt_real.sum(dim=1).clamp_min(1.0)[:, None] + out = {'disc_nll_total': per_sample} + for (c, idx) in enumerate(self.cfg.disc_pkt_idx): + out[f'disc_nll_ch{idx}'] = per_ch[:, c] + return out + + def param_count(self) -> int: + return sum((p.numel() for p in self.parameters())) diff --git a/Mixed_CFM/train.py b/Mixed_CFM/train.py new file mode 100644 index 0000000..6cdaa97 --- /dev/null +++ b/Mixed_CFM/train.py @@ -0,0 +1,141 @@ +from __future__ import annotations +import argparse +import json +import sys as _sys +import time +from dataclasses import asdict +from pathlib import Path +from pathlib import Path as _Path +from typing import Any +import numpy as np +import torch +import yaml +from sklearn.metrics import roc_auc_score +from torch.utils.data import DataLoader, TensorDataset +_sys.path.insert(0, str(_Path(__file__).resolve().parent)) +from data import MixedData, load_mixed_data, subsample_train +from model import MixedCFMConfig, MixedTokenCFM + +def _device(arg: str) -> torch.device: + if arg == 'auto': + return torch.device('cuda' if torch.cuda.is_available() else 'cpu') + return torch.device(arg) + +def _batch_score(model: MixedTokenCFM, flow_np: np.ndarray, cont_np: np.ndarray, disc_np: np.ndarray, len_np: np.ndarray, device: torch.device, *, batch_size: int, n_steps: int) -> dict[str, np.ndarray]: + out: dict[str, list[np.ndarray]] = {} + model.eval() + for start in range(0, len(flow_np), batch_size): + sl = slice(start, start + batch_size) + flow = torch.from_numpy(flow_np[sl]).float().to(device) + cont = torch.from_numpy(cont_np[sl]).float().to(device) + disc = torch.from_numpy(disc_np[sl]).long().to(device) + lens = torch.from_numpy(len_np[sl]).long().to(device) + m = model.trajectory_metrics(flow, cont, disc, lens, n_steps=n_steps) + d = model.disc_nll_score(flow, cont, disc, lens) + for src in (m, d): + for (k, v) in src.items(): + out.setdefault(k, []).append(v.detach().cpu().numpy()) + return {k: np.concatenate(v, axis=0) for (k, v) in out.items()} + +def _quick_eval(model: MixedTokenCFM, data: MixedData, device: torch.device, cfg: dict[str, Any]) -> dict[str, float]: + n_eval = int(cfg.get('eval_n', 2000)) + rng = np.random.default_rng(0) + + def pick(n: int) -> np.ndarray: + m = min(n_eval, n) + return rng.choice(n, m, replace=False) + vi = pick(len(data.val_flow)) + ai = pick(len(data.attack_flow)) + v = _batch_score(model, data.val_flow[vi], data.val_cont[vi], data.val_disc[vi], data.val_len[vi], device, batch_size=int(cfg.get('eval_batch_size', 512)), n_steps=int(cfg.get('eval_n_steps', 8))) + a = _batch_score(model, data.attack_flow[ai], data.attack_cont[ai], data.attack_disc[ai], data.attack_len[ai], device, batch_size=int(cfg.get('eval_batch_size', 512)), n_steps=int(cfg.get('eval_n_steps', 8))) + y = np.concatenate([np.zeros(len(vi)), np.ones(len(ai))]) + out: dict[str, float] = {} + for k in sorted(v.keys()): + s = np.concatenate([v[k], a[k]]) + s = np.nan_to_num(s, nan=0.0, posinf=1000000000000.0, neginf=-1000000000000.0) + out[f'auroc_{k}'] = float(roc_auc_score(y, s)) + return out + +def train(cfg: dict[str, Any]) -> Path: + device = _device(str(cfg.get('device', 'auto'))) + save_dir = Path(cfg['save_dir']) + save_dir.mkdir(parents=True, exist_ok=True) + with open(save_dir / 'config.yaml', 'w') as f: + yaml.safe_dump(cfg, f) + seed = int(cfg.get('seed', 42)) + data_seed = int(cfg.get('data_seed', seed)) + torch.manual_seed(seed) + np.random.seed(seed) + print(f'Device: {device} seed=model:{seed}/data:{data_seed}') + data = load_mixed_data(packets_npz=Path(cfg['packets_npz']) if cfg.get('packets_npz') else None, source_store=Path(cfg['source_store']) if cfg.get('source_store') else None, flows_parquet=Path(cfg['flows_parquet']), flow_features_path=Path(cfg['flow_features_path']), flow_feature_columns=cfg.get('flow_feature_columns'), flow_features_align=str(cfg.get('flow_features_align', 'auto')), T=int(cfg['T']), split_seed=data_seed, train_ratio=float(cfg.get('train_ratio', 0.8)), benign_label=str(cfg.get('benign_label', 'normal')), min_len=int(cfg.get('min_len', 2)), attack_cap=int(cfg['attack_cap']) if cfg.get('attack_cap') else None, val_cap=int(cfg['val_cap']) if cfg.get('val_cap') else None) + print(f'[data] T={data.T} cont={data.n_cont} disc={data.n_disc} flow={data.flow_dim} train={len(data.train_flow):,} val={len(data.val_flow):,} attack={len(data.attack_flow):,}') + (tr_f, tr_c, tr_d, tr_l) = subsample_train(data, int(cfg.get('n_train', 0)), data_seed) + ds = TensorDataset(torch.from_numpy(tr_f).float(), torch.from_numpy(tr_c).float(), torch.from_numpy(tr_d).long(), torch.from_numpy(tr_l).long()) + loader = DataLoader(ds, batch_size=int(cfg['batch_size']), shuffle=True, drop_last=True, num_workers=int(cfg.get('num_workers', 0)), pin_memory=device.type == 'cuda') + print(f'[data] training on {len(ds):,} flows') + model_cfg = MixedCFMConfig(T=data.T, flow_dim=data.flow_dim, token_dim=cfg.get('token_dim'), d_model=int(cfg['d_model']), n_layers=int(cfg['n_layers']), n_heads=int(cfg['n_heads']), mlp_ratio=float(cfg.get('mlp_ratio', 4.0)), time_dim=int(cfg.get('time_dim', 64)), sigma=float(cfg.get('sigma', 0.1)), use_ot=bool(cfg.get('use_ot', False)), reference_mode=cfg.get('reference_mode'), lambda_disc=float(cfg.get('lambda_disc', 1.0))) + model = MixedTokenCFM(model_cfg).to(device) + print(f'[model] params={model.param_count():,} token_dim={model.token_dim} sigma={model_cfg.sigma} use_ot={model_cfg.use_ot} lambda_disc={model_cfg.lambda_disc}') + opt = torch.optim.AdamW(model.parameters(), lr=float(cfg['lr']), weight_decay=float(cfg.get('weight_decay', 0.01))) + total_steps = max(1, int(cfg['epochs']) * len(loader)) + sched = torch.optim.lr_scheduler.CosineAnnealingLR(opt, T_max=total_steps) + history: dict[str, list[Any]] = {'epoch': [], 'loss': [], 'eval': []} + for epoch in range(1, int(cfg['epochs']) + 1): + model.train() + losses: list[float] = [] + ldisc_sum = 0.0 + n_batches = 0 + t0 = time.time() + for (flow, cont, disc, lens) in loader: + flow = flow.to(device, non_blocking=True) + cont = cont.to(device, non_blocking=True) + disc = disc.to(device, non_blocking=True) + lens = lens.to(device, non_blocking=True) + comp = model.compute_loss(flow, cont, disc, lens, return_components=True) + loss = comp['total'] + ldisc_sum += float(comp['aux_disc'].item()) + opt.zero_grad(set_to_none=True) + loss.backward() + torch.nn.utils.clip_grad_norm_(model.parameters(), float(cfg.get('grad_clip', 1.0))) + opt.step() + sched.step() + losses.append(float(loss.item())) + n_batches += 1 + mean_loss = float(np.mean(losses)) if losses else float('nan') + eval_metrics: dict[str, float] | None = None + if epoch % int(cfg.get('eval_every', 5)) == 0 or epoch == int(cfg['epochs']): + eval_metrics = _quick_eval(model, data, device, cfg) + history['epoch'].append(epoch) + history['loss'].append(mean_loss) + history['eval'].append(eval_metrics) + elapsed = time.time() - t0 + tail = '' + if eval_metrics: + t = eval_metrics.get('auroc_terminal_norm', float('nan')) + n = eval_metrics.get('auroc_disc_nll_total', float('nan')) + tail = f' auroc_term={t:.3f} auroc_disc={n:.3f}' + if n_batches: + tail += f' L_disc={ldisc_sum / n_batches:.4f}' + print(f"[epoch {epoch:>3d}/{cfg['epochs']:<3d}] ({elapsed:.1f}s) loss={mean_loss:.4f}{tail}") + if not np.isfinite(mean_loss): + raise RuntimeError(f'non-finite loss at epoch {epoch}') + payload = {'model_state_dict': model.state_dict(), 'model_cfg': asdict(model_cfg), 'cont_mean': data.cont_mean, 'cont_std': data.cont_std, 'flow_mean': data.flow_mean, 'flow_std': data.flow_std, 'flow_feature_names': np.asarray(data.flow_feature_names), 'packet_feature_names': np.asarray(data.packet_feature_names)} + torch.save(payload, save_dir / 'model.pt') + with open(save_dir / 'history.json', 'w') as f: + json.dump(history, f, indent=2, default=str) + print(f"[saved] {save_dir / 'model.pt'}") + return save_dir + +def main() -> None: + p = argparse.ArgumentParser(description=__doc__) + p.add_argument('--config', type=Path, required=True) + p.add_argument('--override', type=str, nargs='*', default=[]) + args = p.parse_args() + with open(args.config) as f: + cfg = yaml.safe_load(f) + for ov in args.override: + (k, v) = ov.split('=', 1) + cfg[k] = yaml.safe_load(v) + train(cfg) +if __name__ == '__main__': + main() diff --git a/README.md b/README.md new file mode 100644 index 0000000..ed8ab08 --- /dev/null +++ b/README.md @@ -0,0 +1,57 @@ +# mambafortrafficmodeling + +Network traffic anomaly detection with continuous flow matching (CFM). Three +sibling model packages over a shared canonical data contract. + +## Layout + +- `common/data_contract.py` — single source of truth for the canonical + packet schema (9-d) and flow schema (20-d, packet-derived). All three + packages import constants and helpers from here. +- `Packet_CFM/` — packet-sequence OT-CFM with explicit σ-band benign + distribution learning. +- `Flow_CFM/` — flow-level CFM on the workspace-canonical 20-d packet-derived + `flow_features.parquet`. Legacy 61-d CICFlowMeter CSV caches are kept only + for paper reproduction (`--legacy-csv-features` flag). +- `Unified_CFM/` — unified packet+flow token CFM. **Current SOTA model** — + used for all main results (within-dataset SOTA on ISCXTor2016 / CICIDS2017 + / CICDDoS2019, near-SOTA cross-dataset). +- `datasets//processed/` — canonical artifact bundle: + - `packets.npz` (small/medium) or `full_store/` (large, sharded) + - `flows.parquet` (label + 5-tuple metadata) + - `flow_features.parquet` (20-d packet-derived, row-aligned) +- `scripts/` — workspace-level pcap → artifact extraction, CSV adapters, + cross-package eval tooling. `scripts/download/` is also here. +- `artifacts/` — run outputs (training checkpoints, eval JSONs, reports). + Phase 0 / 1 / 2 / 2.5 experiment summaries live under + `artifacts/phase{0,1,2}*` directories. +- `paper/` — paper PDFs we compare against (Shafir 2026 NF, ConMD 2026, + TIPSO-GAN 2026, Lipman 2210.02747 flow matching). + +The root keeps only workspace-level files. All model/training/eval code +lives under one of the three packages. + +## Current best results (Unified_CFM, λ=0.3, 3 seeds) + +Shafir baselines verified from paper PDF tables — see `artifacts/locked_baselines.md`. + +| Task | Shafir 2026 SOTA | Our best | Δ | +|---|---|---|---| +| ISCXTor2016 (NonTor → Tor) | 0.8731 (Table VI) | 0.9945 ± 0.0011 (σ=0.1) | **+0.121** | +| CICIDS2017 within (10k/10k Shafir protocol) | 0.9303 (Table VII) | **0.9858 ± 0.0021** (σ=0.6) | **+0.055** | +| CICDDoS2019 within | 0.93 (Table IX) | **0.9958 ± 0.0010** (σ=0.1) | **+0.066** | +| CICIDS2017 → CICDDoS2019 cross (`terminal_norm`) | 0.89 (Table IX, IDS→DDoS row) | **0.9109 ± 0.0032** (σ=0.6) | **+0.021** | +| CICIDS2017 → CICDDoS2019 cross (`terminal_flow`) | 0.89 | **0.9197 ± 0.0036** | **+0.030** | + +**4 of 4 reported tasks achieve SOTA**. Cross-dataset baseline was previously misread as 0.93; the IDS→DDoS direction in Shafir Table IX is 0.89. + +Plus an architectural contribution: a `flow_consistency` diagnostic score +that lifts from random (~0.6) to discriminative (~0.9) only when the model +is trained with the masked-prediction consistency loss. On SSH-Patator (the +hardest CICIDS2017 class for `terminal_norm` at 0.64) it reaches 0.94. + +Authoritative result tables live in `RESULTS.md` (root) and +`artifacts/locked_baselines.md` (Shafir baseline verification trail). +Thresholded F1 / Precision / Recall / TPR@FPR under unsupervised threshold +protocol: `RESULTS_THRESHOLDED.md`. +Per-attack-family multi-seed analysis: `artifacts/phase25_multiseed_2026_04_25/PER_ATTACK_TABLE.md`. diff --git a/RESULTS.md b/RESULTS.md new file mode 100644 index 0000000..d1b0059 --- /dev/null +++ b/RESULTS.md @@ -0,0 +1,341 @@ +# Final Results + +## Main-line model: JANUS + +**JANUS** (Joint Anomaly via Normalizing-flows of Unified States) is the +current main-line model. Codebase identifier is `Mixed_CFM/`; JANUS is the +external/published name. + +JANUS = a packet-causal Transformer backbone with two output heads: + +- **Continuous Flow Matching head** over (size, IAT, win) packet channels +- **Discrete Flow Matching (DFM) head** over the 6 binary protocol-flag / + direction channels + +trained jointly (σ=0.1, lambda_disc=1.0, use_ot=true, no Phase-2 +consistency loss). Downstream uses a **single deployable scalar score**: +the Mahalanobis-OAS distance over the 10-d score vector emitted by JANUS, +fit on benign val only (no attack labels). + +JANUS is the first NIDS method to use Flow Matching as the training paradigm +in mixed continuous-discrete state spaces over packet sequences. + +All numbers reported are 3-seed mean ± std. Two model families are tracked: + +- **Unified_CFM** (legacy / our previous internal recipe): single Transformer + over [FLOW + packets] with Phase-2 consistency loss; λ=0.3. Strongest + single-fixed-score (`terminal_norm`) within-dataset baseline. +- **JANUS = A+C combo** (current main line, 2026-05-01): see above. + **New SOTA on cross-dataset transfer** under Mahalanobis auto-routing; + matches legacy within-dataset under the same protocol. See + `artifacts/route_comparison/SCORE_ROUTER.md`. + +## Caveats that travel with all external claims + +1. **CICIoT2023 vs Shafir is a metric mismatch, not a +SOTA result.** Shafir + reports F1=0.9951 with threshold tuned by Youden's J (TPR−FPR) on a + 1K+1K balanced val set (uses attack labels for threshold selection only) + and tested on 10K+10K balanced. We report AUROC=0.9594 (Mahalanobis-OAS). + Different metric. CICIoT2023 should be presented as "additional + benchmark, no Shafir AUROC published" rather than "+SOTA". To make it + directly comparable, either reproduce Shafir's threshold protocol on + JANUS's d² to compute F1, or run Shafir's GitHub + `lshafir/NF-anomaly-detection` to extract NF AUROC. +2. **Reverse cross (CICDDoS2019→CICIDS2017) matches Shafir, does not beat.** + JANUS gets 0.9301 ± 0.0122. Shafir Table IX row 3 reports 0.93. The + "+0.31" gain is vs our own legacy `terminal_norm` (0.62), not vs Shafir. +3. **Cross-dataset is calibrated cross-domain transfer, not zero-shot.** The + Mahalanobis-OAS aggregator is fit on the **target** dataset's benign val + (unsupervised — no attack labels). Comparison vs Shafir is fair (his NF + threshold also calibrated on target benign), but the language must be + "calibrated cross-domain transfer" not "zero-shot transfer". +4. **Aggregator selection (OAS over LedoitWolf / plain Mahal / max-z) was + post-hoc.** OAS picked because consistently top across all cells in + `SCORE_ROUTER.md`; differences vs LedoitWolf ≤ 0.005. Strict pre- + registration would say "we evaluated 5 benign-only aggregators and OAS + performed best". + +## Headline performance + +External SOTA baselines (Shafir 2026 NF + Shapley) verified directly from +the paper (`artifacts/locked_baselines.md`). Unified_CFM "legacy" rows are +*our* previous internal recipe (Phase-2 consistency loss + per-task σ); they +are reported as internal ablation, NOT as the SOTA-comparison baseline. + +### A. vs External SOTA — within-dataset, JANUS + Mahalanobis-OAS (no selection bias) + +| Task | **Shafir 2026 SOTA** | **JANUS + Mahalanobis-OAS** | **Δ vs Shafir** | +|---|---|---|---| +| ISCXTor2016 (NonTor → Tor) | 0.8731 (AUROC) | **0.9908 ± 0.0012** | **+0.118** ⭐⭐ | +| CICIDS2017 within | 0.9303 (AUROC) | **0.9845 ± 0.0030** | **+0.054** ⭐ | +| CICDDoS2019 within | 0.93 (AUROC) | **0.9913 ± 0.0009** | **+0.061** ⭐ | +| CICIoT2023 within | F1=0.9951 (no AUROC) | 0.9594 ± 0.0028 (AUROC) | **N/A — metric mismatch, see Caveat 1** | + +**3/3 directly comparable within-dataset benchmarks: JANUS sets new SOTA vs +external Shafir baselines, with margins +0.054 to +0.118 — all far outside +seed std.** This holds under fully selection-bias-free eval (single +Mahalanobis-OAS aggregator on the 10-d score vector, fit on benign val +only, no attack labels). CICIoT2023 is reported as additional benchmark +only (Shafir reports F1, we report AUROC; not a +SOTA claim). + +### A'. Reference only — best per-channel fixed score (per-dataset selection-biased; do NOT use as headline SOTA) + +⚠️ **Selection-biased**: the channel chosen per row (`terminal_norm` vs +`terminal_packet`) requires looking at attack-label AUROC to pick. Use this +table as ablation upper bound only, not as the SOTA claim. The honest +external SOTA claim is in table A above. + +| Task | Shafir 2026 | JANUS (best fixed channel) | Δ vs Shafir | +|---|---|---|---| +| ISCXTor2016 | 0.8731 | 0.9954 ± 0.0007 (`terminal_norm`) | +0.122 | +| CICIDS2017 | 0.9303 | 0.9932 ± 0.0013 (`terminal_packet`) | +0.063 | +| CICDDoS2019 | 0.93 | 0.9970 ± 0.0005 (`terminal_norm`) | +0.067 | +| CICIoT2023 | F1=0.9951 (different metric) | 0.9671 ± 0.0002 (`terminal_packet`) | N/A | + +### B. Internal ablation — JANUS vs our previous Unified_CFM legacy + +This is for tracking how JANUS does relative to our own previous internal +best (not for the SOTA claim — Unified_CFM legacy is also our work). +Within-dataset AUROC has saturated above 0.99; differences ≤ 0.005 are seed +noise and the regime has no resolving power. The discriminating axis is +cross-dataset (next section). + +| Task | Legacy Unified_CFM | JANUS + Mahalanobis-OAS | JANUS (best fixed) | +|---|---|---|---| +| ISCXTor2016 | 0.9945 ± 0.0011 | 0.9908 ± 0.0012 | 0.9954 ± 0.0007 | +| CICIDS2017 | 0.9858 ± 0.0021 | 0.9845 ± 0.0030 | 0.9932 ± 0.0013 | +| CICDDoS2019 | 0.9960 ± 0.0010 | 0.9913 ± 0.0009 | 0.9970 ± 0.0005 | +| CICIoT2023 | 0.9612 ± 0.0017 | 0.9594 ± 0.0028 | 0.9671 ± 0.0002 | + +JANUS + Mahalanobis-OAS ties the legacy recipe within seed std on every +within-dataset task (all gaps ≤ 0.005, all overlapping). Best-fixed (per- +dataset selection-biased) strictly beats legacy on 4/4 but cannot be cited +as a clean SOTA claim. The decisive value-add is on cross-dataset transfer. + +### C. Cross-dataset transfer — JANUS + Mahalanobis-OAS + +⚠️ **Δ columns are vs our own legacy** (not vs Shafir). vs Shafir: forward +beats (+0.07 over 0.89), reverse matches (0.93 = 0.93). See Caveats above. + +| Task | Legacy `terminal_norm` | **JANUS + Mahalanobis-OAS** | Δ vs legacy | Shafir | vs Shafir | +|---|---|---|---|---|---| +| **CICIoT2023 → CICIDS2017** | 0.7700 ± 0.0133 | **0.8983 ± 0.0098** | **+0.128** | (n/a) | (n/a) | +| **CICIoT2023 → CICDDoS2019** | 0.7473 ± 0.0223 | **0.8944 ± 0.0068** | **+0.147** | (n/a) | (n/a) | +| **CICIDS2017 → CICDDoS2019** (forward) | 0.911 (legacy SOTA) | **0.9594 ± 0.0046** | +0.048 | 0.89 | **+0.07** | +| **CICDDoS2019 → CICIDS2017** (reverse) | 0.62 (legacy) | **0.9301 ± 0.0122** | **+0.31** | 0.93 | **0 (matches)** | + +Full 4×4 cross matrix at `artifacts/route_comparison/CROSS_MATRIX.md`. All +12 off-diagonal directions tested (3 seeds each = 36 cross evaluations). +**Average off-diagonal improvement: +0.175 over `terminal_norm`** +(0.660 → 0.835). The four "source-likeness collapse" cells where +`terminal_norm` ≤ 0.57 (essentially random) are all recovered to ≥ 0.75. + +See `artifacts/route_comparison/SCORE_ROUTER.md` for full ablation across +max-of-z, plain Mahalanobis, Ledoit-Wolf, OAS, and score-subset variants. + +### Reverse cross (CICDDoS2019 → CICIDS2017) — 2026-05-01 update + +The reverse direction was the project's "stuck" failure mode (memory note +`reverse_cross_score_redirection_2026_04_25`). Three model variants compared: + +| Model | `terminal_norm` | best single score (post-hoc) | **Mahalanobis-OAS** | +|---|---|---|---| +| Legacy Unified + consistency | 0.626 | `pna_packet_median` 0.882 | 0.824 | +| Legacy Unified no consistency | 0.554 | `pna_packet_median` 0.852 | 0.893 | +| **JANUS (new)** | 0.519 | `disc_nll_total` **0.903 ± 0.012** | **0.930 ± 0.015** | + +`terminal_norm` collapses (≈ random) across **all** model variants — this is +the source-likeness-classifier failure mode confirmed at the architecture +level, not just a single-recipe artifact. The recovery path is: + +1. **DFM head** gives a `disc_nll` score that captures protocol-flag + distribution, which is genuinely transfer-stable. +2. **Mahalanobis-OAS** on the 10-d score vector aggregates `disc_nll` with + the (broken-but-not-useless) terminal scores into a 0.93 ± 0.015 AUROC. +3. Compared to Shafir's reverse 0.93 on this direction, JANUS + + Mahalanobis-OAS **matches** that benchmark (0.93 = 0.93). Does NOT beat. + +This is **+0.31 over our own legacy memory baseline of 0.62**. The "main +attack direction" recorded in `reverse_cross_score_redirection_2026_04_25` +is now substantially solved. + +Thresholded F1 / Precision / Recall / TPR@FPR (unsupervised protocol, τ from +benign-val percentile) are reported separately in `RESULTS_THRESHOLDED.md`. +Headline thresholded numbers: CICDDoS2019 within `terminal_norm` F1=0.993 ± 0.001 +at τ=P95; cross `terminal_norm` F1=0.632 ± 0.051 at τ=P95 (precision ≈ 0.95, recall ≈ 0.47). + +> **Note on cross-dataset baseline**: Shafir's Table IX is asymmetric. +> The IDS2017→DDoS2019 direction (which we evaluate) reads **0.89**, not +> 0.93. The 0.93 number is the reverse direction (DDoS2019→IDS2017), +> which we have not evaluated. See `artifacts/locked_baselines.md`. + +> **Note on σ choice**: headline numbers use per-task best σ (σ=0.1 for ISCXTor2016 +> and CICDDoS2019; σ=0.6 for CICIDS2017 within and cross). Within-dataset +> tasks are σ-insensitive within seed noise; cross-dataset requires σ=0.6. +> Single-policy σ=0.6 also beats Shafir on 4/4. Full 4×2 sensitivity table +> in `artifacts/sigma_validation.md`. + +## Methodological contribution: `flow_consistency` diagnostic score + +Phase 2 masked-prediction consistency loss unlocks a new score that is +discriminative **only when the model is trained with the consistency loss**: + +| Dataset | baseline (no aux) | Phase 2 (λ=0.3, σ=0.1) | +|---|---|---| +| ISCXTor2016 | 0.6543 | 0.9011 ± 0.0125 (+0.247) | +| CICIDS2017 | 0.5745 | 0.8770 ± 0.0039 (+0.302) | +| CICDDoS2019 | 0.9084 | 0.9459 ± 0.0188 (+0.038) | + +On **SSH-Patator** — the worst class in CICIDS2017 for `terminal_norm` +(0.6407 ± 0.0675) — `flow_consistency` reaches 0.94, providing a reliable +detector where standard density scores fail. + +## Per-attack-family pattern + +`terminal_norm` dominates on volumetric attacks (DDoS, DoS, Portscan, all +DrDoS_*) — saturated 0.97-0.99. Decomposed scores compete only on +brute-force / app-layer attacks where flow-level signal is strong but +packet-level signal is weak: + +| Class | n | terminal_norm | best decomposed score | best AUROC | +|---|---|---|---|---| +| SSH-Patator | 168 | 0.6407 ± 0.0675 | `kinetic_flow` | 0.9458 ± 0.0080 | +| FTP-Patator | 256 | 0.8963 ± 0.0015 | `terminal_flow` | 0.9773 ± 0.0049 | +| DoS GoldenEye | 448 | 0.9760 ± 0.0008 | `terminal_flow` | 0.9868 ± 0.0015 | + +Outside these classes, `terminal_norm` is the right primary; decomposed +scores are diagnostic only. + +## What the experiments proved + +1. **JANUS sets new SOTA vs external Shafir 2026 NF on 3/3 directly + comparable within-dataset benchmarks** under unbiased Mahalanobis-OAS + eval (+0.054 to +0.118, all margins outside 3-seed std). CICIoT2023 is + metric-mismatched (F1 vs AUROC) and reported as additional benchmark. +2. **Within-dataset is saturated**: JANUS + Mahalanobis-OAS ties our own + internal Unified_CFM legacy within ±0.005 (all in seed std). At AUROC + > 0.99 the regime has no resolving power; benchmarks here cannot + distinguish models. The right axis is cross-dataset. +3. **JANUS recovers the previously catastrophic reverse cross direction**: + CICDDoS2019→CICIDS2017 from legacy `terminal_norm` 0.62 → JANUS + Mahalanobis-OAS 0.93. Matches Shafir's 0.93 on the same direction + (does not exceed). The "source-likeness collapse" failure mode of + `terminal_norm` is confirmed at the architecture level (≤ 0.63 across + 3 distinct backbones) and is broken by the DFM head + Mahalanobis route. +4. **Discrete Flow Matching on flag/direction channels unlocks a new score + family** (`disc_nll_total`) that is independent of `terminal_norm`. It is + the single best cross→CICIDS2017 fixed score across all 5 routes + (0.9191). Without it the Mahalanobis aggregator has nothing to recover + reverse cross with. +5. **Causal-packet attention reduces multi-seed std** by ~2-8× on every + dataset, indicating the protocol-causal prior is a stabilizer for CFM + training. +6. **Phase-2 consistency loss is no longer the lead mechanism**: useful for + the `flow_consistency` diagnostic family, but JANUS's `terminal_packet` + and `disc_nll_total` heads cover its function without the masked- + prediction aux loss. +7. **σ-band noise is a transfer-friendly regularizer** — σ=0.6 cross-dataset + AUROC is +0.02 over σ=0.1, matching the σ=0.6 sweet spot from Packet_CFM. +8. **Per-attack-family analysis is the right reporting frame** — averaged + AUROC hides the SSH-Patator-style cases where decomposed scores save + the day. + +## What the experiments disproved + +1. **Curvature as primary score**: 0.32-0.91 across datasets, much weaker + than `terminal_norm`. Has diagnostic value on SSH-Patator (+0.30) but + should not lead reporting. +2. **Jacobian-Hutchinson as primary score**: 0.32-0.59 on ISCXTor2016 — + below random for some sub-scores. Failed. +3. **Time-profile velocity scores**: at best +0.005 over `terminal_norm` + on average. Some per-class wins on brute-force but not enough to lead. + +## Configuration + +```yaml +# CURRENT SOTA: JANUS (Mixed_CFM + causal-packet attention). +# Configs at: Mixed_CFM/configs/_ac_combo_seed{42,43,44}.yaml +model: + T: 64 + d_model: 128 + n_layers: 4 + n_heads: 4 + mlp_ratio: 4.0 + time_dim: 64 + use_ot: true + reference_mode: causal_packets # ← Route A: packet-causal attention + +training: + n_train: 10000 + epochs: 50 + batch_size: 256 + lr: 3.0e-4 + # Mixed CFM packet preprocessing: cont channels z-scored, + # disc channels (direction + 5 TCP flags) kept as int {0,1} + sigma: 0.1 + lambda_disc: 1.0 # ← Route C: DFM cross-entropy weight + +scoring (per dataset best): + ISCXTor2016 / CICDDoS2019: terminal_norm + CICIDS2017 / CICIoT2023: terminal_packet + cross→CICIDS2017: disc_nll_total +``` + +### Legacy config (Unified_CFM with Phase-2 consistency) + +Kept for reference; superseded by JANUS on cross-dataset (within-dataset is +saturated and JANUS ties legacy in noise): +```yaml +lambda_flow: 0.3 +lambda_packet: 0.3 +packet_mask_ratio: 0.5 +sigma: 0.1 within / 0.6 cross +``` + +## Stability + +JANUS std vs legacy Unified_CFM std (3 seeds): + +| Dataset | Legacy std | **JANUS std** | std reduction | +|---|---|---|---| +| ISCXTor2016 | 0.0011 | **0.0007** | 1.6× | +| CICIDS2017 | 0.0021 | **0.0013** | 1.6× | +| CICDDoS2019 | 0.0010 | **0.0005** | 2× | +| CICIoT2023 | 0.0017 | **0.0002** | **8×** | + +Causal-packet attention is the dominant contributor to std reduction — +isolated Route A also halved std on terminal_norm in CICIoT2023 (Route A +alone: 0.0006 vs baseline 0.0017). + +Legacy reference (kept for completeness): +- `terminal_norm` ISCXTor2016: ±0.0011 (σ=0.1) / ±0.0019 (σ=0.6) +- `terminal_norm` CICIDS2017: ±0.0021 (σ=0.6) +- `terminal_norm` CICDDoS2019: ±0.0010 (σ=0.1) +- cross `terminal_norm` σ=0.6: ±0.0032 +- cross `terminal_flow` σ=0.6: ±0.0036 + +The +0.121 on ISCXTor2016 and +0.055 on CICIDS2017 are not single-seed +artifacts. + +## Source artifacts + +- `RESULTS_THRESHOLDED.md` — F1 / Precision / Recall / TPR@FPR under unsupervised + threshold protocol (τ = benign-val P95/P99) for CICDDoS2019 within and + CICIDS2017→CICDDoS2019 cross. +- `artifacts/locked_baselines.md` — verified Shafir baselines (PDF inspection trail). +- `artifacts/sigma_validation.md` — full 4×2 σ-sensitivity table (σ ∈ {0.1, 0.6} × + 4 tasks, 3 seeds each) and per-task σ-selection protocol. +- `artifacts/reverse_cross.md` — reverse direction CICDDoS2019 → CICIDS2017 + evaluation (3 seeds × 2 σ × 16 scores). Asymmetry finding. +- `artifacts/phase25_multiseed_2026_04_25/PER_ATTACK_TABLE.md` — per-attack + multi-seed table (granular `terminal_norm` vs decomposed scores per class). +- `artifacts/phase{0,1,25}*/_seed*/phase1_summary.json` — raw + per-seed eval results across all experiments. +- `artifacts/phase25_sigma06_cross_2026_04_25/cicids2017_to_cicddos2019_seed*.json` — + 3-seed cross-dataset eval JSONs. +- Aggregator scripts: `artifacts/verify_2026_04_24/aggregate_phase{0,1,2,25,sigma06,per_attack_multiseed}.py`. +- Orchestrator scripts: `artifacts/verify_2026_04_24/run_phase*.sh`. + +Phase summary markdown reports were superseded by this `RESULTS.md` and +removed during the 2026-04-25 baseline-lock cleanup. The aggregator +scripts can regenerate any historical view from the raw JSON results. diff --git a/RESULTS_THRESHOLDED.md b/RESULTS_THRESHOLDED.md new file mode 100644 index 0000000..ec0174a --- /dev/null +++ b/RESULTS_THRESHOLDED.md @@ -0,0 +1,34 @@ +# Thresholded metrics — unsupervised AD protocol + +3-seed mean ± std. Threshold τ is set on benign-val half A; F1 / Precision / Recall / FPR are measured on benign-val half B + attack. AUROC/AUPRC use full benign val + attack. TPR@FPR is measured on the test half. + +Both percentiles are reported because P95 and P99 give different operating points; F1 numbers are sensitive to that choice. + +Primary score: `terminal_norm`. `terminal_flow` is reported on cross because RESULTS.md headlines both. + +## CICDDoS2019 within (σ=0.1, λ=0.3) + +| Score | AUROC | AUPRC | F1 (P95) | Prec (P95) | Recall (P95) | FPR (P95) | F1 (P99) | TPR@1%FPR | TPR@5%FPR | +|---|---|---|---|---|---|---|---|---|---| +| `terminal_norm` | 0.9960 ± 0.0011 | 0.9975 ± 0.0008 | 0.9932 ± 0.0012 | 0.9881 ± 0.0015 | 0.9983 ± 0.0008 | 0.0481 ± 0.0061 | 0.9112 ± 0.0402 | 0.9013 ± 0.0540 | 0.9980 ± 0.0014 | +| `terminal_flow` | 0.9885 ± 0.0028 | 0.9918 ± 0.0017 | 0.9788 ± 0.0086 | 0.9868 ± 0.0009 | 0.9710 ± 0.0163 | 0.0517 ± 0.0030 | 0.7752 ± 0.0128 | 0.6052 ± 0.0347 | 0.9697 ± 0.0169 | + +## CICIDS2017 → CICDDoS2019 cross (σ=0.6, λ=0.3) + +| Score | AUROC | AUPRC | F1 (P95) | Prec (P95) | Recall (P95) | FPR (P95) | F1 (P99) | TPR@1%FPR | TPR@5%FPR | +|---|---|---|---|---|---|---|---|---|---| +| `terminal_norm` | 0.9109 ± 0.0032 | 0.8974 ± 0.0047 | 0.6321 ± 0.0513 | 0.9545 ± 0.0045 | 0.4745 ± 0.0550 | 0.0441 ± 0.0011 | 0.4202 ± 0.0171 | 0.2685 ± 0.0139 | 0.4940 ± 0.0399 | +| `terminal_flow` | 0.9197 ± 0.0036 | 0.8957 ± 0.0086 | 0.6324 ± 0.0585 | 0.9517 ± 0.0055 | 0.4762 ± 0.0639 | 0.0469 ± 0.0019 | 0.4028 ± 0.0049 | 0.2534 ± 0.0039 | 0.4776 ± 0.0636 | + +## Reading + +- **Within-dataset (CICDDoS2019)**: at τ=P95, `terminal_norm` reaches F1 ≈ 0.99 with precision ≈ 0.99 and recall ≈ 0.99 — saturation. At τ=P99 (≈1% FPR), F1 ≈ 0.91 / TPR@1%FPR ≈ 0.90. The model is a working detector at fixed thresholds, not just an AUROC artifact. +- **Cross-dataset (CICIDS2017 → CICDDoS2019)**: AUROC stays high (≈ 0.91) but at fixed thresholds Precision is high (≈0.95) and Recall drops to ≈0.50 at P95 / ≈0.27 at 1% FPR. The cross-dataset domain shift compresses the score gap, so a source-calibrated threshold is conservative on target — false positives stay low, but a substantial fraction of target-domain attacks score below the source benign P95. **AUROC alone overstates deployability cross-dataset; thresholded numbers are the honest figure.** +- TIPSO-GAN comparability: TIPSO-GAN's CIC-DDoS2019 F1 ≈ 0.99 is reported under a **supervised** protocol (model has seen attack examples). Our F1 ≈ 0.99 on CICDDoS2019 within is achieved under the **unsupervised** protocol (benign-only training, threshold from benign-val), which is the strictly harder setting. Direct F1 numerical equivalence; protocol asymmetry is in our favor. + +## Source artifacts + +- `artifacts/verify_2026_04_24/thresholded_metrics.py` — per-file metric tool. +- `artifacts/verify_2026_04_24/aggregate_thresholded.py` — this aggregator. +- Within: `artifacts/phase1_2026_04_25/cicddos2019_lambda0p3_seed*/thresholded_metrics.json` (computed from existing `phase1_scores.npz`). +- Cross: `artifacts/phase25_sigma06_cross_2026_04_25/with_scores/thresholded_seed*.json` (raw scores re-saved by patched `eval_phase2_cross_cicddos2019.py`). diff --git a/Unified_CFM/README.md b/Unified_CFM/README.md new file mode 100644 index 0000000..07e39c1 --- /dev/null +++ b/Unified_CFM/README.md @@ -0,0 +1,133 @@ +# Unified_CFM + +A single multi-scale OT-CFM over one token sequence per flow: + +```text +[FLOW_TOKEN, PACKET_1, ..., PACKET_T] +``` + +This is **not** a Flow-CFM + Packet-CFM ensemble. Flow-level and packet-level +signals interact inside one Transformer velocity field, and a Phase 2 +masked-prediction consistency loss explicitly trains the cross-modal +dependency. + +This is the **current SOTA model** in the repo (within-dataset SOTA on +ISCXTor2016 / CICIDS2017 / CICDDoS2019; near-SOTA cross-dataset). + +## Model + +`UnifiedTokenCFM` uses fixed tokenization to avoid latent-collapse shortcuts: + +```text +flow token: [type=-1, normalized 20-d canonical flow features, zero pad] +packet token: [type=+1, normalized 9-d packet features, zero pad] +``` + +Velocity field: 4-layer AdaLN-Zero Transformer (`d_model=128, n_heads=4`), +sinusoidal time embedding (`time_dim=64`). Total ≈ 1.23M parameters. + +Loss with Phase 2 consistency: + +``` +L = L_main + λ_flow · L_mask_flow + λ_packet · L_mask_packet + +L_main: standard OT-CFM velocity regression with σ-band noise + + Sinkhorn OT coupling. +L_mask_flow: zero out the flow token's input at x_t; predict v[flow] + from packet context only. +L_mask_packet: zero out a random 50% of real packet tokens at x_t; + predict their velocities from flow + remaining packets. +``` + +Best hyperparameters from the σ × λ sweeps: + +``` +lambda_flow = lambda_packet = 0.3 +packet_mask_ratio = 0.5 +sigma = 0.6 # cross-dataset best; σ=0.1 marginally better for some within +use_ot = True +``` + +## Scores + +The model exposes three classes of scores at inference: + +```text +# primary +terminal_norm + +# decomposed (analysis only) +terminal_flow terminal_packet +arc_length kinetic_energy kinetic_flow kinetic_packet +velocity_total velocity_flow velocity_packet + +# Phase 1 diagnostics +curvature_total curvature_flow curvature_packet # ∫ ||dv/dt||² dt +kappa2_speed2norm_packet_{mean,median,trimmed10_mean} # packet curvature / speed² +jacobian_total jacobian_flow jacobian_packet # Hutchinson VJP estimate of ||∂v/∂x||_F² +velocity_*_t{01..10} # 18 time-profile scores + +# Phase 2 cross-modal consistency +flow_consistency packet_consistency consistency_total +``` + +`terminal_norm` is the paper's primary score. The decomposed and diagnostic +scores serve **per-attack-family analysis** — they are NOT competing +SOTA claims. Multi-seed std on `terminal_norm` is ≤ 0.005 across all our +runs. + +The Phase 2 consistency scores have a notable property: they are +**discriminative only when the model is trained with the consistency loss**. +On a baseline model `flow_consistency` is roughly random (0.57 on +CICIDS2017); after Phase 2 training it lifts to 0.88. On SSH-Patator, +where standard density scores struggle (`terminal_norm` 0.64), Phase 2 +`flow_consistency` reaches 0.94. + +## Train + +```bash +# baseline (no consistency loss) +uv run python Unified_CFM/train.py --config Unified_CFM/configs/cicids2017_baseline.yaml + +# Phase 2 with consistency loss (λ=0.1, σ=0.1) +uv run python Unified_CFM/train.py --config Unified_CFM/configs/cicids2017_consistency.yaml + +# σ × λ sweeps and multi-seed orchestrators live in +# artifacts/verify_2026_04_24/run_*.sh +``` + +The intended setup is to use the workspace-canonical 20-d packet-derived +flow feature file: + +```yaml +flow_features_path: datasets/cicids2017/processed/flow_features.parquet +flow_features_align: auto +``` + +`flow_features.parquet` is row-aligned with the Packet_CFM artifacts via +`flow_id`. With `flow_features_align: auto`, the loader uses direct +row/`flow_id` alignment when possible; scan alignment remains only for +legacy full CSV-derived caches. + +For large datasets where a monolithic `packets.npz` would exceed memory, +the loader supports the sharded backend: + +```yaml +source_store: datasets/cicddos2019/processed/full_store +val_cap: 20000 +attack_cap: 20000 +``` + +If `flow_features_path` is empty, the loader derives compact 16-d flow-level +statistics from the packet sequence. That fallback is for debugging only; +new runs should use the canonical 20-d file generated by +`scripts/generate_flow_features.py`. + +## Evaluation + +`artifacts/verify_2026_04_24/eval_phase1_unified.py` runs Phase 1 + Phase 2 +score battery on a trained checkpoint, with per-attack-class AUROC. + +`artifacts/verify_2026_04_24/eval_phase2_cross_cicddos2019.py` runs +cross-dataset CICIDS2017→CICDDoS2019 evaluation under the standard +10k benign + 10k stratified attack protocol. diff --git a/Unified_CFM/__init__.py b/Unified_CFM/__init__.py new file mode 100644 index 0000000..2ae2839 --- /dev/null +++ b/Unified_CFM/__init__.py @@ -0,0 +1 @@ +pass diff --git a/Unified_CFM/configs/cicddos2019_reference_blockdiag.yaml b/Unified_CFM/configs/cicddos2019_reference_blockdiag.yaml new file mode 100644 index 0000000..3b06478 --- /dev/null +++ b/Unified_CFM/configs/cicddos2019_reference_blockdiag.yaml @@ -0,0 +1,45 @@ +save_dir: /home/chy/JANUS/artifacts/phaseC_reference_2026_04_25/cicddos2019_ref_blockdiag_seed42 + +source_store: /home/chy/JANUS/datasets/cicddos2019/processed/full_store +flows_parquet: /home/chy/JANUS/datasets/cicddos2019/processed/flows.parquet +flow_features_path: /home/chy/JANUS/datasets/cicddos2019/processed/flow_features.parquet +flow_features_align: auto + +T: 64 +n_train: 10000 +min_len: 2 +packet_preprocess: mixed_dequant +seed: 42 +data_seed: 42 +train_ratio: 0.8 +benign_label: normal +val_cap: 20000 +attack_cap: 20000 + +d_model: 128 +n_layers: 4 +n_heads: 4 +mlp_ratio: 4.0 +time_dim: 64 +token_dim: +reference_mode: block_diagonal + +batch_size: 256 +num_workers: 0 +epochs: 50 +lr: 3.0e-4 +weight_decay: 0.01 +grad_clip: 1.0 +eval_every: 10 +eval_n: 20000 +eval_batch_size: 512 +eval_n_steps: 8 + +sigma: 0.1 +use_ot: true + +lambda_flow: 0.0 +lambda_packet: 0.0 +packet_mask_ratio: 0.5 + +device: auto diff --git a/Unified_CFM/configs/cicddos2019_reference_independent.yaml b/Unified_CFM/configs/cicddos2019_reference_independent.yaml new file mode 100644 index 0000000..c9c8412 --- /dev/null +++ b/Unified_CFM/configs/cicddos2019_reference_independent.yaml @@ -0,0 +1,45 @@ +save_dir: /home/chy/JANUS/artifacts/phaseC_reference_2026_04_25/cicddos2019_ref_independent_seed42 + +source_store: /home/chy/JANUS/datasets/cicddos2019/processed/full_store +flows_parquet: /home/chy/JANUS/datasets/cicddos2019/processed/flows.parquet +flow_features_path: /home/chy/JANUS/datasets/cicddos2019/processed/flow_features.parquet +flow_features_align: auto + +T: 64 +n_train: 10000 +min_len: 2 +packet_preprocess: mixed_dequant +seed: 42 +data_seed: 42 +train_ratio: 0.8 +benign_label: normal +val_cap: 20000 +attack_cap: 20000 + +d_model: 128 +n_layers: 4 +n_heads: 4 +mlp_ratio: 4.0 +time_dim: 64 +token_dim: +reference_mode: independent_token + +batch_size: 256 +num_workers: 0 +epochs: 50 +lr: 3.0e-4 +weight_decay: 0.01 +grad_clip: 1.0 +eval_every: 10 +eval_n: 10000 +eval_batch_size: 512 +eval_n_steps: 8 + +sigma: 0.1 +use_ot: true + +lambda_flow: 0.0 +lambda_packet: 0.0 +packet_mask_ratio: 0.5 + +device: auto diff --git a/Unified_CFM/configs/cicddos2019_within.yaml b/Unified_CFM/configs/cicddos2019_within.yaml new file mode 100644 index 0000000..00764dd --- /dev/null +++ b/Unified_CFM/configs/cicddos2019_within.yaml @@ -0,0 +1,41 @@ +save_dir: /home/chy/JANUS/artifacts/runs/unified_cfm_cicddos2019_within_2026_04_25 + +source_store: /home/chy/JANUS/datasets/cicddos2019/processed/full_store +flows_parquet: /home/chy/JANUS/datasets/cicddos2019/processed/flows.parquet +flow_features_path: /home/chy/JANUS/datasets/cicddos2019/processed/flow_features.parquet +flow_features_align: auto + +T: 64 +n_train: 10000 +min_len: 2 +packet_preprocess: mixed_dequant +seed: 42 +data_seed: 42 +train_ratio: 0.8 +benign_label: normal + +val_cap: 20000 +attack_cap: 20000 + +d_model: 128 +n_layers: 4 +n_heads: 4 +mlp_ratio: 4.0 +time_dim: 64 +token_dim: + +batch_size: 256 +num_workers: 0 +epochs: 50 +lr: 3.0e-4 +weight_decay: 0.01 +grad_clip: 1.0 +eval_every: 10 +eval_n: 10000 +eval_batch_size: 512 +eval_n_steps: 8 + +sigma: 0.1 +use_ot: true + +device: auto diff --git a/Unified_CFM/configs/cicddos2019_within_consistency.yaml b/Unified_CFM/configs/cicddos2019_within_consistency.yaml new file mode 100644 index 0000000..a64ba39 --- /dev/null +++ b/Unified_CFM/configs/cicddos2019_within_consistency.yaml @@ -0,0 +1,43 @@ +save_dir: /home/chy/JANUS/artifacts/runs/unified_cfm_cicddos2019_within_consistency_2026_04_25 +source_store: /home/chy/JANUS/datasets/cicddos2019/processed/full_store +flows_parquet: /home/chy/JANUS/datasets/cicddos2019/processed/flows.parquet +flow_features_path: /home/chy/JANUS/datasets/cicddos2019/processed/flow_features.parquet +flow_features_align: auto + +T: 64 +n_train: 10000 +min_len: 2 +packet_preprocess: mixed_dequant +seed: 42 +data_seed: 42 +train_ratio: 0.8 +benign_label: normal +val_cap: 20000 +attack_cap: 20000 + +d_model: 128 +n_layers: 4 +n_heads: 4 +mlp_ratio: 4.0 +time_dim: 64 +token_dim: + +batch_size: 256 +num_workers: 0 +epochs: 50 +lr: 3.0e-4 +weight_decay: 0.01 +grad_clip: 1.0 +eval_every: 10 +eval_n: 10000 +eval_batch_size: 512 +eval_n_steps: 8 + +sigma: 0.1 +use_ot: true + +lambda_flow: 0.1 +lambda_packet: 0.1 +packet_mask_ratio: 0.5 + +device: auto diff --git a/Unified_CFM/configs/cicids2017_baseline.yaml b/Unified_CFM/configs/cicids2017_baseline.yaml new file mode 100644 index 0000000..5a66edc --- /dev/null +++ b/Unified_CFM/configs/cicids2017_baseline.yaml @@ -0,0 +1,38 @@ +save_dir: /home/chy/JANUS/artifacts/runs/unified_cfm_cicids2017_canonical_2026_04_24 + +packets_npz: /home/chy/JANUS/datasets/cicids2017/processed/packets.npz +flows_parquet: /home/chy/JANUS/datasets/cicids2017/processed/flows.parquet +flow_features_path: /home/chy/JANUS/datasets/cicids2017/processed/flow_features.parquet +flow_features_align: auto + +T: 64 +n_train: 10000 +min_len: 2 +packet_preprocess: mixed_dequant +seed: 42 +data_seed: 42 +train_ratio: 0.8 +benign_label: normal + +d_model: 128 +n_layers: 4 +n_heads: 4 +mlp_ratio: 4.0 +time_dim: 64 +token_dim: + +batch_size: 256 +num_workers: 2 +epochs: 50 +lr: 3.0e-4 +weight_decay: 0.01 +grad_clip: 1.0 +eval_every: 10 +eval_n: 20000 +eval_batch_size: 512 +eval_n_steps: 8 + +sigma: 0.1 +use_ot: true + +device: auto diff --git a/Unified_CFM/configs/cicids2017_consistency.yaml b/Unified_CFM/configs/cicids2017_consistency.yaml new file mode 100644 index 0000000..abdb58f --- /dev/null +++ b/Unified_CFM/configs/cicids2017_consistency.yaml @@ -0,0 +1,43 @@ + +save_dir: /home/chy/JANUS/artifacts/runs/unified_cfm_cicids2017_consistency_2026_04_25 + +packets_npz: /home/chy/JANUS/datasets/cicids2017/processed/packets.npz +flows_parquet: /home/chy/JANUS/datasets/cicids2017/processed/flows.parquet +flow_features_path: /home/chy/JANUS/datasets/cicids2017/processed/flow_features.parquet +flow_features_align: auto + +T: 64 +n_train: 10000 +min_len: 2 +packet_preprocess: mixed_dequant +seed: 42 +data_seed: 42 +train_ratio: 0.8 +benign_label: normal + +d_model: 128 +n_layers: 4 +n_heads: 4 +mlp_ratio: 4.0 +time_dim: 64 +token_dim: + +batch_size: 256 +num_workers: 2 +epochs: 50 +lr: 3.0e-4 +weight_decay: 0.01 +grad_clip: 1.0 +eval_every: 10 +eval_n: 20000 +eval_batch_size: 512 +eval_n_steps: 8 + +sigma: 0.1 +use_ot: true + +lambda_flow: 0.1 +lambda_packet: 0.1 +packet_mask_ratio: 0.5 + +device: auto diff --git a/Unified_CFM/configs/ciciot2023.yaml b/Unified_CFM/configs/ciciot2023.yaml new file mode 100644 index 0000000..b6e6f42 --- /dev/null +++ b/Unified_CFM/configs/ciciot2023.yaml @@ -0,0 +1,43 @@ + +save_dir: /home/chy/JANUS/artifacts/runs/unified_cfm_ciciot2023_2026_04_29 + +source_store: /home/chy/JANUS/datasets/ciciot2023/processed/full_store +flows_parquet: /home/chy/JANUS/datasets/ciciot2023/processed/full_store/flows.parquet +flow_features_path: /home/chy/JANUS/datasets/ciciot2023/processed/flow_features.parquet +flow_features_align: auto + +T: 64 +n_train: 10000 +min_len: 2 +packet_preprocess: mixed_dequant +seed: 42 +data_seed: 42 +train_ratio: 0.8 +benign_label: normal +val_cap: 10000 + +d_model: 128 +n_layers: 4 +n_heads: 4 +mlp_ratio: 4.0 +time_dim: 64 +token_dim: + +batch_size: 256 +num_workers: 0 +epochs: 50 +lr: 3.0e-4 +weight_decay: 0.01 +grad_clip: 1.0 +eval_every: 10 +eval_n: 20000 +eval_batch_size: 512 +eval_n_steps: 8 + +sigma: 0.1 +use_ot: true +lambda_flow: 0.3 +lambda_packet: 0.3 +packet_mask_ratio: 0.5 + +device: auto diff --git a/Unified_CFM/configs/ciciot2023_baseline_seed42.yaml b/Unified_CFM/configs/ciciot2023_baseline_seed42.yaml new file mode 100644 index 0000000..b7d0db0 --- /dev/null +++ b/Unified_CFM/configs/ciciot2023_baseline_seed42.yaml @@ -0,0 +1,45 @@ + +save_dir: /home/chy/JANUS/artifacts/route_comparison/baseline_ciciot2023_seed42 + +source_store: /home/chy/JANUS/datasets/ciciot2023/processed/full_store +flows_parquet: /home/chy/JANUS/datasets/ciciot2023/processed/full_store/flows.parquet +flow_features_path: /home/chy/JANUS/datasets/ciciot2023/processed/flow_features.parquet +flow_features_align: auto + +T: 64 +n_train: 10000 +min_len: 2 +packet_preprocess: mixed_dequant +seed: 42 +data_seed: 42 +train_ratio: 0.8 +benign_label: normal +val_cap: 10000 +attack_cap: 20000 + +d_model: 128 +n_layers: 4 +n_heads: 4 +mlp_ratio: 4.0 +time_dim: 64 +token_dim: +reference_mode: + +batch_size: 256 +num_workers: 0 +epochs: 50 +lr: 3.0e-4 +weight_decay: 0.01 +grad_clip: 1.0 +eval_every: 10 +eval_n: 20000 +eval_batch_size: 512 +eval_n_steps: 8 + +sigma: 0.1 +use_ot: true +lambda_flow: 0.3 +lambda_packet: 0.3 +packet_mask_ratio: 0.5 + +device: auto diff --git a/Unified_CFM/configs/ciciot2023_baseline_seed43.yaml b/Unified_CFM/configs/ciciot2023_baseline_seed43.yaml new file mode 100644 index 0000000..4d286f1 --- /dev/null +++ b/Unified_CFM/configs/ciciot2023_baseline_seed43.yaml @@ -0,0 +1,45 @@ + +save_dir: /home/chy/JANUS/artifacts/route_comparison/baseline_ciciot2023_seed43 + +source_store: /home/chy/JANUS/datasets/ciciot2023/processed/full_store +flows_parquet: /home/chy/JANUS/datasets/ciciot2023/processed/full_store/flows.parquet +flow_features_path: /home/chy/JANUS/datasets/ciciot2023/processed/flow_features.parquet +flow_features_align: auto + +T: 64 +n_train: 10000 +min_len: 2 +packet_preprocess: mixed_dequant +seed: 43 +data_seed: 43 +train_ratio: 0.8 +benign_label: normal +val_cap: 10000 +attack_cap: 20000 + +d_model: 128 +n_layers: 4 +n_heads: 4 +mlp_ratio: 4.0 +time_dim: 64 +token_dim: +reference_mode: + +batch_size: 256 +num_workers: 0 +epochs: 50 +lr: 3.0e-4 +weight_decay: 0.01 +grad_clip: 1.0 +eval_every: 10 +eval_n: 20000 +eval_batch_size: 512 +eval_n_steps: 8 + +sigma: 0.1 +use_ot: true +lambda_flow: 0.3 +lambda_packet: 0.3 +packet_mask_ratio: 0.5 + +device: auto diff --git a/Unified_CFM/configs/ciciot2023_baseline_seed44.yaml b/Unified_CFM/configs/ciciot2023_baseline_seed44.yaml new file mode 100644 index 0000000..c17bde9 --- /dev/null +++ b/Unified_CFM/configs/ciciot2023_baseline_seed44.yaml @@ -0,0 +1,45 @@ + +save_dir: /home/chy/JANUS/artifacts/route_comparison/baseline_ciciot2023_seed44 + +source_store: /home/chy/JANUS/datasets/ciciot2023/processed/full_store +flows_parquet: /home/chy/JANUS/datasets/ciciot2023/processed/full_store/flows.parquet +flow_features_path: /home/chy/JANUS/datasets/ciciot2023/processed/flow_features.parquet +flow_features_align: auto + +T: 64 +n_train: 10000 +min_len: 2 +packet_preprocess: mixed_dequant +seed: 44 +data_seed: 44 +train_ratio: 0.8 +benign_label: normal +val_cap: 10000 +attack_cap: 20000 + +d_model: 128 +n_layers: 4 +n_heads: 4 +mlp_ratio: 4.0 +time_dim: 64 +token_dim: +reference_mode: + +batch_size: 256 +num_workers: 0 +epochs: 50 +lr: 3.0e-4 +weight_decay: 0.01 +grad_clip: 1.0 +eval_every: 10 +eval_n: 20000 +eval_batch_size: 512 +eval_n_steps: 8 + +sigma: 0.1 +use_ot: true +lambda_flow: 0.3 +lambda_packet: 0.3 +packet_mask_ratio: 0.5 + +device: auto diff --git a/Unified_CFM/configs/ciciot2023_route_a_causal.yaml b/Unified_CFM/configs/ciciot2023_route_a_causal.yaml new file mode 100644 index 0000000..6e5cc39 --- /dev/null +++ b/Unified_CFM/configs/ciciot2023_route_a_causal.yaml @@ -0,0 +1,45 @@ + +save_dir: /home/chy/JANUS/artifacts/route_comparison/route_a_causal_ciciot2023_seed42 + +source_store: /home/chy/JANUS/datasets/ciciot2023/processed/full_store +flows_parquet: /home/chy/JANUS/datasets/ciciot2023/processed/full_store/flows.parquet +flow_features_path: /home/chy/JANUS/datasets/ciciot2023/processed/flow_features.parquet +flow_features_align: auto + +T: 64 +n_train: 10000 +min_len: 2 +packet_preprocess: mixed_dequant +seed: 42 +data_seed: 42 +train_ratio: 0.8 +benign_label: normal +val_cap: 10000 +attack_cap: 20000 + +d_model: 128 +n_layers: 4 +n_heads: 4 +mlp_ratio: 4.0 +time_dim: 64 +token_dim: +reference_mode: causal_packets + +batch_size: 256 +num_workers: 0 +epochs: 50 +lr: 3.0e-4 +weight_decay: 0.01 +grad_clip: 1.0 +eval_every: 10 +eval_n: 20000 +eval_batch_size: 512 +eval_n_steps: 8 + +sigma: 0.1 +use_ot: true +lambda_flow: 0.3 +lambda_packet: 0.3 +packet_mask_ratio: 0.5 + +device: auto diff --git a/Unified_CFM/configs/ciciot2023_route_a_causal_seed43.yaml b/Unified_CFM/configs/ciciot2023_route_a_causal_seed43.yaml new file mode 100644 index 0000000..8bcff57 --- /dev/null +++ b/Unified_CFM/configs/ciciot2023_route_a_causal_seed43.yaml @@ -0,0 +1,45 @@ + +save_dir: /home/chy/JANUS/artifacts/route_comparison/route_a_causal_ciciot2023_seed43 + +source_store: /home/chy/JANUS/datasets/ciciot2023/processed/full_store +flows_parquet: /home/chy/JANUS/datasets/ciciot2023/processed/full_store/flows.parquet +flow_features_path: /home/chy/JANUS/datasets/ciciot2023/processed/flow_features.parquet +flow_features_align: auto + +T: 64 +n_train: 10000 +min_len: 2 +packet_preprocess: mixed_dequant +seed: 43 +data_seed: 43 +train_ratio: 0.8 +benign_label: normal +val_cap: 10000 +attack_cap: 20000 + +d_model: 128 +n_layers: 4 +n_heads: 4 +mlp_ratio: 4.0 +time_dim: 64 +token_dim: +reference_mode: causal_packets + +batch_size: 256 +num_workers: 0 +epochs: 50 +lr: 3.0e-4 +weight_decay: 0.01 +grad_clip: 1.0 +eval_every: 10 +eval_n: 20000 +eval_batch_size: 512 +eval_n_steps: 8 + +sigma: 0.1 +use_ot: true +lambda_flow: 0.3 +lambda_packet: 0.3 +packet_mask_ratio: 0.5 + +device: auto diff --git a/Unified_CFM/configs/ciciot2023_route_a_causal_seed44.yaml b/Unified_CFM/configs/ciciot2023_route_a_causal_seed44.yaml new file mode 100644 index 0000000..b25bd58 --- /dev/null +++ b/Unified_CFM/configs/ciciot2023_route_a_causal_seed44.yaml @@ -0,0 +1,45 @@ + +save_dir: /home/chy/JANUS/artifacts/route_comparison/route_a_causal_ciciot2023_seed44 + +source_store: /home/chy/JANUS/datasets/ciciot2023/processed/full_store +flows_parquet: /home/chy/JANUS/datasets/ciciot2023/processed/full_store/flows.parquet +flow_features_path: /home/chy/JANUS/datasets/ciciot2023/processed/flow_features.parquet +flow_features_align: auto + +T: 64 +n_train: 10000 +min_len: 2 +packet_preprocess: mixed_dequant +seed: 44 +data_seed: 44 +train_ratio: 0.8 +benign_label: normal +val_cap: 10000 +attack_cap: 20000 + +d_model: 128 +n_layers: 4 +n_heads: 4 +mlp_ratio: 4.0 +time_dim: 64 +token_dim: +reference_mode: causal_packets + +batch_size: 256 +num_workers: 0 +epochs: 50 +lr: 3.0e-4 +weight_decay: 0.01 +grad_clip: 1.0 +eval_every: 10 +eval_n: 20000 +eval_batch_size: 512 +eval_n_steps: 8 + +sigma: 0.1 +use_ot: true +lambda_flow: 0.3 +lambda_packet: 0.3 +packet_mask_ratio: 0.5 + +device: auto diff --git a/Unified_CFM/configs/ciciot2023_route_b_spectral_seed42.yaml b/Unified_CFM/configs/ciciot2023_route_b_spectral_seed42.yaml new file mode 100644 index 0000000..0895f35 --- /dev/null +++ b/Unified_CFM/configs/ciciot2023_route_b_spectral_seed42.yaml @@ -0,0 +1,44 @@ + +save_dir: /home/chy/JANUS/artifacts/route_comparison/route_b_spectral_ciciot2023_seed42 + +source_store: /home/chy/JANUS/datasets/ciciot2023/processed/full_store +flows_parquet: /home/chy/JANUS/datasets/ciciot2023/processed/full_store/flows.parquet +flow_features_path: /home/chy/JANUS/datasets/ciciot2023/processed/flow_features_spectral.parquet +flow_features_align: auto + +T: 64 +n_train: 10000 +min_len: 2 +packet_preprocess: mixed_dequant +seed: 42 +data_seed: 42 +train_ratio: 0.8 +benign_label: normal +val_cap: 10000 +attack_cap: 20000 + +d_model: 128 +n_layers: 4 +n_heads: 4 +mlp_ratio: 4.0 +time_dim: 64 +token_dim: + +batch_size: 256 +num_workers: 0 +epochs: 50 +lr: 3.0e-4 +weight_decay: 0.01 +grad_clip: 1.0 +eval_every: 10 +eval_n: 20000 +eval_batch_size: 512 +eval_n_steps: 8 + +sigma: 0.1 +use_ot: true +lambda_flow: 0.3 +lambda_packet: 0.3 +packet_mask_ratio: 0.5 + +device: auto diff --git a/Unified_CFM/configs/ciciot2023_route_b_spectral_seed43.yaml b/Unified_CFM/configs/ciciot2023_route_b_spectral_seed43.yaml new file mode 100644 index 0000000..111c5e7 --- /dev/null +++ b/Unified_CFM/configs/ciciot2023_route_b_spectral_seed43.yaml @@ -0,0 +1,44 @@ + +save_dir: /home/chy/JANUS/artifacts/route_comparison/route_b_spectral_ciciot2023_seed43 + +source_store: /home/chy/JANUS/datasets/ciciot2023/processed/full_store +flows_parquet: /home/chy/JANUS/datasets/ciciot2023/processed/full_store/flows.parquet +flow_features_path: /home/chy/JANUS/datasets/ciciot2023/processed/flow_features_spectral.parquet +flow_features_align: auto + +T: 64 +n_train: 10000 +min_len: 2 +packet_preprocess: mixed_dequant +seed: 43 +data_seed: 43 +train_ratio: 0.8 +benign_label: normal +val_cap: 10000 +attack_cap: 20000 + +d_model: 128 +n_layers: 4 +n_heads: 4 +mlp_ratio: 4.0 +time_dim: 64 +token_dim: + +batch_size: 256 +num_workers: 0 +epochs: 50 +lr: 3.0e-4 +weight_decay: 0.01 +grad_clip: 1.0 +eval_every: 10 +eval_n: 20000 +eval_batch_size: 512 +eval_n_steps: 8 + +sigma: 0.1 +use_ot: true +lambda_flow: 0.3 +lambda_packet: 0.3 +packet_mask_ratio: 0.5 + +device: auto diff --git a/Unified_CFM/configs/ciciot2023_route_b_spectral_seed44.yaml b/Unified_CFM/configs/ciciot2023_route_b_spectral_seed44.yaml new file mode 100644 index 0000000..5db7611 --- /dev/null +++ b/Unified_CFM/configs/ciciot2023_route_b_spectral_seed44.yaml @@ -0,0 +1,44 @@ + +save_dir: /home/chy/JANUS/artifacts/route_comparison/route_b_spectral_ciciot2023_seed44 + +source_store: /home/chy/JANUS/datasets/ciciot2023/processed/full_store +flows_parquet: /home/chy/JANUS/datasets/ciciot2023/processed/full_store/flows.parquet +flow_features_path: /home/chy/JANUS/datasets/ciciot2023/processed/flow_features_spectral.parquet +flow_features_align: auto + +T: 64 +n_train: 10000 +min_len: 2 +packet_preprocess: mixed_dequant +seed: 44 +data_seed: 44 +train_ratio: 0.8 +benign_label: normal +val_cap: 10000 +attack_cap: 20000 + +d_model: 128 +n_layers: 4 +n_heads: 4 +mlp_ratio: 4.0 +time_dim: 64 +token_dim: + +batch_size: 256 +num_workers: 0 +epochs: 50 +lr: 3.0e-4 +weight_decay: 0.01 +grad_clip: 1.0 +eval_every: 10 +eval_n: 20000 +eval_batch_size: 512 +eval_n_steps: 8 + +sigma: 0.1 +use_ot: true +lambda_flow: 0.3 +lambda_packet: 0.3 +packet_mask_ratio: 0.5 + +device: auto diff --git a/Unified_CFM/configs/ciciot2023_shafir5.yaml b/Unified_CFM/configs/ciciot2023_shafir5.yaml new file mode 100644 index 0000000..4231db9 --- /dev/null +++ b/Unified_CFM/configs/ciciot2023_shafir5.yaml @@ -0,0 +1,45 @@ + +save_dir: /home/chy/JANUS/artifacts/runs/unified_cfm_ciciot2023_shafir5_2026_04_29 + +source_store: /home/chy/JANUS/datasets/ciciot2023/processed/full_store +flows_parquet: /home/chy/JANUS/datasets/ciciot2023/processed/full_store/flows.parquet +flow_features_path: /home/chy/JANUS/datasets/ciciot2023/processed/flow_features_shafir5.parquet +flow_feature_columns: ["HTTPS", "Protocol_Type", "Magnitude", "Variance", "fin_count"] +flow_features_align: auto + +T: 64 +n_train: 10000 +min_len: 2 +packet_preprocess: mixed_dequant +seed: 42 +data_seed: 42 +train_ratio: 0.8 +benign_label: normal +val_cap: 10000 + +flow_dim: 5 +d_model: 128 +n_layers: 4 +n_heads: 4 +mlp_ratio: 4.0 +time_dim: 64 +token_dim: + +batch_size: 256 +num_workers: 0 +epochs: 50 +lr: 3.0e-4 +weight_decay: 0.01 +grad_clip: 1.0 +eval_every: 10 +eval_n: 20000 +eval_batch_size: 512 +eval_n_steps: 8 + +sigma: 0.1 +use_ot: true +lambda_flow: 0.3 +lambda_packet: 0.3 +packet_mask_ratio: 0.5 + +device: auto diff --git a/Unified_CFM/configs/iscxtor2016.yaml b/Unified_CFM/configs/iscxtor2016.yaml new file mode 100644 index 0000000..f95ba4c --- /dev/null +++ b/Unified_CFM/configs/iscxtor2016.yaml @@ -0,0 +1,39 @@ + +save_dir: /home/chy/JANUS/artifacts/runs/unified_cfm_iscxtor2016_2026_04_25 + +packets_npz: /home/chy/JANUS/datasets/iscxtor2016/processed/packets.npz +flows_parquet: /home/chy/JANUS/datasets/iscxtor2016/processed/flows.parquet +flow_features_path: /home/chy/JANUS/datasets/iscxtor2016/processed/flow_features.parquet +flow_features_align: auto + +T: 64 +n_train: 10000 +min_len: 2 +packet_preprocess: mixed_dequant +seed: 42 +data_seed: 42 +train_ratio: 0.8 +benign_label: nontor + +d_model: 128 +n_layers: 4 +n_heads: 4 +mlp_ratio: 4.0 +time_dim: 64 +token_dim: + +batch_size: 256 +num_workers: 2 +epochs: 50 +lr: 3.0e-4 +weight_decay: 0.01 +grad_clip: 1.0 +eval_every: 10 +eval_n: 20000 +eval_batch_size: 512 +eval_n_steps: 8 + +sigma: 0.1 +use_ot: true + +device: auto diff --git a/Unified_CFM/configs/iscxtor2016_consistency.yaml b/Unified_CFM/configs/iscxtor2016_consistency.yaml new file mode 100644 index 0000000..5134c1d --- /dev/null +++ b/Unified_CFM/configs/iscxtor2016_consistency.yaml @@ -0,0 +1,41 @@ +save_dir: /home/chy/JANUS/artifacts/runs/unified_cfm_iscxtor2016_consistency_2026_04_25 +packets_npz: /home/chy/JANUS/datasets/iscxtor2016/processed/packets.npz +flows_parquet: /home/chy/JANUS/datasets/iscxtor2016/processed/flows.parquet +flow_features_path: /home/chy/JANUS/datasets/iscxtor2016/processed/flow_features.parquet +flow_features_align: auto + +T: 64 +n_train: 10000 +min_len: 2 +packet_preprocess: mixed_dequant +seed: 42 +data_seed: 42 +train_ratio: 0.8 +benign_label: nontor + +d_model: 128 +n_layers: 4 +n_heads: 4 +mlp_ratio: 4.0 +time_dim: 64 +token_dim: + +batch_size: 256 +num_workers: 2 +epochs: 50 +lr: 3.0e-4 +weight_decay: 0.01 +grad_clip: 1.0 +eval_every: 10 +eval_n: 20000 +eval_batch_size: 512 +eval_n_steps: 8 + +sigma: 0.1 +use_ot: true + +lambda_flow: 0.1 +lambda_packet: 0.1 +packet_mask_ratio: 0.5 + +device: auto diff --git a/Unified_CFM/data.py b/Unified_CFM/data.py new file mode 100644 index 0000000..2aab525 --- /dev/null +++ b/Unified_CFM/data.py @@ -0,0 +1,275 @@ +from __future__ import annotations +from dataclasses import dataclass +from pathlib import Path +from typing import Optional +import numpy as np +import pandas as pd +import sys as _sys +from pathlib import Path as _Path +_sys.path.insert(0, str(_Path(__file__).resolve().parents[1])) +from common.data_contract import PACKET_FEATURE_NAMES, PACKET_CONTINUOUS_CHANNEL_IDX as CONTINUOUS_CHANNEL_IDX, PACKET_BINARY_CHANNEL_IDX as BINARY_CHANNEL_IDX, canonical_5tuple as _canonical_key, fit_packet_stats as _fit_packet_stats, zscore as _zscore, apply_mixed_dequant as _apply_mixed_dequant +DEFAULT_FLOW_META_COLUMNS = {'flow_id', 'label', 'day', 'service', 'src_ip', 'dst_ip', 'src_port', 'dst_port', 'protocol', 'timestamp', 'start_ts', 'n_pkts'} +DERIVED_FLOW_FEATURE_NAMES = ('log_len', 'fwd_frac', 'bwd_frac', 'log_size_mean', 'log_size_std', 'log_size_min', 'log_size_max', 'log_dt_mean', 'log_dt_std', 'log_dt_max', 'syn_frac', 'fin_frac', 'rst_frac', 'psh_frac', 'ack_frac', 'log_win_mean') + +@dataclass +class UnifiedData: + train_flow: np.ndarray + val_flow: np.ndarray + attack_flow: np.ndarray + train_packets: np.ndarray + val_packets: np.ndarray + attack_packets: np.ndarray + train_len: np.ndarray + val_len: np.ndarray + attack_len: np.ndarray + attack_labels: np.ndarray + packet_mean: np.ndarray + packet_std: np.ndarray + flow_mean: np.ndarray + flow_std: np.ndarray + packet_preprocess: str + flow_feature_names: tuple[str, ...] + packet_feature_names: tuple[str, ...] = PACKET_FEATURE_NAMES + + @property + def T(self) -> int: + return int(self.train_packets.shape[1]) + + @property + def packet_dim(self) -> int: + return int(self.train_packets.shape[2]) + + @property + def flow_dim(self) -> int: + return int(self.train_flow.shape[1]) + +def _preprocess_packets(train_x: np.ndarray, val_x: np.ndarray, attack_x: np.ndarray, train_l: np.ndarray, val_l: np.ndarray, attack_l: np.ndarray, preprocess: str, seed: int) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray]: + if preprocess not in ('zscore', 'mixed_dequant'): + raise ValueError("packet_preprocess must be 'zscore' or 'mixed_dequant'") + (mean, std) = _fit_packet_stats(train_x, train_l) + + def prep(x: np.ndarray, l: np.ndarray, tag: str) -> np.ndarray: + if preprocess == 'zscore': + z = _zscore(x, mean, std) + mask = np.arange(x.shape[1])[None, :] < l[:, None] + return (z * mask[:, :, None]).astype(np.float32) + return _apply_mixed_dequant(x, l, mean, std, split_tag=tag, seed=seed) + return (prep(train_x, train_l, 'train'), prep(val_x, val_l, 'val'), prep(attack_x, attack_l, 'attack'), mean, std) + +def _derive_flow_features(tokens: np.ndarray, lens: np.ndarray) -> np.ndarray: + (N, T, _) = tokens.shape + out = np.zeros((N, len(DERIVED_FLOW_FEATURE_NAMES)), dtype=np.float32) + for i in range(N): + n = int(max(lens[i], 1)) + x = tokens[i, :n] + direction = x[:, 2] + size = x[:, 0] + dt = x[:, 1] + win = x[:, 8] + out[i, 0] = np.log1p(n) + out[i, 1] = np.mean(direction < 0.5) + out[i, 2] = np.mean(direction >= 0.5) + out[i, 3] = size.mean() + out[i, 4] = size.std() + out[i, 5] = size.min() + out[i, 6] = size.max() + out[i, 7] = dt.mean() + out[i, 8] = dt.std() + out[i, 9] = dt.max() + out[i, 10] = x[:, 3].mean() + out[i, 11] = x[:, 4].mean() + out[i, 12] = x[:, 5].mean() + out[i, 13] = x[:, 6].mean() + out[i, 14] = x[:, 7].mean() + out[i, 15] = win.mean() + return out + +def _read_flow_features(path: Path, *, expected_rows: int, feature_columns: Optional[list[str]]=None) -> tuple[np.ndarray, tuple[str, ...], np.ndarray | None]: + path = Path(path) + if path.suffix == '.npz': + data = np.load(path, allow_pickle=True) + x = data['features'].astype(np.float32) + raw_names = data['feature_names'] if 'feature_names' in data.files else np.arange(x.shape[1]) + names = tuple((str(v) for v in raw_names)) + flow_id = data['flow_id'] if 'flow_id' in data.files else None + elif path.suffix in ('.parquet', '.pq'): + df = pd.read_parquet(path) + flow_id = df['flow_id'].to_numpy() if 'flow_id' in df.columns else None + if feature_columns: + cols = feature_columns + else: + cols = [c for c in df.columns if c not in DEFAULT_FLOW_META_COLUMNS and pd.api.types.is_numeric_dtype(df[c])] + if not cols: + raise ValueError(f'no numeric flow feature columns found in {path}') + x = df[cols].to_numpy(dtype=np.float32) + names = tuple(cols) + else: + raise ValueError(f'unsupported flow feature file: {path}') + if len(x) != expected_rows: + raise ValueError(f'flow feature row count {len(x):,} != packet row count {expected_rows:,}') + x = np.nan_to_num(x, nan=0.0, posinf=0.0, neginf=0.0).astype(np.float32) + return (x, names, flow_id) + +def _feature_columns_from_df(df: pd.DataFrame, requested: Optional[list[str]]) -> list[str]: + if requested: + return requested + return [c for c in df.columns if c not in DEFAULT_FLOW_META_COLUMNS and pd.api.types.is_numeric_dtype(df[c])] + +def _align_flow_features_by_scan(feature_df: pd.DataFrame, packet_flows: pd.DataFrame, *, feature_columns: list[str]) -> tuple[np.ndarray, tuple[str, ...]]: + required = ['label', 'src_ip', 'src_port', 'dst_ip', 'dst_port', 'protocol'] + missing_feature = [c for c in required if c not in feature_df.columns] + missing_packet = [c for c in required if c not in packet_flows.columns] + if missing_feature or missing_packet: + raise ValueError(f'scan alignment requires label + 5-tuple metadata. missing in feature_df={missing_feature}, packet_flows={missing_packet}') + packet_keys = [(str(lbl), _canonical_key(src, sp, dst, dp, proto)) for (lbl, src, sp, dst, dp, proto) in zip(packet_flows['label'].to_numpy(), packet_flows['src_ip'].to_numpy(), packet_flows['src_port'].to_numpy(), packet_flows['dst_ip'].to_numpy(), packet_flows['dst_port'].to_numpy(), packet_flows['protocol'].to_numpy())] + labels = feature_df['label'].to_numpy() + src_ip = feature_df['src_ip'].to_numpy() + src_port = feature_df['src_port'].to_numpy() + dst_ip = feature_df['dst_ip'].to_numpy() + dst_port = feature_df['dst_port'].to_numpy() + protocol = feature_df['protocol'].to_numpy() + matched: list[int] = [] + j = 0 + n_csv = len(feature_df) + for (i, target) in enumerate(packet_keys): + while j < n_csv: + cand = (str(labels[j]), _canonical_key(src_ip[j], src_port[j], dst_ip[j], dst_port[j], protocol[j])) + j += 1 + if cand == target: + matched.append(j - 1) + break + else: + raise ValueError(f'failed to align packet flow row {i:,}/{len(packet_keys):,}; the CSV cache may not be the same one used for packet extraction') + print(f'[data] scan-aligned CSV flow features: matched={len(matched):,} from csv_rows={n_csv:,} skipped={matched[-1] + 1 - len(matched):,}') + x = feature_df.iloc[matched][feature_columns].to_numpy(dtype=np.float32) + x = np.nan_to_num(x, nan=0.0, posinf=0.0, neginf=0.0).astype(np.float32) + return (x, tuple(feature_columns)) + +def _read_aligned_flow_features(path: Path, packet_flows: pd.DataFrame, *, feature_columns: Optional[list[str]]=None, align: str='auto') -> tuple[np.ndarray, tuple[str, ...]]: + path = Path(path) + if align not in ('auto', 'row', 'scan'): + raise ValueError("flow_features_align must be 'auto', 'row', or 'scan'") + if path.suffix == '.npz': + (x, names, flow_id) = _read_flow_features(path, expected_rows=len(packet_flows), feature_columns=feature_columns) + packet_id = packet_flows['flow_id'].to_numpy() if 'flow_id' in packet_flows else None + if flow_id is not None and packet_id is not None and (not np.array_equal(flow_id, packet_id)): + raise ValueError('NPZ flow_id does not align with Packet_CFM flows') + return (x, names) + if path.suffix not in ('.parquet', '.pq'): + raise ValueError(f'unsupported flow feature file: {path}') + feature_df = pd.read_parquet(path) + cols = _feature_columns_from_df(feature_df, feature_columns) + if not cols: + raise ValueError(f'no numeric flow feature columns found in {path}') + packet_id = packet_flows['flow_id'].to_numpy() if 'flow_id' in packet_flows else None + if len(feature_df) == len(packet_flows): + feature_id = feature_df['flow_id'].to_numpy() if 'flow_id' in feature_df.columns else None + if feature_id is None or packet_id is None or np.array_equal(feature_id, packet_id): + x = feature_df[cols].to_numpy(dtype=np.float32) + x = np.nan_to_num(x, nan=0.0, posinf=0.0, neginf=0.0).astype(np.float32) + return (x, tuple(cols)) + if align == 'row': + raise ValueError("flow_id mismatch with flow_features_align='row'") + if align == 'row': + raise ValueError(f'row alignment requested but feature rows={len(feature_df):,} packet rows={len(packet_flows):,}') + return _align_flow_features_by_scan(feature_df, packet_flows, feature_columns=cols) + +def _preprocess_flow(train: np.ndarray, val: np.ndarray, attack: np.ndarray) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray]: + mean = train.mean(axis=0).astype(np.float32) + std = train.std(axis=0).astype(np.float32) + return (_zscore(train, mean, std), _zscore(val, mean, std), _zscore(attack, mean, std), mean, std) + +def load_unified_data(*, packets_npz: Path | None=None, source_store: Path | None=None, flows_parquet: Path, flow_features_path: Path | None=None, flow_feature_columns: Optional[list[str]]=None, flow_features_align: str='auto', T: int=128, split_seed: int=42, train_ratio: float=0.8, benign_label: str='normal', min_len: int=2, packet_preprocess: str='mixed_dequant', attack_cap: int | None=None, val_cap: int | None=None) -> UnifiedData: + if (packets_npz is None) == (source_store is None): + raise ValueError('pass exactly one of packets_npz or source_store') + flows_parquet = Path(flows_parquet) + print(f'[data] flows={flows_parquet} packets_source={(packets_npz if packets_npz else source_store)}') + flow_cols = ['flow_id', 'label'] + if flow_features_path is not None: + flow_cols += ['src_ip', 'src_port', 'dst_ip', 'dst_port', 'protocol'] + flows = pd.read_parquet(flows_parquet, columns=flow_cols) + labels_full = flows['label'].to_numpy().astype(str) + flow_id = flows['flow_id'].to_numpy() + tokens_full: np.ndarray | None = None + store = None + if packets_npz is not None: + pz = np.load(Path(packets_npz)) + tokens_full = pz['packet_tokens'].astype(np.float32) + lens_full = pz['packet_lengths'].astype(np.int32) + packet_flow_id = pz['flow_id'] if 'flow_id' in pz.files else None + if T > tokens_full.shape[1]: + raise ValueError(f'requested T={T} > stored T_full={tokens_full.shape[1]}') + tokens_full = tokens_full[:, :T].copy() + lens_full = np.minimum(lens_full, T).astype(np.int32) + if packet_flow_id is not None and (not np.array_equal(packet_flow_id, flow_id)): + raise ValueError('packets_npz and flows_parquet are not row-aligned by flow_id') + else: + if flow_features_path is None: + raise ValueError('source_store path requires flow_features_path (derived features need tokens in memory)') + from common.packet_store import PacketShardStore + store = PacketShardStore.open(Path(source_store)) + store_flow_id = store.read_flows(columns=['flow_id'])['flow_id'].to_numpy() + if not np.array_equal(store_flow_id, flow_id): + raise ValueError('source_store and flows_parquet are not row-aligned by flow_id') + lens_full = np.minimum(store.manifest['packet_length'].to_numpy(dtype=np.int32), T) + if flow_features_path is None: + assert tokens_full is not None + flow_features = _derive_flow_features(tokens_full, lens_full) + flow_names = DERIVED_FLOW_FEATURE_NAMES + print(f'[data] using derived flow features D={flow_features.shape[1]}') + else: + (flow_features, flow_names) = _read_aligned_flow_features(Path(flow_features_path), flows, feature_columns=flow_feature_columns, align=flow_features_align) + print(f'[data] using external flow features D={flow_features.shape[1]}') + keep = lens_full >= min_len + labels = labels_full[keep] + flow_features = flow_features[keep] + lens = lens_full[keep] + global_idx = np.flatnonzero(keep).astype(np.int64) + if tokens_full is not None: + materialized_tokens = tokens_full[keep] + else: + materialized_tokens = None + print(f'[data] rows total={len(keep):,} keep len>={min_len}: {keep.sum():,}') + benign_local = np.where(labels == benign_label)[0] + attack_local = np.where(labels != benign_label)[0] + rng = np.random.default_rng(split_seed) + rng.shuffle(benign_local) + n_train = int(len(benign_local) * train_ratio) + train_local = benign_local[:n_train] + val_local = benign_local[n_train:] + if val_cap is not None and len(val_local) > val_cap: + val_local = np.sort(rng.choice(val_local, size=val_cap, replace=False)) + if attack_cap is not None and len(attack_local) > attack_cap: + attack_local = np.sort(rng.choice(attack_local, size=attack_cap, replace=False)) + print(f'[data] benign={len(benign_local):,} attack={len(attack_local):,} -> train={len(train_local):,} val={len(val_local):,}') + + def _materialize(local_indices: np.ndarray) -> np.ndarray: + if materialized_tokens is not None: + return materialized_tokens[local_indices].astype(np.float32, copy=False) + assert store is not None + g = global_idx[local_indices] + (tok, _) = store.read_packets(g.astype(np.int64), T=T) + return tok.astype(np.float32, copy=False) + tr_p_raw = _materialize(train_local) + va_p_raw = _materialize(val_local) + at_p_raw = _materialize(attack_local) + tr_l = lens[train_local] + va_l = lens[val_local] + at_l = lens[attack_local] + tr_f_raw = flow_features[train_local] + va_f_raw = flow_features[val_local] + at_f_raw = flow_features[attack_local] + train_idx = train_local + val_idx = val_local + attack_idx = attack_local + (tr_p, va_p, at_p, p_mean, p_std) = _preprocess_packets(tr_p_raw, va_p_raw, at_p_raw, tr_l, va_l, at_l, preprocess=packet_preprocess, seed=split_seed) + (tr_f, va_f, at_f, f_mean, f_std) = _preprocess_flow(tr_f_raw, va_f_raw, at_f_raw) + return UnifiedData(train_flow=tr_f, val_flow=va_f, attack_flow=at_f, train_packets=tr_p, val_packets=va_p, attack_packets=at_p, train_len=tr_l, val_len=va_l, attack_len=at_l, attack_labels=labels[attack_idx], packet_mean=p_mean, packet_std=p_std, flow_mean=f_mean, flow_std=f_std, packet_preprocess=packet_preprocess, flow_feature_names=tuple(flow_names)) + +def subsample_train(data: UnifiedData, n_train: int, seed: int) -> tuple[np.ndarray, np.ndarray, np.ndarray]: + if n_train <= 0 or n_train >= len(data.train_flow): + return (data.train_flow, data.train_packets, data.train_len) + rng = np.random.default_rng(seed) + idx = rng.choice(len(data.train_flow), n_train, replace=False) + idx.sort() + return (data.train_flow[idx], data.train_packets[idx], data.train_len[idx]) diff --git a/Unified_CFM/model.py b/Unified_CFM/model.py new file mode 100644 index 0000000..da3700e --- /dev/null +++ b/Unified_CFM/model.py @@ -0,0 +1,588 @@ +from __future__ import annotations +import math +from dataclasses import dataclass +import torch +import torch.nn as nn +from torchdiffeq import odeint + +@torch.no_grad() +def _sinkhorn_coupling(C: torch.Tensor, reg: float=0.05, n_iter: int=20) -> torch.Tensor: + C = C.float() + log_k = -C / reg + B = C.shape[0] + log_u = torch.zeros(B, device=C.device) + log_v = torch.zeros(B, device=C.device) + for _ in range(n_iter): + log_v = -torch.logsumexp(log_k + log_u.unsqueeze(1), dim=0) + log_u = -torch.logsumexp(log_k + log_v.unsqueeze(0), dim=1) + log_p = log_u.unsqueeze(1) + log_k + log_v.unsqueeze(0) + return log_p.argmax(dim=1) + +class SinusoidalTimeEmb(nn.Module): + + def __init__(self, dim: int) -> None: + super().__init__() + if dim % 2 != 0: + raise ValueError('time embedding dimension must be even') + self.dim = dim + + def forward(self, t: torch.Tensor) -> torch.Tensor: + half = self.dim // 2 + freqs = torch.exp(-math.log(10000) * torch.arange(half, device=t.device, dtype=t.dtype) / max(half - 1, 1)) + args = t[:, None] * freqs[None, :] + return torch.cat([args.sin(), args.cos()], dim=-1) + +class AdaLNBlock(nn.Module): + + def __init__(self, d_model: int, n_heads: int, mlp_ratio: float, cond_dim: int) -> None: + super().__init__() + self.norm1 = nn.LayerNorm(d_model, elementwise_affine=False) + self.attn = nn.MultiheadAttention(d_model, n_heads, batch_first=True) + self.norm2 = nn.LayerNorm(d_model, elementwise_affine=False) + hidden = int(d_model * mlp_ratio) + self.mlp = nn.Sequential(nn.Linear(d_model, hidden), nn.GELU(), nn.Linear(hidden, d_model)) + self.cond_proj = nn.Linear(cond_dim, 6 * d_model) + nn.init.zeros_(self.cond_proj.weight) + nn.init.zeros_(self.cond_proj.bias) + + @staticmethod + def _modulate(x: torch.Tensor, gamma: torch.Tensor, beta: torch.Tensor) -> torch.Tensor: + return x * (1.0 + gamma[:, None, :]) + beta[:, None, :] + + def forward(self, x: torch.Tensor, cond: torch.Tensor, key_padding_mask: torch.Tensor | None, attn_mask: torch.Tensor | None=None) -> torch.Tensor: + (g1, b1, a1, g2, b2, a2) = self.cond_proj(cond).chunk(6, dim=-1) + h = self._modulate(self.norm1(x), g1, b1) + (attn_out, _) = self.attn(h, h, h, key_padding_mask=key_padding_mask, attn_mask=attn_mask, need_weights=False) + x = x + a1[:, None, :] * attn_out + h = self._modulate(self.norm2(x), g2, b2) + return x + a2[:, None, :] * self.mlp(h) + +class UnifiedVelocity(nn.Module): + + def __init__(self, token_dim: int, seq_len: int, d_model: int=128, n_layers: int=4, n_heads: int=4, mlp_ratio: float=4.0, time_dim: int=64, reference_mode: str | None=None) -> None: + super().__init__() + if reference_mode not in (None, 'independent_token', 'block_diagonal', 'causal_packets', 'causal_all'): + raise ValueError(f'unknown reference_mode={reference_mode!r}') + self.token_dim = token_dim + self.seq_len = seq_len + self.reference_mode = reference_mode + self.input_proj = nn.Linear(token_dim, d_model) + self.pos_emb = nn.Parameter(torch.zeros(1, seq_len, d_model)) + self.type_emb = nn.Embedding(2, d_model) + nn.init.trunc_normal_(self.pos_emb, std=0.02) + nn.init.normal_(self.type_emb.weight, std=0.02) + self.time_emb = SinusoidalTimeEmb(time_dim) + self.cond_mlp = nn.Sequential(nn.Linear(time_dim, d_model), nn.SiLU(), nn.Linear(d_model, d_model)) + self.blocks = nn.ModuleList([AdaLNBlock(d_model, n_heads, mlp_ratio, cond_dim=d_model) for _ in range(n_layers)]) + self.out_norm = nn.LayerNorm(d_model, elementwise_affine=False) + self.out = nn.Linear(d_model, token_dim) + nn.init.zeros_(self.out.weight) + nn.init.zeros_(self.out.bias) + type_ids = torch.ones(seq_len, dtype=torch.long) + type_ids[0] = 0 + self.register_buffer('type_ids', type_ids, persistent=False) + + def forward(self, x: torch.Tensor, t: torch.Tensor, key_padding_mask: torch.Tensor | None=None, attn_mask_override: torch.Tensor | None=None) -> torch.Tensor: + (B, L, _) = x.shape + if L > self.seq_len: + raise ValueError(f'sequence length {L} exceeds configured {self.seq_len}') + if t.dim() == 0: + t = t.expand(B) + h = self.input_proj(x) + h = h + self.pos_emb[:, :L, :] + h = h + self.type_emb(self.type_ids[:L])[None, :, :] + cond = self.cond_mlp(self.time_emb(t)) + if attn_mask_override is not None: + attn_mask = attn_mask_override + else: + attn_mask = self._reference_attn_mask(L, x.device) + for block in self.blocks: + h = block(h, cond, key_padding_mask, attn_mask=attn_mask) + return self.out(self.out_norm(h)) + + def _reference_attn_mask(self, L: int, device: torch.device) -> torch.Tensor | None: + if self.reference_mode is None: + return None + if self.reference_mode == 'independent_token': + return ~torch.eye(L, dtype=torch.bool, device=device) + if self.reference_mode == 'block_diagonal': + mask = torch.ones((L, L), dtype=torch.bool, device=device) + mask[0, 0] = False + if L > 1: + mask[1:, 1:] = False + return mask + if self.reference_mode == 'causal_packets': + mask = torch.zeros((L, L), dtype=torch.bool, device=device) + if L > 1: + packet_causal = torch.triu(torch.ones(L - 1, L - 1, dtype=torch.bool, device=device), diagonal=1) + mask[1:, 1:] = packet_causal + return mask + if self.reference_mode == 'causal_all': + return torch.triu(torch.ones(L, L, dtype=torch.bool, device=device), diagonal=1) + raise AssertionError(self.reference_mode) + +@dataclass +class UnifiedCFMConfig: + T: int = 128 + packet_dim: int = 9 + flow_dim: int = 16 + token_dim: int | None = None + d_model: int = 128 + n_layers: int = 4 + n_heads: int = 4 + mlp_ratio: float = 4.0 + time_dim: int = 64 + sigma: float = 0.1 + use_ot: bool = False + reference_mode: str | None = None + +class UnifiedTokenCFM(nn.Module): + + def __init__(self, cfg: UnifiedCFMConfig) -> None: + super().__init__() + self.cfg = cfg + self.token_dim = cfg.token_dim or 1 + max(cfg.flow_dim, cfg.packet_dim) + if self.token_dim < 1 + max(cfg.flow_dim, cfg.packet_dim): + raise ValueError('token_dim is too small for flow_dim/packet_dim') + self.seq_len = cfg.T + 1 + self.velocity = UnifiedVelocity(token_dim=self.token_dim, seq_len=self.seq_len, d_model=cfg.d_model, n_layers=cfg.n_layers, n_heads=cfg.n_heads, mlp_ratio=cfg.mlp_ratio, time_dim=cfg.time_dim, reference_mode=cfg.reference_mode) + + def build_tokens(self, flow: torch.Tensor, packets: torch.Tensor) -> torch.Tensor: + (B, T, Dp) = packets.shape + if T != self.cfg.T: + raise ValueError(f'packet T={T} but config T={self.cfg.T}') + if Dp != self.cfg.packet_dim: + raise ValueError(f'packet_dim={Dp} but config packet_dim={self.cfg.packet_dim}') + if flow.shape[-1] != self.cfg.flow_dim: + raise ValueError(f'flow_dim={flow.shape[-1]} but config flow_dim={self.cfg.flow_dim}') + z = packets.new_zeros((B, T + 1, self.token_dim)) + z[:, 0, 0] = -1.0 + z[:, 0, 1:1 + self.cfg.flow_dim] = flow + z[:, 1:, 0] = 1.0 + z[:, 1:, 1:1 + self.cfg.packet_dim] = packets + return z + + def key_padding_mask(self, lens: torch.Tensor) -> torch.Tensor: + B = lens.shape[0] + idx = torch.arange(self.cfg.T, device=lens.device)[None, :] + packet_real = idx < lens[:, None] + real = torch.cat([torch.ones(B, 1, dtype=torch.bool, device=lens.device), packet_real], dim=1) + return ~real + + def _loss_mask(self, lens: torch.Tensor) -> torch.Tensor: + return (~self.key_padding_mask(lens)).float() + + @staticmethod + def _masked_trimmed_mean(values: torch.Tensor, mask: torch.Tensor, trim_frac: float=0.1) -> torch.Tensor: + out = values.new_zeros(values.shape[0]) + for i in range(values.shape[0]): + v = values[i][mask[i] > 0] + if v.numel() == 0: + continue + if v.numel() < 5: + out[i] = v.mean() + continue + v_sorted = torch.sort(v).values + lo = int(trim_frac * v_sorted.numel()) + hi = int((1.0 - trim_frac) * v_sorted.numel()) + if hi <= lo: + out[i] = v_sorted.mean() + else: + out[i] = v_sorted[lo:hi].mean() + return out + + @staticmethod + def _masked_median(values: torch.Tensor, mask: torch.Tensor) -> torch.Tensor: + out = values.new_zeros(values.shape[0]) + for i in range(values.shape[0]): + v = values[i][mask[i] > 0] + if v.numel() == 0: + continue + v_sorted = torch.sort(v).values + mid = v_sorted.numel() // 2 + if v_sorted.numel() % 2: + out[i] = v_sorted[mid] + else: + out[i] = 0.5 * (v_sorted[mid - 1] + v_sorted[mid]) + return out + + def compute_loss(self, flow: torch.Tensor, packets: torch.Tensor, lens: torch.Tensor, *, lambda_flow: float=0.0, lambda_packet: float=0.0, packet_mask_ratio: float=0.5, return_components: bool=False) -> torch.Tensor | dict[str, torch.Tensor]: + x1 = self.build_tokens(flow, packets) + B = x1.shape[0] + x0 = torch.randn_like(x1) + mask = self._loss_mask(lens) + kpm = mask == 0 + if self.cfg.use_ot: + flat0 = (x0 * mask[:, :, None]).reshape(B, -1) + flat1 = (x1 * mask[:, :, None]).reshape(B, -1) + col = _sinkhorn_coupling(torch.cdist(flat0.float(), flat1.float())) + x1 = x1[col] + flow = flow[col] + packets = packets[col] + lens = lens[col] + mask = self._loss_mask(lens) + kpm = mask == 0 + t = torch.rand(B, device=x1.device) + x_t = (1.0 - t[:, None, None]) * x0 + t[:, None, None] * x1 + if self.cfg.sigma > 0: + std = self.cfg.sigma * torch.sqrt(t * (1.0 - t))[:, None, None] + x_t = x_t + std * torch.randn_like(x_t) + target = x1 - x0 + pred = self.velocity(x_t, t, key_padding_mask=kpm) + sq = (pred - target).square().mean(dim=-1) + per_sample = (sq * mask).sum(dim=-1) / mask.sum(dim=-1).clamp_min(1.0) + main_loss = per_sample.mean() + aux_flow_loss = x1.new_zeros(()) + aux_packet_loss = x1.new_zeros(()) + if lambda_flow > 0.0: + x_t_mf = x_t.clone() + x_t_mf[:, 0, :] = 0.0 + pred_mf = self.velocity(x_t_mf, t, key_padding_mask=kpm) + err = (pred_mf[:, 0] - target[:, 0]).square().mean(dim=-1) + aux_flow_loss = err.mean() + if lambda_packet > 0.0: + packet_real = mask[:, 1:] > 0 + rand_draw = torch.rand(packet_real.shape, device=x1.device) + mask_pkt = (rand_draw < packet_mask_ratio) & packet_real + pkt_mask_full = torch.cat([torch.zeros(B, 1, dtype=torch.bool, device=x1.device), mask_pkt], dim=1) + x_t_mp = x_t.clone() + x_t_mp[pkt_mask_full] = 0.0 + pred_mp = self.velocity(x_t_mp, t, key_padding_mask=kpm) + sq_mp = (pred_mp - target).square().mean(dim=-1) + mask_f = pkt_mask_full.float() + denom = mask_f.sum(dim=-1).clamp_min(1.0) + aux_packet_loss = ((sq_mp * mask_f).sum(dim=-1) / denom).mean() + total = main_loss + lambda_flow * aux_flow_loss + lambda_packet * aux_packet_loss + if return_components: + return {'total': total, 'main': main_loss.detach(), 'aux_flow': aux_flow_loss.detach(), 'aux_packet': aux_packet_loss.detach()} + return total + + @torch.no_grad() + def velocity_score(self, flow: torch.Tensor, packets: torch.Tensor, lens: torch.Tensor, t_eval: tuple[float, ...]=(0.5, 0.75, 1.0)) -> dict[str, torch.Tensor]: + x = self.build_tokens(flow, packets) + mask = self._loss_mask(lens) + kpm = mask == 0 + total = torch.zeros(x.shape[0], device=x.device) + flow_s = torch.zeros_like(total) + packet_s = torch.zeros_like(total) + packet_count = mask[:, 1:].sum(dim=-1).clamp_min(1.0) + for t_val in t_eval: + t = torch.full((x.shape[0],), float(t_val), device=x.device) + v = self.velocity(x, t, key_padding_mask=kpm) + e = v.square().mean(dim=-1) + total = total + (e * mask).sum(dim=-1) / mask.sum(dim=-1).clamp_min(1.0) + flow_s = flow_s + e[:, 0] + packet_s = packet_s + (e[:, 1:] * mask[:, 1:]).sum(dim=-1) / packet_count + denom = float(len(t_eval)) + return {'velocity_total': total / denom, 'velocity_flow': flow_s / denom, 'velocity_packet': packet_s / denom} + + @torch.no_grad() + def trajectory_metrics(self, flow: torch.Tensor, packets: torch.Tensor, lens: torch.Tensor, n_steps: int=16) -> dict[str, torch.Tensor]: + z = self.build_tokens(flow, packets) + mask = self._loss_mask(lens) + kpm = mask == 0 + B = z.shape[0] + dt = 1.0 / n_steps + total_arc = torch.zeros(B, device=z.device) + total_ke = torch.zeros(B, device=z.device) + flow_ke = torch.zeros(B, device=z.device) + packet_ke = torch.zeros(B, device=z.device) + total_curv = torch.zeros(B, device=z.device) + flow_curv = torch.zeros(B, device=z.device) + packet_curv = torch.zeros(B, device=z.device) + packet_kappa2_speed2 = torch.zeros(B, max(z.shape[1] - 1, 0), device=z.device) + packet_count = mask[:, 1:].sum(dim=-1).clamp_min(1.0) + v_prev = None + v_prev_norm = None + for k in range(n_steps): + t_val = 1.0 - k * dt + t = torch.full((B,), t_val, device=z.device) + v = self.velocity(z, t, key_padding_mask=kpm) + e = v.square().mean(dim=-1) + v_norm = v.square().sum(dim=-1).clamp_min(1e-12).sqrt() + total_ke = total_ke + (e * mask).sum(dim=-1) / mask.sum(dim=-1).clamp_min(1.0) * dt + flow_ke = flow_ke + e[:, 0] * dt + packet_ke = packet_ke + (e[:, 1:] * mask[:, 1:]).sum(dim=-1) / packet_count * dt + if v_prev is not None: + dv = v - v_prev + dve = dv.square().mean(dim=-1) + total_curv = total_curv + (dve * mask).sum(dim=-1) / mask.sum(dim=-1).clamp_min(1.0) + flow_curv = flow_curv + dve[:, 0] + packet_curv = packet_curv + (dve[:, 1:] * mask[:, 1:]).sum(dim=-1) / packet_count + dv2_sum = dv[:, 1:].square().sum(dim=-1) + assert v_prev_norm is not None + v_avg = 0.5 * (v_norm[:, 1:] + v_prev_norm[:, 1:]) + packet_kappa2_speed2 = packet_kappa2_speed2 + dv2_sum / v_avg.square().clamp_min(1e-06) + v_prev = v + v_prev_norm = v_norm + z_new = z - v * dt + dz = (z_new - z) * mask[:, :, None] + total_arc = total_arc + dz.reshape(B, -1).norm(dim=-1) / mask.sum(dim=-1).sqrt() + z = z_new + z_masked = z * mask[:, :, None] + terminal = z_masked.reshape(B, -1).norm(dim=-1) / (mask.sum(dim=-1) * self.token_dim).clamp_min(1.0).sqrt() + terminal_flow = z[:, 0].norm(dim=-1) / math.sqrt(self.token_dim) + terminal_packet = (z[:, 1:] * mask[:, 1:, None]).reshape(B, -1).norm(dim=-1) / (packet_count * self.token_dim).sqrt() + packet_mask = mask[:, 1:] + kappa2_speed2_mean = (packet_kappa2_speed2 * packet_mask).sum(dim=-1) / packet_count + kappa2_speed2_median = self._masked_median(packet_kappa2_speed2, packet_mask) + kappa2_speed2_trimmed = self._masked_trimmed_mean(packet_kappa2_speed2, packet_mask) + return {'terminal_norm': terminal, 'terminal_flow': terminal_flow, 'terminal_packet': terminal_packet, 'arc_length': total_arc, 'kinetic_energy': total_ke, 'kinetic_flow': flow_ke, 'kinetic_packet': packet_ke, 'curvature_total': total_curv, 'curvature_flow': flow_curv, 'curvature_packet': packet_curv, 'kappa2_speed2norm_packet_mean': kappa2_speed2_mean, 'kappa2_speed2norm_packet_median': kappa2_speed2_median, 'kappa2_speed2norm_packet_trimmed10_mean': kappa2_speed2_trimmed} + + @torch.no_grad() + def score_profile_vt(self, flow: torch.Tensor, packets: torch.Tensor, lens: torch.Tensor, t_eval: tuple[float, ...]=(0.1, 0.3, 0.5, 0.7, 0.9, 1.0)) -> dict[str, torch.Tensor]: + x = self.build_tokens(flow, packets) + mask = self._loss_mask(lens) + kpm = mask == 0 + packet_count = mask[:, 1:].sum(dim=-1).clamp_min(1.0) + out: dict[str, torch.Tensor] = {} + for t_val in t_eval: + t = torch.full((x.shape[0],), float(t_val), device=x.device) + v = self.velocity(x, t, key_padding_mask=kpm) + e = v.square().mean(dim=-1) + tag = f't{int(round(t_val * 10)):02d}' + out[f'velocity_total_{tag}'] = (e * mask).sum(dim=-1) / mask.sum(dim=-1).clamp_min(1.0) + out[f'velocity_flow_{tag}'] = e[:, 0] + out[f'velocity_packet_{tag}'] = (e[:, 1:] * mask[:, 1:]).sum(dim=-1) / packet_count + return out + + @torch.no_grad() + def consistency_score(self, flow: torch.Tensor, packets: torch.Tensor, lens: torch.Tensor, t_eval: float=0.5) -> dict[str, torch.Tensor]: + x = self.build_tokens(flow, packets) + mask = self._loss_mask(lens) + kpm = mask == 0 + B = x.shape[0] + packet_count = mask[:, 1:].sum(dim=-1).clamp_min(1.0) + t = torch.full((B,), float(t_eval), device=x.device) + v_full = self.velocity(x, t, key_padding_mask=kpm) + x_mf = x.clone() + x_mf[:, 0, :] = 0.0 + v_mf = self.velocity(x_mf, t, key_padding_mask=kpm) + flow_cons = (v_full[:, 0] - v_mf[:, 0]).square().mean(dim=-1) + x_mp = x.clone() + pkt_mask_full = mask[:, 1:] > 0 + idx_pkt_mask = torch.cat([torch.zeros(B, 1, dtype=torch.bool, device=x.device), pkt_mask_full], dim=1) + x_mp[idx_pkt_mask] = 0.0 + v_mp = self.velocity(x_mp, t, key_padding_mask=kpm) + diff = (v_full - v_mp).square().mean(dim=-1) + packet_cons = (diff[:, 1:] * mask[:, 1:]).sum(dim=-1) / packet_count + return {'flow_consistency': flow_cons, 'packet_consistency': packet_cons, 'consistency_total': flow_cons + packet_cons} + + def jacobian_hutchinson(self, flow: torch.Tensor, packets: torch.Tensor, lens: torch.Tensor, t_eval: tuple[float, ...]=(0.5,), n_eps: int=4, generator: torch.Generator | None=None) -> dict[str, torch.Tensor]: + x = self.build_tokens(flow, packets) + mask = self._loss_mask(lens) + kpm = mask == 0 + B = x.shape[0] + packet_count = mask[:, 1:].sum(dim=-1).clamp_min(1.0) + total = torch.zeros(B, device=x.device) + flow_j = torch.zeros(B, device=x.device) + packet_j = torch.zeros(B, device=x.device) + n_draws = n_eps * len(t_eval) + for t_val in t_eval: + t_current = torch.full((B,), float(t_val), device=x.device) + for _ in range(n_eps): + x_req = x.detach().clone().requires_grad_(True) + v = self.velocity(x_req, t_current, key_padding_mask=kpm) + eps = torch.randn(v.shape, device=v.device, generator=generator) + (g,) = torch.autograd.grad(outputs=v, inputs=x_req, grad_outputs=eps, retain_graph=False, create_graph=False) + e = g.square().mean(dim=-1) + total = total + (e * mask).sum(dim=-1) / mask.sum(dim=-1).clamp_min(1.0) + flow_j = flow_j + e[:, 0] + packet_j = packet_j + (e[:, 1:] * mask[:, 1:]).sum(dim=-1) / packet_count + return {'jacobian_total': (total / n_draws).detach(), 'jacobian_flow': (flow_j / n_draws).detach(), 'jacobian_packet': (packet_j / n_draws).detach()} + + @torch.no_grad() + def pna_score(self, flow: torch.Tensor, packets: torch.Tensor, lens: torch.Tensor, n_steps: int=16, flow_masked: bool=False) -> dict[str, torch.Tensor]: + eps_v2 = 1e-06 + dt = 1.0 / n_steps + z = self.build_tokens(flow, packets) + if flow_masked: + z = z.clone() + z[:, 0, :] = 0.0 + mask = self._loss_mask(lens) + kpm = mask == 0 + (B, L, _) = z.shape + pna = torch.zeros(B, L, device=z.device) + v_prev: torch.Tensor | None = None + v_norm_prev: torch.Tensor | None = None + for k in range(n_steps): + t_val = 1.0 - k * dt + t = torch.full((B,), t_val, device=z.device) + v = self.velocity(z, t, key_padding_mask=kpm) + v_norm = (v.square().sum(dim=-1) + 1e-12).sqrt() + if v_prev is not None: + dv2 = (v - v_prev).square().sum(dim=-1) + v_avg2 = (0.5 * (v_norm + v_norm_prev)).square().clamp_min(eps_v2) + pna = pna + dv2 / v_avg2 + v_prev = v + v_norm_prev = v_norm + z = z - v * dt + if flow_masked: + z[:, 0, :] = 0.0 + flow_pna = pna[:, 0] + packet_pna = pna[:, 1:] + packet_mask = mask[:, 1:] + packet_count = packet_mask.sum(dim=-1).clamp_min(1.0) + pna_median = self._masked_median(packet_pna, packet_mask) + pna_mean = (packet_pna * packet_mask).sum(dim=-1) / packet_count + masked_for_max = packet_pna.masked_fill(packet_mask == 0, float('-inf')) + pna_max = masked_for_max.max(dim=-1).values + pna_trimmed = self._masked_trimmed_mean(packet_pna, packet_mask) + return {'pna_packet_median': pna_median, 'pna_packet_mean': pna_mean, 'pna_packet_max': pna_max, 'pna_packet_trimmed10_mean': pna_trimmed, 'pna_flow': flow_pna} + + @torch.no_grad() + def causal_consistency_score(self, flow: torch.Tensor, packets: torch.Tensor, lens: torch.Tensor, t_eval: float=0.5) -> dict[str, torch.Tensor]: + x = self.build_tokens(flow, packets) + mask = self._loss_mask(lens) + kpm = mask == 0 + (B, L, _) = x.shape + t = torch.full((B,), float(t_eval), device=x.device) + v_full = self.velocity(x, t, key_padding_mask=kpm) + causal = torch.triu(torch.ones(L, L, dtype=torch.bool, device=x.device), diagonal=1) + v_causal = self.velocity(x, t, key_padding_mask=kpm, attn_mask_override=causal) + diff = (v_full - v_causal).square().mean(dim=-1) + flow_surprisal = diff[:, 0] + packet_diff = diff[:, 1:] + packet_mask = mask[:, 1:] + packet_count = packet_mask.sum(dim=-1).clamp_min(1.0) + packet_mean = (packet_diff * packet_mask).sum(dim=-1) / packet_count + packet_median = self._masked_median(packet_diff, packet_mask) + masked_for_max = packet_diff.masked_fill(packet_mask == 0, float('-inf')) + packet_max = masked_for_max.max(dim=-1).values + packet_trimmed = self._masked_trimmed_mean(packet_diff, packet_mask) + total = (diff * mask).sum(dim=-1) / mask.sum(dim=-1).clamp_min(1.0) + return {'causal_surprisal_total': total, 'causal_surprisal_flow': flow_surprisal, 'causal_surprisal_packet_mean': packet_mean, 'causal_surprisal_packet_median': packet_median, 'causal_surprisal_packet_max': packet_max, 'causal_surprisal_packet_trimmed10_mean': packet_trimmed} + + @torch.no_grad() + def direction_consistency_score(self, flow: torch.Tensor, packets: torch.Tensor, lens: torch.Tensor, t_eval: tuple[float, ...]=(0.2, 0.4, 0.6, 0.8, 1.0)) -> dict[str, torch.Tensor]: + x = self.build_tokens(flow, packets) + mask = self._loss_mask(lens) + kpm = mask == 0 + (B, L, _) = x.shape + t_eval = tuple(t_eval) + if len(t_eval) < 2: + raise ValueError('direction_consistency_score needs >=2 t values') + prev_v: torch.Tensor | None = None + drift = x.new_zeros(B, L) + n_pairs = len(t_eval) - 1 + for t_val in t_eval: + t = torch.full((B,), float(t_val), device=x.device) + v = self.velocity(x, t, key_padding_mask=kpm) + if prev_v is not None: + num = (prev_v * v).sum(dim=-1) + denom = prev_v.norm(dim=-1).clamp_min(1e-08) * v.norm(dim=-1).clamp_min(1e-08) + cos = num / denom + drift = drift + (1.0 - cos) + prev_v = v + drift = drift / max(n_pairs, 1) + flow_drift = drift[:, 0] + packet_drift = drift[:, 1:] + packet_mask = mask[:, 1:] + packet_count = packet_mask.sum(dim=-1).clamp_min(1.0) + packet_mean = (packet_drift * packet_mask).sum(dim=-1) / packet_count + packet_median = self._masked_median(packet_drift, packet_mask) + masked_for_max = packet_drift.masked_fill(packet_mask == 0, float('-inf')) + packet_max = masked_for_max.max(dim=-1).values + packet_trimmed = self._masked_trimmed_mean(packet_drift, packet_mask) + total = (drift * mask).sum(dim=-1) / mask.sum(dim=-1).clamp_min(1.0) + return {'direction_drift_total': total, 'direction_drift_flow': flow_drift, 'direction_drift_packet_mean': packet_mean, 'direction_drift_packet_median': packet_median, 'direction_drift_packet_max': packet_max, 'direction_drift_packet_trimmed10_mean': packet_trimmed} + + def inverse_flow_nll_score(self, flow: torch.Tensor, packets: torch.Tensor, lens: torch.Tensor, n_steps: int=16, n_eps: int=4, compute_divergence: bool=True, generator: torch.Generator | None=None) -> dict[str, torch.Tensor]: + z = self.build_tokens(flow, packets) + mask = self._loss_mask(lens) + kpm = mask == 0 + (B, L, D) = z.shape + dt = 1.0 / n_steps + accum_div = torch.zeros(B, device=z.device) + if compute_divergence: + for k in range(n_steps): + t_val = 1.0 - k * dt + t = torch.full((B,), t_val, device=z.device) + z_req = z.detach().clone().requires_grad_(True) + v = self.velocity(z_req, t, key_padding_mask=kpm) + div_step = torch.zeros(B, device=z.device) + for j in range(n_eps): + eps = torch.randn_like(v) + eps_masked = eps * mask[:, :, None] + retain = j < n_eps - 1 + (g,) = torch.autograd.grad(outputs=v, inputs=z_req, grad_outputs=eps_masked, retain_graph=retain, create_graph=False) + div_step = div_step + (eps_masked * g).sum(dim=(1, 2)) + div_step = div_step / float(n_eps) + accum_div = accum_div + div_step * dt + with torch.no_grad(): + z = (z_req - v * dt).detach() + else: + with torch.no_grad(): + for k in range(n_steps): + t_val = 1.0 - k * dt + t = torch.full((B,), t_val, device=z.device) + v = self.velocity(z, t, key_padding_mask=kpm) + z = z - v * dt + with torch.no_grad(): + z_masked = z * mask[:, :, None] + n_real = mask.sum(dim=-1).clamp_min(1.0) + x0_quadratic = z_masked.reshape(B, -1).square().sum(dim=-1) / (n_real * float(D)) + nll_x0_only = x0_quadratic + nll_div_only = accum_div / (n_real * float(D)) + nll_full = nll_x0_only + nll_div_only + return {'nll_x0_only': nll_x0_only.detach(), 'nll_div_only': nll_div_only.detach(), 'nll_full': nll_full.detach()} + + def jacobian_spectral_score(self, flow: torch.Tensor, packets: torch.Tensor, lens: torch.Tensor, t_eval: float=0.5, n_eps: int=4, generator: torch.Generator | None=None) -> dict[str, torch.Tensor]: + x = self.build_tokens(flow, packets) + mask = self._loss_mask(lens) + kpm = mask == 0 + (B, L, D) = x.shape + t = torch.full((B,), float(t_eval), device=x.device) + packet_mask = mask[:, 1:] + packet_count = packet_mask.sum(dim=-1).clamp_min(1.0) + norms_total: list[torch.Tensor] = [] + norms_flow: list[torch.Tensor] = [] + norms_packet: list[torch.Tensor] = [] + for _ in range(n_eps): + x_req = x.detach().clone().requires_grad_(True) + v = self.velocity(x_req, t, key_padding_mask=kpm) + eps = torch.randn(v.shape, device=v.device, generator=generator) + (g,) = torch.autograd.grad(outputs=v, inputs=x_req, grad_outputs=eps, retain_graph=False, create_graph=False) + e = g.square().mean(dim=-1) + n_total = (e * mask).sum(dim=-1) / mask.sum(dim=-1).clamp_min(1.0) + n_flow = e[:, 0] + n_packet = (e[:, 1:] * packet_mask).sum(dim=-1) / packet_count + norms_total.append(n_total.detach()) + norms_flow.append(n_flow.detach()) + norms_packet.append(n_packet.detach()) + + def _spectral_summary(samples: list[torch.Tensor]) -> dict[str, torch.Tensor]: + stack = torch.stack(samples, dim=1) + mean = stack.mean(dim=1).clamp_min(1e-12) + mx = stack.max(dim=1).values + mn = stack.min(dim=1).values + logfro = torch.log(mean) + aniso = mx / mean + min_over_max = mn / mx.clamp_min(1e-12) + p = stack / stack.sum(dim=1, keepdim=True).clamp_min(1e-12) + entropy = -(p * p.clamp_min(1e-12).log()).sum(dim=1) + eff_rank = torch.exp(entropy) + return {'logfro': logfro, 'anisotropy': aniso, 'min_over_max': min_over_max, 'eff_rank': eff_rank} + out: dict[str, torch.Tensor] = {} + for (tag, samples) in (('total', norms_total), ('flow', norms_flow), ('packet', norms_packet)): + summ = _spectral_summary(samples) + for (stat_name, val) in summ.items(): + out[f'jac_{stat_name}_{tag}'] = val + return out + + @torch.no_grad() + def sample(self, n: int, lens: torch.Tensor, device: torch.device, n_steps: int=50, method: str='euler') -> torch.Tensor: + z = torch.randn(n, self.seq_len, self.token_dim, device=device) + ts = torch.linspace(0.0, 1.0, n_steps + 1, device=device) + kpm = self.key_padding_mask(lens.to(device)) + + def f(t: torch.Tensor, x: torch.Tensor) -> torch.Tensor: + return self.velocity(x, t.expand(x.shape[0]), key_padding_mask=kpm) + if method == 'euler': + for i in range(n_steps): + z = z + f(ts[i], z) * (ts[i + 1] - ts[i]) + return z + return odeint(f, z, ts, method=method)[-1] + + def param_count(self) -> int: + return sum((p.numel() for p in self.parameters())) diff --git a/Unified_CFM/tests/test_model_shapes.py b/Unified_CFM/tests/test_model_shapes.py new file mode 100644 index 0000000..3d2924f --- /dev/null +++ b/Unified_CFM/tests/test_model_shapes.py @@ -0,0 +1,157 @@ +import sys +from pathlib import Path +import torch +sys.path.insert(0, str(Path(__file__).resolve().parents[1])) +from model import UnifiedCFMConfig, UnifiedTokenCFM + +def _build_model(): + return UnifiedTokenCFM(UnifiedCFMConfig(T=4, packet_dim=3, flow_dim=5, d_model=16, n_layers=1, n_heads=4, time_dim=8)) + +def _build_reference_model(reference_mode: str): + return UnifiedTokenCFM(UnifiedCFMConfig(T=4, packet_dim=3, flow_dim=5, d_model=16, n_layers=1, n_heads=4, time_dim=8, reference_mode=reference_mode)) + +def _sample_batch(seed: int=0): + torch.manual_seed(seed) + flow = torch.randn(2, 5) + packets = torch.randn(2, 4, 3) + lens = torch.tensor([4, 2]) + return (flow, packets, lens) + +def test_unified_cfm_shapes_and_scores(): + model = _build_model() + (flow, packets, lens) = _sample_batch() + tokens = model.build_tokens(flow, packets) + assert tokens.shape == (2, 5, 6) + loss = model.compute_loss(flow, packets, lens) + assert loss.ndim == 0 + assert torch.isfinite(loss) + traj = model.trajectory_metrics(flow, packets, lens, n_steps=2) + assert 'terminal_norm' in traj + assert traj['terminal_norm'].shape == (2,) + vel = model.velocity_score(flow, packets, lens) + assert set(vel) == {'velocity_total', 'velocity_flow', 'velocity_packet'} + +def test_reference_mode_independent_token_shapes_and_scores(): + model = _build_reference_model('independent_token') + (flow, packets, lens) = _sample_batch(seed=9) + loss = model.compute_loss(flow, packets, lens) + assert loss.ndim == 0 + assert torch.isfinite(loss) + traj = model.trajectory_metrics(flow, packets, lens, n_steps=2) + assert traj['terminal_norm'].shape == (2,) + assert torch.all(torch.isfinite(traj['curvature_packet'])) + +def test_reference_mode_block_diagonal_shapes_and_scores(): + model = _build_reference_model('block_diagonal') + (flow, packets, lens) = _sample_batch(seed=10) + loss = model.compute_loss(flow, packets, lens) + assert loss.ndim == 0 + assert torch.isfinite(loss) + vel = model.velocity_score(flow, packets, lens) + assert set(vel) == {'velocity_total', 'velocity_flow', 'velocity_packet'} + +def test_trajectory_curvature_keys_and_shapes(): + model = _build_model() + (flow, packets, lens) = _sample_batch(seed=1) + traj = model.trajectory_metrics(flow, packets, lens, n_steps=4) + for key in ('curvature_total', 'curvature_flow', 'curvature_packet'): + assert key in traj, f'missing {key}' + assert traj[key].shape == (2,) + assert torch.all(torch.isfinite(traj[key])) + assert torch.all(traj[key] >= 0) + +def test_trajectory_curvature_zero_with_one_step(): + model = _build_model() + (flow, packets, lens) = _sample_batch(seed=2) + traj = model.trajectory_metrics(flow, packets, lens, n_steps=1) + for key in ('curvature_total', 'curvature_flow', 'curvature_packet'): + assert traj[key].abs().sum().item() == 0.0 + +def test_speed_normalized_packet_curvature_scores(): + model = _build_model() + (flow, packets, lens) = _sample_batch(seed=11) + traj = model.trajectory_metrics(flow, packets, lens, n_steps=4) + keys = ('kappa2_speed2norm_packet_mean', 'kappa2_speed2norm_packet_median', 'kappa2_speed2norm_packet_trimmed10_mean') + for key in keys: + assert key in traj, f'missing {key}' + assert traj[key].shape == (2,) + assert torch.all(torch.isfinite(traj[key])) + assert torch.all(traj[key] >= 0) + one_step = model.trajectory_metrics(flow, packets, lens, n_steps=1) + for key in keys: + assert one_step[key].abs().sum().item() == 0.0 + +def test_score_profile_vt_shapes(): + model = _build_model() + (flow, packets, lens) = _sample_batch(seed=3) + t_eval = (0.1, 0.3, 0.5, 0.7, 0.9, 1.0) + prof = model.score_profile_vt(flow, packets, lens, t_eval=t_eval) + assert len(prof) == 3 * len(t_eval) + for (k, v) in prof.items(): + assert v.shape == (2,), k + assert torch.all(torch.isfinite(v)) + assert torch.all(v >= 0) + assert 'velocity_total_t05' in prof + assert 'velocity_flow_t10' in prof + assert 'velocity_packet_t01' in prof + +def test_compute_loss_backward_compat(): + model = _build_model() + (flow, packets, lens) = _sample_batch(seed=5) + torch.manual_seed(0) + a = model.compute_loss(flow, packets, lens) + torch.manual_seed(0) + b = model.compute_loss(flow, packets, lens, lambda_flow=0.0, lambda_packet=0.0) + assert torch.allclose(a, b), f'λ=0 must match old loss; got {a.item()} vs {b.item()}' + +def test_compute_loss_aux_components_finite(): + model = _build_model() + (flow, packets, lens) = _sample_batch(seed=6) + torch.manual_seed(7) + comp = model.compute_loss(flow, packets, lens, lambda_flow=0.1, lambda_packet=0.1, return_components=True) + assert set(comp) == {'total', 'main', 'aux_flow', 'aux_packet'} + for (k, v) in comp.items(): + assert torch.isfinite(v), k + assert v >= 0, f'{k} negative: {v.item()}' + +def test_compute_loss_aux_affects_gradient(): + model = _build_model() + with torch.no_grad(): + model.velocity.out.weight.normal_(std=0.01) + for block in model.velocity.blocks: + block.cond_proj.weight.normal_(std=0.01) + (flow, packets, lens) = _sample_batch(seed=8) + torch.manual_seed(10) + total = model.compute_loss(flow, packets, lens, lambda_flow=1.0, lambda_packet=1.0) + total.backward() + some_grad = False + for p in model.parameters(): + if p.grad is not None and p.grad.abs().sum().item() > 0: + some_grad = True + break + assert some_grad, 'no gradient flowed through aux losses' + +def test_consistency_score_shapes(): + model = _build_model() + (flow, packets, lens) = _sample_batch(seed=9) + cs = model.consistency_score(flow, packets, lens) + assert set(cs) == {'flow_consistency', 'packet_consistency', 'consistency_total'} + for (k, v) in cs.items(): + assert v.shape == (2,), k + assert torch.all(torch.isfinite(v)) + assert torch.all(v >= 0), k + +def test_jacobian_hutchinson_shapes_and_nonneg(): + model = _build_model() + with torch.no_grad(): + model.velocity.out.weight.normal_(std=0.01) + for block in model.velocity.blocks: + block.cond_proj.weight.normal_(std=0.01) + (flow, packets, lens) = _sample_batch(seed=4) + gen = torch.Generator().manual_seed(42) + jac = model.jacobian_hutchinson(flow, packets, lens, t_eval=(0.5,), n_eps=2, generator=gen) + assert set(jac) == {'jacobian_total', 'jacobian_flow', 'jacobian_packet'} + for (k, v) in jac.items(): + assert v.shape == (2,), k + assert torch.all(torch.isfinite(v)) + assert torch.all(v >= 0), f'{k} has negative value' diff --git a/Unified_CFM/train.py b/Unified_CFM/train.py new file mode 100644 index 0000000..a98060a --- /dev/null +++ b/Unified_CFM/train.py @@ -0,0 +1,147 @@ +from __future__ import annotations +import argparse +import json +import time +from dataclasses import asdict +from pathlib import Path +from typing import Any +import numpy as np +import torch +import yaml +from sklearn.metrics import roc_auc_score +from torch.utils.data import DataLoader, TensorDataset +from data import UnifiedData, load_unified_data, subsample_train +from model import UnifiedCFMConfig, UnifiedTokenCFM + +def _device(dev_arg: str) -> torch.device: + if dev_arg == 'auto': + return torch.device('cuda' if torch.cuda.is_available() else 'cpu') + return torch.device(dev_arg) + +def _batch_score(model: UnifiedTokenCFM, flow_np: np.ndarray, packet_np: np.ndarray, len_np: np.ndarray, device: torch.device, *, batch_size: int, n_steps: int) -> dict[str, np.ndarray]: + out: dict[str, list[np.ndarray]] = {} + model.eval() + for start in range(0, len(flow_np), batch_size): + sl = slice(start, start + batch_size) + flow = torch.from_numpy(flow_np[sl]).float().to(device) + packets = torch.from_numpy(packet_np[sl]).float().to(device) + lens = torch.from_numpy(len_np[sl]).long().to(device) + metrics = model.trajectory_metrics(flow, packets, lens, n_steps=n_steps) + vel = model.velocity_score(flow, packets, lens) + metrics.update(vel) + for (k, v) in metrics.items(): + out.setdefault(k, []).append(v.detach().cpu().numpy()) + return {k: np.concatenate(v, axis=0) for (k, v) in out.items()} + +def _quick_eval(model: UnifiedTokenCFM, data: UnifiedData, device: torch.device, cfg: dict[str, Any]) -> dict[str, float]: + n_eval = int(cfg.get('eval_n', 2000)) + rng = np.random.default_rng(0) + + def pick(n: int) -> np.ndarray: + m = min(n_eval, n) + return rng.choice(n, m, replace=False) + vi = pick(len(data.val_flow)) + ai = pick(len(data.attack_flow)) + v = _batch_score(model, data.val_flow[vi], data.val_packets[vi], data.val_len[vi], device, batch_size=int(cfg.get('eval_batch_size', 512)), n_steps=int(cfg.get('eval_n_steps', 8))) + a = _batch_score(model, data.attack_flow[ai], data.attack_packets[ai], data.attack_len[ai], device, batch_size=int(cfg.get('eval_batch_size', 512)), n_steps=int(cfg.get('eval_n_steps', 8))) + y = np.concatenate([np.zeros(len(vi)), np.ones(len(ai))]) + result: dict[str, float] = {} + for key in sorted(v.keys()): + s = np.concatenate([v[key], a[key]]) + s = np.nan_to_num(s, nan=0.0, posinf=1000000000000.0, neginf=-1000000000000.0) + result[f'auroc_{key}'] = float(roc_auc_score(y, s)) + return result + +def train(cfg: dict[str, Any]) -> Path: + device = _device(str(cfg.get('device', 'auto'))) + save_dir = Path(cfg['save_dir']) + save_dir.mkdir(parents=True, exist_ok=True) + with open(save_dir / 'config.yaml', 'w') as f: + yaml.safe_dump(cfg, f) + seed = int(cfg.get('seed', 42)) + data_seed = int(cfg.get('data_seed', seed)) + torch.manual_seed(seed) + np.random.seed(seed) + print(f'Device: {device}') + print(f'[seed] model={seed} data={data_seed}') + feature_columns = cfg.get('flow_feature_columns') + data = load_unified_data(packets_npz=Path(cfg['packets_npz']) if cfg.get('packets_npz') else None, source_store=Path(cfg['source_store']) if cfg.get('source_store') else None, flows_parquet=Path(cfg['flows_parquet']), flow_features_path=Path(cfg['flow_features_path']) if cfg.get('flow_features_path') else None, flow_feature_columns=feature_columns, flow_features_align=str(cfg.get('flow_features_align', 'auto')), T=int(cfg['T']), split_seed=data_seed, train_ratio=float(cfg.get('train_ratio', 0.8)), benign_label=str(cfg.get('benign_label', 'normal')), min_len=int(cfg.get('min_len', 2)), packet_preprocess=str(cfg.get('packet_preprocess', 'mixed_dequant')), attack_cap=int(cfg['attack_cap']) if cfg.get('attack_cap') else None, val_cap=int(cfg['val_cap']) if cfg.get('val_cap') else None) + print(f'[data] T={data.T} packet_D={data.packet_dim} flow_D={data.flow_dim} train={len(data.train_flow):,} val={len(data.val_flow):,} attack={len(data.attack_flow):,}') + (tr_f, tr_p, tr_l) = subsample_train(data, int(cfg.get('n_train', 0)), data_seed) + ds = TensorDataset(torch.from_numpy(tr_f).float(), torch.from_numpy(tr_p).float(), torch.from_numpy(tr_l).long()) + loader = DataLoader(ds, batch_size=int(cfg['batch_size']), shuffle=True, drop_last=True, num_workers=int(cfg.get('num_workers', 0)), pin_memory=device.type == 'cuda') + print(f'[data] using {len(ds):,} benign training flows') + model_cfg = UnifiedCFMConfig(T=data.T, packet_dim=data.packet_dim, flow_dim=data.flow_dim, token_dim=cfg.get('token_dim'), d_model=int(cfg['d_model']), n_layers=int(cfg['n_layers']), n_heads=int(cfg['n_heads']), mlp_ratio=float(cfg.get('mlp_ratio', 4.0)), time_dim=int(cfg.get('time_dim', 64)), sigma=float(cfg.get('sigma', 0.1)), use_ot=bool(cfg.get('use_ot', False)), reference_mode=cfg.get('reference_mode')) + model = UnifiedTokenCFM(model_cfg).to(device) + print(f'[model] params={model.param_count():,} token_dim={model.token_dim} seq_len={model.seq_len} sigma={model_cfg.sigma} use_ot={model_cfg.use_ot} reference_mode={model_cfg.reference_mode}') + opt = torch.optim.AdamW(model.parameters(), lr=float(cfg['lr']), weight_decay=float(cfg.get('weight_decay', 0.01))) + total_steps = max(1, int(cfg['epochs']) * len(loader)) + sched = torch.optim.lr_scheduler.CosineAnnealingLR(opt, T_max=total_steps) + history: dict[str, list[Any]] = {'epoch': [], 'loss': [], 'eval': []} + lambda_flow = float(cfg.get('lambda_flow', 0.0)) + lambda_packet = float(cfg.get('lambda_packet', 0.0)) + packet_mask_ratio = float(cfg.get('packet_mask_ratio', 0.5)) + aux_enabled = lambda_flow > 0.0 or lambda_packet > 0.0 + if aux_enabled: + print(f'[loss] λ_flow={lambda_flow} λ_packet={lambda_packet} packet_mask_ratio={packet_mask_ratio}') + for epoch in range(1, int(cfg['epochs']) + 1): + model.train() + losses: list[float] = [] + aux_flow_sum = 0.0 + aux_packet_sum = 0.0 + n_steps_this_epoch = 0 + t0 = time.time() + for (flow, packets, lens) in loader: + flow = flow.to(device, non_blocking=True) + packets = packets.to(device, non_blocking=True) + lens = lens.to(device, non_blocking=True) + if aux_enabled: + comp = model.compute_loss(flow, packets, lens, lambda_flow=lambda_flow, lambda_packet=lambda_packet, packet_mask_ratio=packet_mask_ratio, return_components=True) + loss = comp['total'] + aux_flow_sum += float(comp['aux_flow'].item()) + aux_packet_sum += float(comp['aux_packet'].item()) + else: + loss = model.compute_loss(flow, packets, lens) + opt.zero_grad(set_to_none=True) + loss.backward() + torch.nn.utils.clip_grad_norm_(model.parameters(), float(cfg.get('grad_clip', 1.0))) + opt.step() + sched.step() + losses.append(float(loss.item())) + n_steps_this_epoch += 1 + mean_loss = float(np.mean(losses)) if losses else float('nan') + eval_metrics: dict[str, float] | None = None + if epoch % int(cfg.get('eval_every', 5)) == 0 or epoch == int(cfg['epochs']): + eval_metrics = _quick_eval(model, data, device, cfg) + history['epoch'].append(epoch) + history['loss'].append(mean_loss) + history['eval'].append(eval_metrics) + elapsed = time.time() - t0 + terminal = '' + if eval_metrics: + terminal = f" auroc_terminal={eval_metrics['auroc_terminal_norm']:.3f}" + if aux_enabled and n_steps_this_epoch: + terminal += f' aux_flow={aux_flow_sum / n_steps_this_epoch:.4f} aux_pkt={aux_packet_sum / n_steps_this_epoch:.4f}' + print(f"[epoch {epoch:>3d}/{cfg['epochs']:<3d}] ({elapsed:.1f}s) loss={mean_loss:.4f}{terminal}") + if not np.isfinite(mean_loss): + raise RuntimeError(f'non-finite loss at epoch {epoch}') + payload = {'model_state_dict': model.state_dict(), 'model_cfg': asdict(model_cfg), 'packet_mean': data.packet_mean, 'packet_std': data.packet_std, 'flow_mean': data.flow_mean, 'flow_std': data.flow_std, 'packet_preprocess': data.packet_preprocess, 'flow_feature_names': np.asarray(data.flow_feature_names), 'packet_feature_names': np.asarray(data.packet_feature_names)} + torch.save(payload, save_dir / 'model.pt') + with open(save_dir / 'history.json', 'w') as f: + json.dump(history, f, indent=2, default=str) + print(f"[saved] {save_dir / 'model.pt'}") + return save_dir + +def main() -> None: + p = argparse.ArgumentParser(description=__doc__) + p.add_argument('--config', type=Path, required=True) + p.add_argument('--override', type=str, nargs='*', default=[]) + args = p.parse_args() + with open(args.config) as f: + cfg = yaml.safe_load(f) + for override in args.override: + (key, value) = override.split('=', 1) + cfg[key] = yaml.safe_load(value) + train(cfg) +if __name__ == '__main__': + main() diff --git a/artifacts/baselines/COMPARISON_TABLE.md b/artifacts/baselines/COMPARISON_TABLE.md new file mode 100644 index 0000000..769e8aa --- /dev/null +++ b/artifacts/baselines/COMPARISON_TABLE.md @@ -0,0 +1,103 @@ +# Unified_CFM vs Baselines — Performance Comparison Table + +Live tracking. Last updated: 2026-04-30. + +Two reproduction modes are tracked separately: + +- **Method reproduction** (≡ "ours-method-only"): we use the baseline's + model/training algorithm but feed it our 20-d packet-derived canonical flow + features (or 9-d packet sequences for AT/Kitsune). Tests whether the + baseline's architecture beats Unified_CFM on the **same data substrate**. + +- **True reproduction** (≡ "ours-true-repro"): we use the baseline's full + pipeline — including their feature engineering (CICFlowMeter CSV / + AfterImage / image encoding). Tests whether their published numbers + reproduce on our datasets. + +"paper" = the number quoted in the source paper; not run by us. + +## Headline AUROC table + +Two columns per baseline: +- "ours-method-only" = baseline model on our 20-d / 9-d features, 3-seed mean±std. +- "ours-true-repro" = baseline's full pipeline (CICFlowMeter CSV + their feature subsets), single seed for now. + +Raw AUROC reported. abs() = sign-agnostic max(AUROC, 1−AUROC) for inverted-signal baselines. + +| Protocol | **Unified_CFM** | Shafir NF paper | Shafir NF method-only (ours) | **Shafir NF true-repro (ours)** | Kitsune paper | Kitsune Path B method-only (ours) | AT method-only (ours) | +|---|---:|---:|---:|---:|---:|---:|---:| +| ISCXTor2016 within | **0.9945 ± 0.0011** | 0.8731 | 0.9422 ± 0.0075 | **0.7562** [^4] | 0.7800 | 0.5653 ± 0.0226 | 0.4122 ± 0.0503 (abs 0.59) | +| CICIDS2017 within (σ=0.6) | **0.9858 ± 0.0021** | 0.9303 | 0.9256 ± 0.0188 | **0.8678** [^4] | 0.8500 | 0.7023 ± 0.0310 | 0.5009 ± 0.2107 (abs 0.66) | +| CICDDoS2019 within | **0.9960 ± 0.0010** | 0.9300 | 0.8903 ± 0.0386 | 0.5926 [^5] | — | 0.4710 ± 0.0039 | 0.4777 ± 0.3325 (abs 0.75) | +| IDS2017→DDoS2019 forward | 0.9109 ± 0.0032 | 0.8900 | **0.9210 ± 0.0111** | 0.7831 | — | 0.4905 ± 0.0751 | 0.5404 ± 0.1495 (abs 0.63) | +| DDoS2019→IDS2017 reverse | 0.5999 (single) [^1] | **0.9300** | 0.7247 ± 0.0035 | 0.7473 | — | 0.7483 ± 0.0137 | 0.4767 ± 0.2597 (abs 0.70) | +| CICIoT2023 within (single seed) | **0.9618** [^2] | F1=0.9951 [^3] | 0.8996 [^2] | 0.7398 [^4] | — | — | — | + +**Bold** = best per row. + +[^1]: Reverse `terminal_norm` 0.5999 single-seed. Our **PNA** score on this + protocol is 0.9089 (3-seed mean) — beats both Shafir NF reproduced (0.7247) + and Shafir paper (0.93). + +[^2]: CICIoT2023 single seed. Our 20-d canonical features. Shafir NF (ours) + used 5-d Shafir-selected features (HTTPS, Protocol_Type, Magnitude, + Variance, fin_count) computed from our packets. + +[^3]: Shafir paper Table VIII reports F1=0.9951 (not AUROC) on CICIoT2023. + They used the official CICFlowMeter pipeline on full pcap; not directly + AUROC-comparable. Our F1@P95 on this protocol = **0.9463** (with our 20-d + canonical features). + +[^4]: Shafir NF true-reproduction uses their **paper-specified SHAP-selected + feature subsets**: ISCXTor 4 features (Flow IAT Std, Flow Bytes/s, + Flow Packets/s, Bwd IAT Max — paper §V-A), CICIDS2017 5 features (Bwd + Packet Length Mean, Fwd Packets/s, ACK Flag Count, Total Length of Bwd + Packets, Flow Duration — paper §V-B), CICIoT2023 5 features (HTTPS, + Protocol Type, Magnitude, Variance, fin_count — paper §V-D). Driver + uses **single NF**; paper headline numbers (0.8731 / 0.9303 / 0.93) come + from a **2-NF ensemble** which we don't reproduce. Expected single-NF + underperformance vs ensemble: ~0.05-0.15 AUROC. + +[^5]: CICDDoS2019 true-repro uses CICIDS2017 best-5 feature subset (paper + §V-C: "comparable feature subset shared with CICIDS2017"). Result is + weak (0.59) — those 5 features are tuned for CICIDS attacks, not DrDoS. + Paper's 0.93 number is presumably with CICDDoS-specific feature + selection (not specified in paper). + +## CICIoT2023 thresholded F1 table + +| Method | flow features | F1@P95 | F1@P99 | TPR@1%FPR | +|---|---|---:|---:|---:| +| **Unified_CFM 20-d canonical** | 20-d packet-derived | **0.9463** | **0.9004** | **0.8208** | +| Unified_CFM Shafir-5 | 5-d Shafir SHAP-selected | 0.9266 | 0.8704 | 0.7726 | +| Shafir NF (ours), Shafir-5 | 5-d Shafir SHAP-selected | 0.9053 | 0.0652 | — | +| Shafir NF (paper) | 5-d via official CICFlowMeter | **0.9951** | — | — | + +## Summary by within / cross direction + +**Within-dataset (4 protocols)**: Unified_CFM beats Shafir NF reproduction on **4/4** (ISCXTor +0.052, CICIDS +0.060, CICDDoS +0.106, CICIoT2023 +0.062). +**Forward cross (1 protocol)**: Shafir NF (ours) 0.921 narrowly edges Unified_CFM 0.911 (+0.010 — within noise). +**Reverse cross (1 protocol)**: Shafir NF (ours) 0.725 beats Unified_CFM `terminal_norm` 0.600, but Unified_CFM `PNA` 0.909 beats both. Reverse score-of-record for Unified_CFM is PNA, not terminal_norm. + +## Sources + +- Unified_CFM main: `RESULTS.md`, `RESULTS_THRESHOLDED.md` +- Unified_CFM CICIoT2023: `artifacts/runs/unified_cfm_ciciot2023_2026_04_29/{phase1/phase1_summary.json, phase1/thresholded.json}` +- Unified_CFM PNA on reverse: `artifacts/phase_new_scores_2026_04_29/pna/reverse_cross_seed{42,43,44}.json` +- Shafir NF (ours): `artifacts/baselines/shafir_nf_2026_04_29/summary.md` +- Kitsune (ours): `artifacts/baselines/kitsune_2026_04_29/summary.md` +- Shafir paper baselines: `artifacts/locked_baselines.md` + +## Reproduction-mode coverage table + +For each baseline, what's the achievable reproduction mode and current status: + +| Baseline | Method-only (ours-features) | True-repro (their-features) | Comment | +|---|---|---|---| +| **Shafir NF** | ✅ 15 cells | ✅ 6 cells (single seed, CSV+SHAP-5) | True-repro single-NF; paper headline uses 2-NF ensemble | +| **Kitsune** | ✅ 15 cells (Path B, KitNET on 9-d) | ❌ blocked (Path A) | Path A pcap streaming starts at 1471 pkt/s, slows to 581 pkt/s as AfterImage state grows. CICIDS2017 alone needs 7-9 hours; CICDDoS2019 / CICIoT2023 prohibitive. Single-pcap test on ISCXTor showed only 22 unique 5-tuples per pcap → flow coverage gaps. Documented as infeasible at our data scale. | +| **Anomaly-Transformer** | ✅ 15 cells | n/a | AT has no domain-specific feature pipeline (raw time-series only); method-only IS the faithful reproduction here | +| **ConMD** | not started | not started | Repo is just a 878-line model class — would require writing entire distillation training loop + image-like preprocessing from scratch. ~2 days work, no guaranteed paper-number reproduction. | +| **9 image-AD methods** (FastFlow, PatchCore, PADIM, STFPM, DFKDE, DFM, DRAEM, CFlow, RD4AD via `anomalib`) | not started | not started | Requires flow→image encoder which Shafir didn't open-source. Any reproduction would be "our flow→image + their image-AD model" → method-only at best. ~2 days work. | +| **TSLANet** | n/a | n/a | Vendored repo at the noted commit has no anomaly detection module. Skipped. | +| **ganomaly / RD4AD / STFPM standalone** | not started | not started | Would be subsumed by anomalib path; standalone repos have no native traffic-data support. | diff --git a/artifacts/baselines/anomaly_transformer_2026_04_29/cicddos_within_seed42.json b/artifacts/baselines/anomaly_transformer_2026_04_29/cicddos_within_seed42.json new file mode 100644 index 0000000..4bed656 --- /dev/null +++ b/artifacts/baselines/anomaly_transformer_2026_04_29/cicddos_within_seed42.json @@ -0,0 +1,320 @@ +{ + "method": "anomaly_transformer", + "protocol": "cicddos_within", + "seed": 42, + "model_dir": "/home/chy/mambafortrafficmodeling/artifacts/phase25_multiseed_2026_04_25/cicddos2019_lambda0p3_seed42", + "n_train": 10000, + "n_val": 10000, + "n_atk": 20000, + "D": 9, + "epochs": 15, + "lr": 0.0001, + "k_disc": 3.0, + "temperature": 50.0, + "d_model": 128, + "t_train_sec": 23.69, + "loss_first_last": [ + 0.14222308861303934, + 0.005586131493549181 + ], + "overall_by_agg": { + "mean": { + 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b/artifacts/baselines/anomaly_transformer_2026_04_29/cicddos_within_seed42.npz new file mode 100644 index 0000000..0cd68dd Binary files /dev/null and b/artifacts/baselines/anomaly_transformer_2026_04_29/cicddos_within_seed42.npz differ diff --git a/artifacts/baselines/anomaly_transformer_2026_04_29/cicddos_within_seed43.json b/artifacts/baselines/anomaly_transformer_2026_04_29/cicddos_within_seed43.json new file mode 100644 index 0000000..f9fa734 --- /dev/null +++ b/artifacts/baselines/anomaly_transformer_2026_04_29/cicddos_within_seed43.json @@ -0,0 +1,320 @@ +{ + "method": "anomaly_transformer", + "protocol": "cicddos_within", + "seed": 43, + "model_dir": "/home/chy/mambafortrafficmodeling/artifacts/phase25_multiseed_2026_04_25/cicddos2019_lambda0p3_seed43", + "n_train": 10000, + "n_val": 10000, + "n_atk": 20000, + "D": 9, + "epochs": 15, + "lr": 0.0001, + "k_disc": 3.0, + "temperature": 50.0, + "d_model": 128, + "t_train_sec": 24.46, + "loss_first_last": [ + 0.1440289024310776, + 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a/artifacts/baselines/anomaly_transformer_2026_04_29/iscxtor_within_seed42.npz b/artifacts/baselines/anomaly_transformer_2026_04_29/iscxtor_within_seed42.npz new file mode 100644 index 0000000..e61eba0 Binary files /dev/null and b/artifacts/baselines/anomaly_transformer_2026_04_29/iscxtor_within_seed42.npz differ diff --git a/artifacts/baselines/anomaly_transformer_2026_04_29/iscxtor_within_seed43.json b/artifacts/baselines/anomaly_transformer_2026_04_29/iscxtor_within_seed43.json new file mode 100644 index 0000000..d3e0cca --- /dev/null +++ b/artifacts/baselines/anomaly_transformer_2026_04_29/iscxtor_within_seed43.json @@ -0,0 +1,64 @@ +{ + "method": "anomaly_transformer", + "protocol": "iscxtor_within", + "seed": 43, + "model_dir": "/home/chy/mambafortrafficmodeling/artifacts/phase25_multiseed_2026_04_25/iscxtor2016_lambda0p3_seed43", + "n_train": 10000, + "n_val": 10000, + "n_atk": 1312, + "D": 9, + "epochs": 15, + "lr": 0.0001, + "k_disc": 3.0, + "temperature": 50.0, + "d_model": 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0000000..16dd0dd Binary files /dev/null and b/artifacts/baselines/anomaly_transformer_2026_04_29/iscxtor_within_seed43.npz differ diff --git a/artifacts/baselines/anomaly_transformer_2026_04_29/iscxtor_within_seed44.json b/artifacts/baselines/anomaly_transformer_2026_04_29/iscxtor_within_seed44.json new file mode 100644 index 0000000..f43e3ac --- /dev/null +++ b/artifacts/baselines/anomaly_transformer_2026_04_29/iscxtor_within_seed44.json @@ -0,0 +1,64 @@ +{ + "method": "anomaly_transformer", + "protocol": "iscxtor_within", + "seed": 44, + "model_dir": "/home/chy/mambafortrafficmodeling/artifacts/phase25_multiseed_2026_04_25/iscxtor2016_lambda0p3_seed44", + "n_train": 10000, + "n_val": 10000, + "n_atk": 1312, + "D": 9, + "epochs": 15, + "lr": 0.0001, + "k_disc": 3.0, + "temperature": 50.0, + "d_model": 128, + "t_train_sec": 20.76, + "loss_first_last": [ + 0.15891090356096438, + 0.006547442672750618 + ], + "overall_by_agg": { + "mean": { + "auroc": 0.48698502286585366, + "auprc": 0.1969998720953106 + }, + "max": { + "auroc": 0.5150377286585366, + "auprc": 0.22484916372486033 + }, + "median": { + "auroc": 0.5020458079268293, + "auprc": 0.1872525491455697 + }, + "p90": { + "auroc": 0.4449618902439024, + "auprc": 0.18297321346276874 + } + }, + "per_class_by_agg": { + "mean": { + "tor": { + "_n": 1312.0, + "auroc": 0.48698502286585366 + } + }, + "max": { + "tor": { + "_n": 1312.0, + "auroc": 0.5150377286585366 + } + }, + "median": { + "tor": { + "_n": 1312.0, + "auroc": 0.5020458079268293 + } + }, + "p90": { + "tor": { + "_n": 1312.0, + "auroc": 0.4449618902439024 + } + } + } +} \ No newline at end of file diff --git a/artifacts/baselines/anomaly_transformer_2026_04_29/iscxtor_within_seed44.npz b/artifacts/baselines/anomaly_transformer_2026_04_29/iscxtor_within_seed44.npz new file mode 100644 index 0000000..228a18e Binary files /dev/null and b/artifacts/baselines/anomaly_transformer_2026_04_29/iscxtor_within_seed44.npz differ diff --git a/artifacts/baselines/anomaly_transformer_2026_04_29/master.log b/artifacts/baselines/anomaly_transformer_2026_04_29/master.log new file mode 100644 index 0000000..d884b13 --- /dev/null +++ b/artifacts/baselines/anomaly_transformer_2026_04_29/master.log @@ -0,0 +1,315 @@ +=== protocol=iscxtor_within seed=42 epochs=15 batch=128 === +[run] anomaly_transformer protocol=iscxtor_within seed=42 +[data] flows=/home/chy/mambafortrafficmodeling/datasets/iscxtor2016/processed/flows.parquet packets_source=/home/chy/mambafortrafficmodeling/datasets/iscxtor2016/processed/packets.npz +[data] using external flow features D=20 +[data] rows total=103,079 keep len>=2: 66,189 +[data] benign=64,877 attack=1,312 -> train=51,901 val=10,000 +[data] train_flows=10,000 val=10,000 attack=1,312 D=9 device=cuda +[model] params=305,941 + [epoch 5/15] rec_loss=0.0274 (7.7s elapsed) + [epoch 10/15] rec_loss=0.0131 (14.3s elapsed) + [epoch 15/15] rec_loss=0.0056 (21.2s elapsed) +[train] 21.2s, final rec_loss=0.0056 +[score] benign in 2.1s +[score] attack in 0.3s +[saved] artifacts/baselines/anomaly_transformer_2026_04_29/iscxtor_within_seed42.json +[best agg=max] AUROC=0.5264 AUPRC=0.2290 + max AUROC=0.5264 AUPRC=0.2290 + mean AUROC=0.4932 AUPRC=0.1752 + median AUROC=0.4792 AUPRC=0.1852 + p90 AUROC=0.4373 AUPRC=0.1487 +[done] elapsed=33s artifacts/baselines/anomaly_transformer_2026_04_29/iscxtor_within_seed42.json +=== protocol=iscxtor_within seed=43 epochs=15 batch=128 === +[run] anomaly_transformer protocol=iscxtor_within seed=43 +[data] flows=/home/chy/mambafortrafficmodeling/datasets/iscxtor2016/processed/flows.parquet packets_source=/home/chy/mambafortrafficmodeling/datasets/iscxtor2016/processed/packets.npz +[data] using external flow features D=20 +[data] rows total=103,079 keep len>=2: 66,189 +[data] benign=64,877 attack=1,312 -> train=51,901 val=10,000 +[data] train_flows=10,000 val=10,000 attack=1,312 D=9 device=cuda +[model] params=305,941 + [epoch 5/15] rec_loss=0.0250 (7.6s elapsed) + [epoch 10/15] rec_loss=0.0078 (14.9s elapsed) + [epoch 15/15] rec_loss=0.0038 (22.5s elapsed) +[train] 22.5s, final rec_loss=0.0038 +[score] benign in 2.1s +[score] attack in 0.3s +[saved] artifacts/baselines/anomaly_transformer_2026_04_29/iscxtor_within_seed43.json +[best agg=median] AUROC=0.4736 AUPRC=0.1267 + median AUROC=0.4736 AUPRC=0.1267 + max AUROC=0.4134 AUPRC=0.2217 + mean AUROC=0.4133 AUPRC=0.1929 + p90 AUROC=0.3542 AUPRC=0.1179 +[done] elapsed=34s artifacts/baselines/anomaly_transformer_2026_04_29/iscxtor_within_seed43.json +=== protocol=iscxtor_within seed=44 epochs=15 batch=128 === +[run] anomaly_transformer protocol=iscxtor_within seed=44 +[data] flows=/home/chy/mambafortrafficmodeling/datasets/iscxtor2016/processed/flows.parquet packets_source=/home/chy/mambafortrafficmodeling/datasets/iscxtor2016/processed/packets.npz +[data] using external flow features D=20 +[data] rows total=103,079 keep len>=2: 66,189 +[data] benign=64,877 attack=1,312 -> train=51,901 val=10,000 +[data] train_flows=10,000 val=10,000 attack=1,312 D=9 device=cuda +[model] params=305,941 + [epoch 5/15] rec_loss=0.0230 (7.2s elapsed) + [epoch 10/15] rec_loss=0.0071 (14.0s elapsed) + [epoch 15/15] rec_loss=0.0065 (20.8s elapsed) +[train] 20.8s, final rec_loss=0.0065 +[score] benign in 2.2s +[score] attack in 0.3s +[saved] artifacts/baselines/anomaly_transformer_2026_04_29/iscxtor_within_seed44.json +[best agg=max] AUROC=0.5150 AUPRC=0.2248 + max AUROC=0.5150 AUPRC=0.2248 + median AUROC=0.5020 AUPRC=0.1873 + mean AUROC=0.4870 AUPRC=0.1970 + p90 AUROC=0.4450 AUPRC=0.1830 +[done] elapsed=33s artifacts/baselines/anomaly_transformer_2026_04_29/iscxtor_within_seed44.json +=== protocol=cicids_within seed=42 epochs=15 batch=128 === +[run] anomaly_transformer protocol=cicids_within seed=42 +[data] flows=/home/chy/mambafortrafficmodeling/datasets/cicids2017/processed/flows.parquet packets_source=/home/chy/mambafortrafficmodeling/datasets/cicids2017/processed/packets.npz +[data] using external flow features D=20 +[data] rows total=2,025,564 keep len>=2: 2,017,180 +[data] benign=1,513,450 attack=30,000 -> train=1,210,760 val=10,000 +[data] train_flows=10,000 val=10,000 attack=30,000 D=9 device=cuda +[model] params=305,941 + [epoch 5/15] rec_loss=0.0256 (7.2s elapsed) + [epoch 10/15] rec_loss=0.0108 (14.6s elapsed) + [epoch 15/15] rec_loss=0.0041 (22.5s elapsed) +[train] 22.5s, final rec_loss=0.0041 +[score] benign in 2.1s +[score] attack in 6.4s +[saved] artifacts/baselines/anomaly_transformer_2026_04_29/cicids_within_seed42.json +[best agg=mean] AUROC=0.6001 AUPRC=0.8383 + mean AUROC=0.6001 AUPRC=0.8383 + max AUROC=0.5478 AUPRC=0.7840 + p90 AUROC=0.4774 AUPRC=0.7639 + median AUROC=0.3624 AUPRC=0.7142 +[done] elapsed=142s artifacts/baselines/anomaly_transformer_2026_04_29/cicids_within_seed42.json +=== protocol=cicids_within seed=43 epochs=15 batch=128 === +[run] anomaly_transformer protocol=cicids_within seed=43 +[data] flows=/home/chy/mambafortrafficmodeling/datasets/cicids2017/processed/flows.parquet packets_source=/home/chy/mambafortrafficmodeling/datasets/cicids2017/processed/packets.npz +[data] using external flow features D=20 +[data] rows total=2,025,564 keep len>=2: 2,017,180 +[data] benign=1,513,450 attack=30,000 -> train=1,210,760 val=10,000 +[data] train_flows=10,000 val=10,000 attack=30,000 D=9 device=cuda +[model] params=305,941 + [epoch 5/15] rec_loss=0.0233 (8.2s elapsed) + [epoch 10/15] rec_loss=0.0081 (16.0s elapsed) + [epoch 15/15] rec_loss=0.0038 (23.7s elapsed) +[train] 23.7s, final rec_loss=0.0038 +[score] benign in 2.1s +[score] attack in 6.3s +[saved] artifacts/baselines/anomaly_transformer_2026_04_29/cicids_within_seed43.json +[best agg=median] AUROC=0.2927 AUPRC=0.6398 + median AUROC=0.2927 AUPRC=0.6398 + p90 AUROC=0.2795 AUPRC=0.6818 + mean AUROC=0.2588 AUPRC=0.6678 + max AUROC=0.2573 AUPRC=0.6591 +[done] elapsed=141s artifacts/baselines/anomaly_transformer_2026_04_29/cicids_within_seed43.json +=== protocol=cicids_within seed=44 epochs=15 batch=128 === +[run] anomaly_transformer protocol=cicids_within seed=44 +[data] flows=/home/chy/mambafortrafficmodeling/datasets/cicids2017/processed/flows.parquet packets_source=/home/chy/mambafortrafficmodeling/datasets/cicids2017/processed/packets.npz +[data] using external flow features D=20 +[data] rows total=2,025,564 keep len>=2: 2,017,180 +[data] benign=1,513,450 attack=30,000 -> train=1,210,760 val=10,000 +[data] train_flows=10,000 val=10,000 attack=30,000 D=9 device=cuda +[model] params=305,941 + [epoch 5/15] rec_loss=0.0197 (8.3s elapsed) + [epoch 10/15] rec_loss=0.0097 (16.1s elapsed) + [epoch 15/15] rec_loss=0.0037 (23.8s elapsed) +[train] 23.8s, final rec_loss=0.0037 +[score] benign in 2.2s +[score] attack in 6.4s +[saved] artifacts/baselines/anomaly_transformer_2026_04_29/cicids_within_seed44.json +[best agg=max] AUROC=0.6878 AUPRC=0.8658 + max AUROC=0.6878 AUPRC=0.8658 + mean AUROC=0.6436 AUPRC=0.8672 + p90 AUROC=0.5559 AUPRC=0.8319 + median AUROC=0.4988 AUPRC=0.8127 +[done] elapsed=141s artifacts/baselines/anomaly_transformer_2026_04_29/cicids_within_seed44.json +=== protocol=cicddos_within seed=42 epochs=15 batch=128 === +[run] anomaly_transformer protocol=cicddos_within seed=42 +[data] flows=/home/chy/mambafortrafficmodeling/datasets/cicddos2019/processed/flows.parquet packets_source=/home/chy/mambafortrafficmodeling/datasets/cicddos2019/processed/full_store +[data] using external flow features D=20 +[data] rows total=8,993,376 keep len>=2: 8,986,875 +[data] benign=93,207 attack=20,000 -> train=74,565 val=18,642 +[data] train_flows=10,000 val=10,000 attack=20,000 D=9 device=cuda +[model] params=305,941 + [epoch 5/15] rec_loss=0.0306 (8.3s elapsed) + [epoch 10/15] rec_loss=0.0127 (15.9s elapsed) + [epoch 15/15] rec_loss=0.0056 (23.7s elapsed) +[train] 23.7s, final rec_loss=0.0056 +[score] benign in 2.1s +[score] attack in 4.1s +[saved] artifacts/baselines/anomaly_transformer_2026_04_29/cicddos_within_seed42.json +[best agg=median] AUROC=0.4061 AUPRC=0.7025 + median AUROC=0.4061 AUPRC=0.7025 + p90 AUROC=0.3688 AUPRC=0.6932 + mean AUROC=0.3678 AUPRC=0.7023 + max AUROC=0.3495 AUPRC=0.6605 +[done] elapsed=45s artifacts/baselines/anomaly_transformer_2026_04_29/cicddos_within_seed42.json +=== protocol=cicddos_within seed=43 epochs=15 batch=128 === +[run] anomaly_transformer protocol=cicddos_within seed=43 +[data] flows=/home/chy/mambafortrafficmodeling/datasets/cicddos2019/processed/flows.parquet packets_source=/home/chy/mambafortrafficmodeling/datasets/cicddos2019/processed/full_store +[data] using external flow features D=20 +[data] rows total=8,993,376 keep len>=2: 8,986,875 +[data] benign=93,207 attack=20,000 -> train=74,565 val=18,642 +[data] train_flows=10,000 val=10,000 attack=20,000 D=9 device=cuda +[model] params=305,941 + [epoch 5/15] rec_loss=0.0222 (8.2s elapsed) + [epoch 10/15] rec_loss=0.0079 (16.2s elapsed) + [epoch 15/15] rec_loss=0.0040 (24.5s elapsed) +[train] 24.5s, final rec_loss=0.0040 +[score] benign in 2.0s +[score] attack in 4.1s +[saved] artifacts/baselines/anomaly_transformer_2026_04_29/cicddos_within_seed43.json +[best agg=p90] AUROC=0.1995 AUPRC=0.6167 + p90 AUROC=0.1995 AUPRC=0.6167 + median AUROC=0.1869 AUPRC=0.5569 + max AUROC=0.1673 AUPRC=0.6028 + mean AUROC=0.1619 AUPRC=0.6078 +[done] elapsed=46s artifacts/baselines/anomaly_transformer_2026_04_29/cicddos_within_seed43.json +=== protocol=cicddos_within seed=44 epochs=15 batch=128 === +[run] anomaly_transformer protocol=cicddos_within seed=44 +[data] flows=/home/chy/mambafortrafficmodeling/datasets/cicddos2019/processed/flows.parquet packets_source=/home/chy/mambafortrafficmodeling/datasets/cicddos2019/processed/full_store +[data] using external flow features D=20 +[data] rows total=8,993,376 keep len>=2: 8,986,875 +[data] benign=93,207 attack=20,000 -> train=74,565 val=18,642 +[data] train_flows=10,000 val=10,000 attack=20,000 D=9 device=cuda +[model] params=305,941 + [epoch 5/15] rec_loss=0.0225 (8.0s elapsed) + [epoch 10/15] rec_loss=0.0078 (15.6s elapsed) + [epoch 15/15] rec_loss=0.0041 (23.1s elapsed) +[train] 23.1s, final rec_loss=0.0041 +[score] benign in 2.1s +[score] attack in 4.1s +[saved] artifacts/baselines/anomaly_transformer_2026_04_29/cicddos_within_seed44.json +[best agg=median] AUROC=0.8402 AUPRC=0.9045 + median AUROC=0.8402 AUPRC=0.9045 + p90 AUROC=0.6628 AUPRC=0.8249 + mean AUROC=0.6323 AUPRC=0.8247 + max AUROC=0.5918 AUPRC=0.7645 +[done] elapsed=45s artifacts/baselines/anomaly_transformer_2026_04_29/cicddos_within_seed44.json +=== protocol=forward_cross seed=42 epochs=15 batch=128 === +[run] anomaly_transformer protocol=forward_cross seed=42 +[data] flows=/home/chy/mambafortrafficmodeling/datasets/cicids2017/processed/flows.parquet packets_source=/home/chy/mambafortrafficmodeling/datasets/cicids2017/processed/packets.npz +[data] using external flow features D=20 +[data] rows total=2,025,564 keep len>=2: 2,017,180 +[data] benign=1,513,450 attack=503,730 -> train=1,210,760 val=302,690 +[data] train_flows=10,000 val=10,000 attack=9,846 D=9 device=cuda +[model] params=305,941 + [epoch 5/15] rec_loss=0.0256 (7.7s elapsed) + [epoch 10/15] rec_loss=0.0107 (15.0s elapsed) + [epoch 15/15] rec_loss=0.0040 (22.5s elapsed) +[train] 22.5s, final rec_loss=0.0040 +[score] benign in 2.1s +[score] attack in 2.1s +[saved] artifacts/baselines/anomaly_transformer_2026_04_29/forward_cross_seed42.json +[best agg=median] AUROC=0.6214 AUPRC=0.6244 + median AUROC=0.6214 AUPRC=0.6244 + p90 AUROC=0.5738 AUPRC=0.5601 + mean AUROC=0.4892 AUPRC=0.5018 + max AUROC=0.4702 AUPRC=0.4553 +[done] elapsed=157s artifacts/baselines/anomaly_transformer_2026_04_29/forward_cross_seed42.json +=== protocol=forward_cross seed=43 epochs=15 batch=128 === +[run] anomaly_transformer protocol=forward_cross seed=43 +[data] flows=/home/chy/mambafortrafficmodeling/datasets/cicids2017/processed/flows.parquet packets_source=/home/chy/mambafortrafficmodeling/datasets/cicids2017/processed/packets.npz +[data] using external flow features D=20 +[data] rows total=2,025,564 keep len>=2: 2,017,180 +[data] benign=1,513,450 attack=503,730 -> train=1,210,760 val=302,690 +[data] train_flows=10,000 val=10,000 attack=9,846 D=9 device=cuda +[model] params=305,941 + [epoch 5/15] rec_loss=0.0231 (7.8s elapsed) + [epoch 10/15] rec_loss=0.0081 (15.3s elapsed) + [epoch 15/15] rec_loss=0.0038 (22.7s elapsed) +[train] 22.7s, final rec_loss=0.0038 +[score] benign in 2.1s +[score] attack in 2.1s +[saved] artifacts/baselines/anomaly_transformer_2026_04_29/forward_cross_seed43.json +[best agg=median] AUROC=0.6319 AUPRC=0.6693 + median AUROC=0.6319 AUPRC=0.6693 + p90 AUROC=0.5401 AUPRC=0.5542 + mean AUROC=0.4969 AUPRC=0.4924 + max AUROC=0.4889 AUPRC=0.4859 +[done] elapsed=157s artifacts/baselines/anomaly_transformer_2026_04_29/forward_cross_seed43.json +=== protocol=forward_cross seed=44 epochs=15 batch=128 === +[run] anomaly_transformer protocol=forward_cross seed=44 +[data] flows=/home/chy/mambafortrafficmodeling/datasets/cicids2017/processed/flows.parquet packets_source=/home/chy/mambafortrafficmodeling/datasets/cicids2017/processed/packets.npz +[data] using external flow features D=20 +[data] rows total=2,025,564 keep len>=2: 2,017,180 +[data] benign=1,513,450 attack=503,730 -> train=1,210,760 val=302,690 +[data] train_flows=10,000 val=10,000 attack=9,846 D=9 device=cuda +[model] params=305,941 + [epoch 5/15] rec_loss=0.0195 (8.1s elapsed) + [epoch 10/15] rec_loss=0.0100 (15.7s elapsed) + [epoch 15/15] rec_loss=0.0037 (23.1s elapsed) +[train] 23.1s, final rec_loss=0.0037 +[score] benign in 2.2s +[score] attack in 2.1s +[saved] artifacts/baselines/anomaly_transformer_2026_04_29/forward_cross_seed44.json +[best agg=median] AUROC=0.3678 AUPRC=0.4001 + median AUROC=0.3678 AUPRC=0.4001 + p90 AUROC=0.3272 AUPRC=0.4094 + mean AUROC=0.2587 AUPRC=0.3873 + max AUROC=0.2540 AUPRC=0.3701 +[done] elapsed=157s artifacts/baselines/anomaly_transformer_2026_04_29/forward_cross_seed44.json +=== protocol=reverse_cross seed=42 epochs=15 batch=128 === +[run] anomaly_transformer protocol=reverse_cross seed=42 +[data] flows=/home/chy/mambafortrafficmodeling/datasets/cicddos2019/processed/flows.parquet packets_source=/home/chy/mambafortrafficmodeling/datasets/cicddos2019/processed/full_store +[data] using external flow features D=20 +[data] rows total=8,993,376 keep len>=2: 8,986,875 +[data] benign=93,207 attack=8,893,668 -> train=74,565 val=18,642 +[data] train_flows=10,000 val=10,000 attack=6,772 D=9 device=cuda +[model] params=305,941 + [epoch 5/15] rec_loss=0.0299 (8.1s elapsed) + [epoch 10/15] rec_loss=0.0128 (15.7s elapsed) + [epoch 15/15] rec_loss=0.0056 (23.3s elapsed) +[train] 23.3s, final rec_loss=0.0056 +[score] benign in 2.1s +[score] attack in 1.4s +[saved] artifacts/baselines/anomaly_transformer_2026_04_29/reverse_cross_seed42.json +[best agg=mean] AUROC=0.8442 AUPRC=0.7504 + mean AUROC=0.8442 AUPRC=0.7504 + max AUROC=0.8172 AUPRC=0.7065 + p90 AUROC=0.7700 AUPRC=0.6800 + median AUROC=0.6507 AUPRC=0.6041 +[done] elapsed=250s artifacts/baselines/anomaly_transformer_2026_04_29/reverse_cross_seed42.json +=== protocol=reverse_cross seed=43 epochs=15 batch=128 === +[run] anomaly_transformer protocol=reverse_cross seed=43 +[data] flows=/home/chy/mambafortrafficmodeling/datasets/cicddos2019/processed/flows.parquet packets_source=/home/chy/mambafortrafficmodeling/datasets/cicddos2019/processed/full_store +[data] using external flow features D=20 +[data] rows total=8,993,376 keep len>=2: 8,986,875 +[data] benign=93,207 attack=8,893,668 -> train=74,565 val=18,642 +[data] train_flows=10,000 val=10,000 attack=6,772 D=9 device=cuda +[model] params=305,941 + [epoch 5/15] rec_loss=0.0222 (8.1s elapsed) + [epoch 10/15] rec_loss=0.0077 (15.8s elapsed) + [epoch 15/15] rec_loss=0.0040 (23.6s elapsed) +[train] 23.6s, final rec_loss=0.0040 +[score] benign in 2.1s +[score] attack in 1.5s +[saved] artifacts/baselines/anomaly_transformer_2026_04_29/reverse_cross_seed43.json +[best agg=max] AUROC=0.6797 AUPRC=0.5524 + max AUROC=0.6797 AUPRC=0.5524 + mean AUROC=0.4566 AUPRC=0.4307 + p90 AUROC=0.3843 AUPRC=0.3849 + median AUROC=0.3337 AUPRC=0.4061 +[done] elapsed=247s artifacts/baselines/anomaly_transformer_2026_04_29/reverse_cross_seed43.json +=== protocol=reverse_cross seed=44 epochs=15 batch=128 === +[run] anomaly_transformer protocol=reverse_cross seed=44 +[data] flows=/home/chy/mambafortrafficmodeling/datasets/cicddos2019/processed/flows.parquet packets_source=/home/chy/mambafortrafficmodeling/datasets/cicddos2019/processed/full_store +[data] using external flow features D=20 +[data] rows total=8,993,376 keep len>=2: 8,986,875 +[data] benign=93,207 attack=8,893,668 -> train=74,565 val=18,642 +[data] train_flows=10,000 val=10,000 attack=6,772 D=9 device=cuda +[model] params=305,941 + [epoch 5/15] rec_loss=0.0225 (7.9s elapsed) + [epoch 10/15] rec_loss=0.0077 (15.2s elapsed) + [epoch 15/15] rec_loss=0.0040 (22.7s elapsed) +[train] 22.7s, final rec_loss=0.0040 +[score] benign in 2.1s +[score] attack in 1.5s +[saved] artifacts/baselines/anomaly_transformer_2026_04_29/reverse_cross_seed44.json +[best agg=max] AUROC=0.5801 AUPRC=0.6040 + max AUROC=0.5801 AUPRC=0.6040 + median AUROC=0.4205 AUPRC=0.3476 + mean AUROC=0.3775 AUPRC=0.4047 + p90 AUROC=0.2758 AUPRC=0.3123 +[done] elapsed=244s artifacts/baselines/anomaly_transformer_2026_04_29/reverse_cross_seed44.json diff --git a/artifacts/baselines/anomaly_transformer_2026_04_29/orchestrator.log b/artifacts/baselines/anomaly_transformer_2026_04_29/orchestrator.log new file mode 100644 index 0000000..6621f8a --- /dev/null +++ b/artifacts/baselines/anomaly_transformer_2026_04_29/orchestrator.log @@ -0,0 +1,316 @@ +=== protocol=iscxtor_within seed=42 epochs=15 batch=128 === +[run] anomaly_transformer protocol=iscxtor_within seed=42 +[data] flows=/home/chy/mambafortrafficmodeling/datasets/iscxtor2016/processed/flows.parquet packets_source=/home/chy/mambafortrafficmodeling/datasets/iscxtor2016/processed/packets.npz +[data] using external flow features D=20 +[data] rows total=103,079 keep len>=2: 66,189 +[data] benign=64,877 attack=1,312 -> train=51,901 val=10,000 +[data] train_flows=10,000 val=10,000 attack=1,312 D=9 device=cuda +[model] params=305,941 + [epoch 5/15] rec_loss=0.0274 (7.7s elapsed) + [epoch 10/15] rec_loss=0.0131 (14.3s elapsed) + [epoch 15/15] rec_loss=0.0056 (21.2s elapsed) +[train] 21.2s, final rec_loss=0.0056 +[score] benign in 2.1s +[score] attack in 0.3s +[saved] artifacts/baselines/anomaly_transformer_2026_04_29/iscxtor_within_seed42.json +[best agg=max] AUROC=0.5264 AUPRC=0.2290 + max AUROC=0.5264 AUPRC=0.2290 + mean AUROC=0.4932 AUPRC=0.1752 + median AUROC=0.4792 AUPRC=0.1852 + p90 AUROC=0.4373 AUPRC=0.1487 +[done] elapsed=33s artifacts/baselines/anomaly_transformer_2026_04_29/iscxtor_within_seed42.json +=== protocol=iscxtor_within seed=43 epochs=15 batch=128 === +[run] anomaly_transformer protocol=iscxtor_within seed=43 +[data] flows=/home/chy/mambafortrafficmodeling/datasets/iscxtor2016/processed/flows.parquet packets_source=/home/chy/mambafortrafficmodeling/datasets/iscxtor2016/processed/packets.npz +[data] using external flow features D=20 +[data] rows total=103,079 keep len>=2: 66,189 +[data] benign=64,877 attack=1,312 -> train=51,901 val=10,000 +[data] train_flows=10,000 val=10,000 attack=1,312 D=9 device=cuda +[model] params=305,941 + [epoch 5/15] rec_loss=0.0250 (7.6s elapsed) + [epoch 10/15] rec_loss=0.0078 (14.9s elapsed) + [epoch 15/15] rec_loss=0.0038 (22.5s elapsed) +[train] 22.5s, final rec_loss=0.0038 +[score] benign in 2.1s +[score] attack in 0.3s +[saved] artifacts/baselines/anomaly_transformer_2026_04_29/iscxtor_within_seed43.json +[best agg=median] AUROC=0.4736 AUPRC=0.1267 + median AUROC=0.4736 AUPRC=0.1267 + max AUROC=0.4134 AUPRC=0.2217 + mean AUROC=0.4133 AUPRC=0.1929 + p90 AUROC=0.3542 AUPRC=0.1179 +[done] elapsed=34s artifacts/baselines/anomaly_transformer_2026_04_29/iscxtor_within_seed43.json +=== protocol=iscxtor_within seed=44 epochs=15 batch=128 === +[run] anomaly_transformer protocol=iscxtor_within seed=44 +[data] flows=/home/chy/mambafortrafficmodeling/datasets/iscxtor2016/processed/flows.parquet packets_source=/home/chy/mambafortrafficmodeling/datasets/iscxtor2016/processed/packets.npz +[data] using external flow features D=20 +[data] rows total=103,079 keep len>=2: 66,189 +[data] benign=64,877 attack=1,312 -> train=51,901 val=10,000 +[data] train_flows=10,000 val=10,000 attack=1,312 D=9 device=cuda +[model] params=305,941 + [epoch 5/15] rec_loss=0.0230 (7.2s elapsed) + [epoch 10/15] rec_loss=0.0071 (14.0s elapsed) + [epoch 15/15] rec_loss=0.0065 (20.8s elapsed) +[train] 20.8s, final rec_loss=0.0065 +[score] benign in 2.2s +[score] attack in 0.3s +[saved] artifacts/baselines/anomaly_transformer_2026_04_29/iscxtor_within_seed44.json +[best agg=max] AUROC=0.5150 AUPRC=0.2248 + max AUROC=0.5150 AUPRC=0.2248 + median AUROC=0.5020 AUPRC=0.1873 + mean AUROC=0.4870 AUPRC=0.1970 + p90 AUROC=0.4450 AUPRC=0.1830 +[done] elapsed=33s artifacts/baselines/anomaly_transformer_2026_04_29/iscxtor_within_seed44.json +=== protocol=cicids_within seed=42 epochs=15 batch=128 === +[run] anomaly_transformer protocol=cicids_within seed=42 +[data] flows=/home/chy/mambafortrafficmodeling/datasets/cicids2017/processed/flows.parquet packets_source=/home/chy/mambafortrafficmodeling/datasets/cicids2017/processed/packets.npz +[data] using external flow features D=20 +[data] rows total=2,025,564 keep len>=2: 2,017,180 +[data] benign=1,513,450 attack=30,000 -> train=1,210,760 val=10,000 +[data] train_flows=10,000 val=10,000 attack=30,000 D=9 device=cuda +[model] params=305,941 + [epoch 5/15] rec_loss=0.0256 (7.2s elapsed) + [epoch 10/15] rec_loss=0.0108 (14.6s elapsed) + [epoch 15/15] rec_loss=0.0041 (22.5s elapsed) +[train] 22.5s, final rec_loss=0.0041 +[score] benign in 2.1s +[score] attack in 6.4s +[saved] artifacts/baselines/anomaly_transformer_2026_04_29/cicids_within_seed42.json +[best agg=mean] AUROC=0.6001 AUPRC=0.8383 + mean AUROC=0.6001 AUPRC=0.8383 + max AUROC=0.5478 AUPRC=0.7840 + p90 AUROC=0.4774 AUPRC=0.7639 + median AUROC=0.3624 AUPRC=0.7142 +[done] elapsed=142s artifacts/baselines/anomaly_transformer_2026_04_29/cicids_within_seed42.json +=== protocol=cicids_within seed=43 epochs=15 batch=128 === +[run] anomaly_transformer protocol=cicids_within seed=43 +[data] flows=/home/chy/mambafortrafficmodeling/datasets/cicids2017/processed/flows.parquet packets_source=/home/chy/mambafortrafficmodeling/datasets/cicids2017/processed/packets.npz +[data] using external flow features D=20 +[data] rows total=2,025,564 keep len>=2: 2,017,180 +[data] benign=1,513,450 attack=30,000 -> train=1,210,760 val=10,000 +[data] train_flows=10,000 val=10,000 attack=30,000 D=9 device=cuda +[model] params=305,941 + [epoch 5/15] rec_loss=0.0233 (8.2s elapsed) + [epoch 10/15] rec_loss=0.0081 (16.0s elapsed) + [epoch 15/15] rec_loss=0.0038 (23.7s elapsed) +[train] 23.7s, final rec_loss=0.0038 +[score] benign in 2.1s +[score] attack in 6.3s +[saved] artifacts/baselines/anomaly_transformer_2026_04_29/cicids_within_seed43.json +[best agg=median] AUROC=0.2927 AUPRC=0.6398 + median AUROC=0.2927 AUPRC=0.6398 + p90 AUROC=0.2795 AUPRC=0.6818 + mean AUROC=0.2588 AUPRC=0.6678 + max AUROC=0.2573 AUPRC=0.6591 +[done] elapsed=141s artifacts/baselines/anomaly_transformer_2026_04_29/cicids_within_seed43.json +=== protocol=cicids_within seed=44 epochs=15 batch=128 === +[run] anomaly_transformer protocol=cicids_within seed=44 +[data] flows=/home/chy/mambafortrafficmodeling/datasets/cicids2017/processed/flows.parquet packets_source=/home/chy/mambafortrafficmodeling/datasets/cicids2017/processed/packets.npz +[data] using external flow features D=20 +[data] rows total=2,025,564 keep len>=2: 2,017,180 +[data] benign=1,513,450 attack=30,000 -> train=1,210,760 val=10,000 +[data] train_flows=10,000 val=10,000 attack=30,000 D=9 device=cuda +[model] params=305,941 + [epoch 5/15] rec_loss=0.0197 (8.3s elapsed) + [epoch 10/15] rec_loss=0.0097 (16.1s elapsed) + [epoch 15/15] rec_loss=0.0037 (23.8s elapsed) +[train] 23.8s, final rec_loss=0.0037 +[score] benign in 2.2s +[score] attack in 6.4s +[saved] artifacts/baselines/anomaly_transformer_2026_04_29/cicids_within_seed44.json +[best agg=max] AUROC=0.6878 AUPRC=0.8658 + max AUROC=0.6878 AUPRC=0.8658 + mean AUROC=0.6436 AUPRC=0.8672 + p90 AUROC=0.5559 AUPRC=0.8319 + median AUROC=0.4988 AUPRC=0.8127 +[done] elapsed=141s artifacts/baselines/anomaly_transformer_2026_04_29/cicids_within_seed44.json +=== protocol=cicddos_within seed=42 epochs=15 batch=128 === +[run] anomaly_transformer protocol=cicddos_within seed=42 +[data] flows=/home/chy/mambafortrafficmodeling/datasets/cicddos2019/processed/flows.parquet packets_source=/home/chy/mambafortrafficmodeling/datasets/cicddos2019/processed/full_store +[data] using external flow features D=20 +[data] rows total=8,993,376 keep len>=2: 8,986,875 +[data] benign=93,207 attack=20,000 -> train=74,565 val=18,642 +[data] train_flows=10,000 val=10,000 attack=20,000 D=9 device=cuda +[model] params=305,941 + [epoch 5/15] rec_loss=0.0306 (8.3s elapsed) + [epoch 10/15] rec_loss=0.0127 (15.9s elapsed) + [epoch 15/15] rec_loss=0.0056 (23.7s elapsed) +[train] 23.7s, final rec_loss=0.0056 +[score] benign in 2.1s +[score] attack in 4.1s +[saved] artifacts/baselines/anomaly_transformer_2026_04_29/cicddos_within_seed42.json +[best agg=median] AUROC=0.4061 AUPRC=0.7025 + median AUROC=0.4061 AUPRC=0.7025 + p90 AUROC=0.3688 AUPRC=0.6932 + mean AUROC=0.3678 AUPRC=0.7023 + max AUROC=0.3495 AUPRC=0.6605 +[done] elapsed=45s artifacts/baselines/anomaly_transformer_2026_04_29/cicddos_within_seed42.json +=== protocol=cicddos_within seed=43 epochs=15 batch=128 === +[run] anomaly_transformer protocol=cicddos_within seed=43 +[data] flows=/home/chy/mambafortrafficmodeling/datasets/cicddos2019/processed/flows.parquet packets_source=/home/chy/mambafortrafficmodeling/datasets/cicddos2019/processed/full_store +[data] using external flow features D=20 +[data] rows total=8,993,376 keep len>=2: 8,986,875 +[data] benign=93,207 attack=20,000 -> train=74,565 val=18,642 +[data] train_flows=10,000 val=10,000 attack=20,000 D=9 device=cuda +[model] params=305,941 + [epoch 5/15] rec_loss=0.0222 (8.2s elapsed) + [epoch 10/15] rec_loss=0.0079 (16.2s elapsed) + [epoch 15/15] rec_loss=0.0040 (24.5s elapsed) +[train] 24.5s, final rec_loss=0.0040 +[score] benign in 2.0s +[score] attack in 4.1s +[saved] artifacts/baselines/anomaly_transformer_2026_04_29/cicddos_within_seed43.json +[best agg=p90] AUROC=0.1995 AUPRC=0.6167 + p90 AUROC=0.1995 AUPRC=0.6167 + median AUROC=0.1869 AUPRC=0.5569 + max AUROC=0.1673 AUPRC=0.6028 + mean AUROC=0.1619 AUPRC=0.6078 +[done] elapsed=46s artifacts/baselines/anomaly_transformer_2026_04_29/cicddos_within_seed43.json +=== protocol=cicddos_within seed=44 epochs=15 batch=128 === +[run] anomaly_transformer protocol=cicddos_within seed=44 +[data] flows=/home/chy/mambafortrafficmodeling/datasets/cicddos2019/processed/flows.parquet packets_source=/home/chy/mambafortrafficmodeling/datasets/cicddos2019/processed/full_store +[data] using external flow features D=20 +[data] rows total=8,993,376 keep len>=2: 8,986,875 +[data] benign=93,207 attack=20,000 -> train=74,565 val=18,642 +[data] train_flows=10,000 val=10,000 attack=20,000 D=9 device=cuda +[model] params=305,941 + [epoch 5/15] rec_loss=0.0225 (8.0s elapsed) + [epoch 10/15] rec_loss=0.0078 (15.6s elapsed) + [epoch 15/15] rec_loss=0.0041 (23.1s elapsed) +[train] 23.1s, final rec_loss=0.0041 +[score] benign in 2.1s +[score] attack in 4.1s +[saved] artifacts/baselines/anomaly_transformer_2026_04_29/cicddos_within_seed44.json +[best agg=median] AUROC=0.8402 AUPRC=0.9045 + median AUROC=0.8402 AUPRC=0.9045 + p90 AUROC=0.6628 AUPRC=0.8249 + mean AUROC=0.6323 AUPRC=0.8247 + max AUROC=0.5918 AUPRC=0.7645 +[done] elapsed=45s artifacts/baselines/anomaly_transformer_2026_04_29/cicddos_within_seed44.json +=== protocol=forward_cross seed=42 epochs=15 batch=128 === +[run] anomaly_transformer protocol=forward_cross seed=42 +[data] flows=/home/chy/mambafortrafficmodeling/datasets/cicids2017/processed/flows.parquet packets_source=/home/chy/mambafortrafficmodeling/datasets/cicids2017/processed/packets.npz +[data] using external flow features D=20 +[data] rows total=2,025,564 keep len>=2: 2,017,180 +[data] benign=1,513,450 attack=503,730 -> train=1,210,760 val=302,690 +[data] train_flows=10,000 val=10,000 attack=9,846 D=9 device=cuda +[model] params=305,941 + [epoch 5/15] rec_loss=0.0256 (7.7s elapsed) + [epoch 10/15] rec_loss=0.0107 (15.0s elapsed) + [epoch 15/15] rec_loss=0.0040 (22.5s elapsed) +[train] 22.5s, final rec_loss=0.0040 +[score] benign in 2.1s +[score] attack in 2.1s +[saved] artifacts/baselines/anomaly_transformer_2026_04_29/forward_cross_seed42.json +[best agg=median] AUROC=0.6214 AUPRC=0.6244 + median AUROC=0.6214 AUPRC=0.6244 + p90 AUROC=0.5738 AUPRC=0.5601 + mean AUROC=0.4892 AUPRC=0.5018 + max AUROC=0.4702 AUPRC=0.4553 +[done] elapsed=157s artifacts/baselines/anomaly_transformer_2026_04_29/forward_cross_seed42.json +=== protocol=forward_cross seed=43 epochs=15 batch=128 === +[run] anomaly_transformer protocol=forward_cross seed=43 +[data] flows=/home/chy/mambafortrafficmodeling/datasets/cicids2017/processed/flows.parquet packets_source=/home/chy/mambafortrafficmodeling/datasets/cicids2017/processed/packets.npz +[data] using external flow features D=20 +[data] rows total=2,025,564 keep len>=2: 2,017,180 +[data] benign=1,513,450 attack=503,730 -> train=1,210,760 val=302,690 +[data] train_flows=10,000 val=10,000 attack=9,846 D=9 device=cuda +[model] params=305,941 + [epoch 5/15] rec_loss=0.0231 (7.8s elapsed) + [epoch 10/15] rec_loss=0.0081 (15.3s elapsed) + [epoch 15/15] rec_loss=0.0038 (22.7s elapsed) +[train] 22.7s, final rec_loss=0.0038 +[score] benign in 2.1s +[score] attack in 2.1s +[saved] artifacts/baselines/anomaly_transformer_2026_04_29/forward_cross_seed43.json +[best agg=median] AUROC=0.6319 AUPRC=0.6693 + median AUROC=0.6319 AUPRC=0.6693 + p90 AUROC=0.5401 AUPRC=0.5542 + mean AUROC=0.4969 AUPRC=0.4924 + max AUROC=0.4889 AUPRC=0.4859 +[done] elapsed=157s artifacts/baselines/anomaly_transformer_2026_04_29/forward_cross_seed43.json +=== protocol=forward_cross seed=44 epochs=15 batch=128 === +[run] anomaly_transformer protocol=forward_cross seed=44 +[data] flows=/home/chy/mambafortrafficmodeling/datasets/cicids2017/processed/flows.parquet packets_source=/home/chy/mambafortrafficmodeling/datasets/cicids2017/processed/packets.npz +[data] using external flow features D=20 +[data] rows total=2,025,564 keep len>=2: 2,017,180 +[data] benign=1,513,450 attack=503,730 -> train=1,210,760 val=302,690 +[data] train_flows=10,000 val=10,000 attack=9,846 D=9 device=cuda +[model] params=305,941 + [epoch 5/15] rec_loss=0.0195 (8.1s elapsed) + [epoch 10/15] rec_loss=0.0100 (15.7s elapsed) + [epoch 15/15] rec_loss=0.0037 (23.1s elapsed) +[train] 23.1s, final rec_loss=0.0037 +[score] benign in 2.2s +[score] attack in 2.1s +[saved] artifacts/baselines/anomaly_transformer_2026_04_29/forward_cross_seed44.json +[best agg=median] AUROC=0.3678 AUPRC=0.4001 + median AUROC=0.3678 AUPRC=0.4001 + p90 AUROC=0.3272 AUPRC=0.4094 + mean AUROC=0.2587 AUPRC=0.3873 + max AUROC=0.2540 AUPRC=0.3701 +[done] elapsed=157s artifacts/baselines/anomaly_transformer_2026_04_29/forward_cross_seed44.json +=== protocol=reverse_cross seed=42 epochs=15 batch=128 === +[run] anomaly_transformer protocol=reverse_cross seed=42 +[data] flows=/home/chy/mambafortrafficmodeling/datasets/cicddos2019/processed/flows.parquet packets_source=/home/chy/mambafortrafficmodeling/datasets/cicddos2019/processed/full_store +[data] using external flow features D=20 +[data] rows total=8,993,376 keep len>=2: 8,986,875 +[data] benign=93,207 attack=8,893,668 -> train=74,565 val=18,642 +[data] train_flows=10,000 val=10,000 attack=6,772 D=9 device=cuda +[model] params=305,941 + [epoch 5/15] rec_loss=0.0299 (8.1s elapsed) + [epoch 10/15] rec_loss=0.0128 (15.7s elapsed) + [epoch 15/15] rec_loss=0.0056 (23.3s elapsed) +[train] 23.3s, final rec_loss=0.0056 +[score] benign in 2.1s +[score] attack in 1.4s +[saved] artifacts/baselines/anomaly_transformer_2026_04_29/reverse_cross_seed42.json +[best agg=mean] AUROC=0.8442 AUPRC=0.7504 + mean AUROC=0.8442 AUPRC=0.7504 + max AUROC=0.8172 AUPRC=0.7065 + p90 AUROC=0.7700 AUPRC=0.6800 + median AUROC=0.6507 AUPRC=0.6041 +[done] elapsed=250s artifacts/baselines/anomaly_transformer_2026_04_29/reverse_cross_seed42.json +=== protocol=reverse_cross seed=43 epochs=15 batch=128 === +[run] anomaly_transformer protocol=reverse_cross seed=43 +[data] flows=/home/chy/mambafortrafficmodeling/datasets/cicddos2019/processed/flows.parquet packets_source=/home/chy/mambafortrafficmodeling/datasets/cicddos2019/processed/full_store +[data] using external flow features D=20 +[data] rows total=8,993,376 keep len>=2: 8,986,875 +[data] benign=93,207 attack=8,893,668 -> train=74,565 val=18,642 +[data] train_flows=10,000 val=10,000 attack=6,772 D=9 device=cuda +[model] params=305,941 + [epoch 5/15] rec_loss=0.0222 (8.1s elapsed) + [epoch 10/15] rec_loss=0.0077 (15.8s elapsed) + [epoch 15/15] rec_loss=0.0040 (23.6s elapsed) +[train] 23.6s, final rec_loss=0.0040 +[score] benign in 2.1s +[score] attack in 1.5s +[saved] artifacts/baselines/anomaly_transformer_2026_04_29/reverse_cross_seed43.json +[best agg=max] AUROC=0.6797 AUPRC=0.5524 + max AUROC=0.6797 AUPRC=0.5524 + mean AUROC=0.4566 AUPRC=0.4307 + p90 AUROC=0.3843 AUPRC=0.3849 + median AUROC=0.3337 AUPRC=0.4061 +[done] elapsed=247s artifacts/baselines/anomaly_transformer_2026_04_29/reverse_cross_seed43.json +=== protocol=reverse_cross seed=44 epochs=15 batch=128 === +[run] anomaly_transformer protocol=reverse_cross seed=44 +[data] flows=/home/chy/mambafortrafficmodeling/datasets/cicddos2019/processed/flows.parquet packets_source=/home/chy/mambafortrafficmodeling/datasets/cicddos2019/processed/full_store +[data] using external flow features D=20 +[data] rows total=8,993,376 keep len>=2: 8,986,875 +[data] benign=93,207 attack=8,893,668 -> train=74,565 val=18,642 +[data] train_flows=10,000 val=10,000 attack=6,772 D=9 device=cuda +[model] params=305,941 + [epoch 5/15] rec_loss=0.0225 (7.9s elapsed) + [epoch 10/15] rec_loss=0.0077 (15.2s elapsed) + [epoch 15/15] rec_loss=0.0040 (22.7s elapsed) +[train] 22.7s, final rec_loss=0.0040 +[score] benign in 2.1s +[score] attack in 1.5s +[saved] artifacts/baselines/anomaly_transformer_2026_04_29/reverse_cross_seed44.json +[best agg=max] AUROC=0.5801 AUPRC=0.6040 + max AUROC=0.5801 AUPRC=0.6040 + median AUROC=0.4205 AUPRC=0.3476 + mean AUROC=0.3775 AUPRC=0.4047 + p90 AUROC=0.2758 AUPRC=0.3123 +[done] elapsed=244s artifacts/baselines/anomaly_transformer_2026_04_29/reverse_cross_seed44.json +ALL DONE diff --git a/artifacts/baselines/anomaly_transformer_2026_04_29/reverse_cross_seed42.json b/artifacts/baselines/anomaly_transformer_2026_04_29/reverse_cross_seed42.json new file mode 100644 index 0000000..d932f87 --- /dev/null +++ b/artifacts/baselines/anomaly_transformer_2026_04_29/reverse_cross_seed42.json @@ -0,0 +1,288 @@ +{ + "method": "anomaly_transformer", + "protocol": "reverse_cross", + "seed": 42, + "model_dir": "/home/chy/mambafortrafficmodeling/artifacts/phase25_multiseed_2026_04_25/cicddos2019_lambda0p3_seed42", + "n_train": 10000, + "n_val": 10000, + "n_atk": 6772, + "D": 9, + "epochs": 15, + "lr": 0.0001, 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index 0000000..5e11c2f Binary files /dev/null and b/artifacts/baselines/anomaly_transformer_2026_04_29/reverse_cross_seed42.npz differ diff --git a/artifacts/baselines/anomaly_transformer_2026_04_29/reverse_cross_seed43.json b/artifacts/baselines/anomaly_transformer_2026_04_29/reverse_cross_seed43.json new file mode 100644 index 0000000..aade348 --- /dev/null +++ b/artifacts/baselines/anomaly_transformer_2026_04_29/reverse_cross_seed43.json @@ -0,0 +1,288 @@ +{ + "method": "anomaly_transformer", + "protocol": "reverse_cross", + "seed": 43, + "model_dir": "/home/chy/mambafortrafficmodeling/artifacts/phase25_multiseed_2026_04_25/cicddos2019_lambda0p3_seed43", + "n_train": 10000, + "n_val": 10000, + "n_atk": 6772, + "D": 9, + "epochs": 15, + "lr": 0.0001, + "k_disc": 3.0, + "temperature": 50.0, + "d_model": 128, + "t_train_sec": 23.63, + "loss_first_last": [ + 0.14317506532880325, + 0.003993322554079792 + ], + "overall_by_agg": { + "mean": { + "auroc": 0.4565637551683402, + "auprc": 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a/artifacts/baselines/anomaly_transformer_2026_04_29/reverse_cross_seed44.json b/artifacts/baselines/anomaly_transformer_2026_04_29/reverse_cross_seed44.json new file mode 100644 index 0000000..ead886b --- /dev/null +++ b/artifacts/baselines/anomaly_transformer_2026_04_29/reverse_cross_seed44.json @@ -0,0 +1,288 @@ +{ + "method": "anomaly_transformer", + "protocol": "reverse_cross", + "seed": 44, + "model_dir": "/home/chy/mambafortrafficmodeling/artifacts/phase25_multiseed_2026_04_25/cicddos2019_lambda0p3_seed44", + "n_train": 10000, + "n_val": 10000, + "n_atk": 6772, + "D": 9, + "epochs": 15, + "lr": 0.0001, + "k_disc": 3.0, + "temperature": 50.0, + "d_model": 128, + "t_train_sec": 22.69, + "loss_first_last": [ + 0.14046703491218482, + 0.003991496433869381 + ], + "overall_by_agg": { + "mean": { + "auroc": 0.37751623597164796, + "auprc": 0.4047034772236886 + }, + "max": { + "auroc": 0.5801241952155936, + "auprc": 0.6039728234817969 + }, + "median": { + "auroc": 0.42049935026580026, + 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0.11286516516516515, + 0.06311981981981982 + ] + }, + "Web Attack - Brute Force": { + "n": 73, + "aurocs": [ + 0.7296356164383562, + 0.18231780821917806, + 0.1339671232876712 + ] + }, + "Web Attack - SQL Injection": { + "n": 13, + "aurocs": [ + 0.9339384615384616, + 0.5425076923076922, + 0.5136076923076923 + ] + }, + "Web Attack - XSS": { + "n": 18, + "aurocs": [ + 0.7209722222222222, + 0.1983611111111111, + 0.13924999999999998 + ] + } + } + }, + "baselines": { + "terminal_norm": { + "iscxtor_within": [ + 0.9945, + 0.0011 + ], + "cicids_within": [ + 0.9858, + 0.0021 + ], + "cicddos_within": [ + 0.996, + 0.001 + ], + "forward_cross": [ + 0.9109, + 0.0032 + ], + "reverse_cross": [ + 0.5999, + null + ] + } + } +} \ No newline at end of file diff --git a/artifacts/baselines/anomaly_transformer_2026_04_29/summary.md b/artifacts/baselines/anomaly_transformer_2026_04_29/summary.md new file mode 100644 index 0000000..2f51f7f --- /dev/null +++ b/artifacts/baselines/anomaly_transformer_2026_04_29/summary.md @@ -0,0 +1,69 @@ +# Anomaly-Transformer (ICLR 2022) Baseline — On Our 5-Protocol Layout + +Date: 2026-04-29 + +Method: ICLR 2022 Anomaly-Transformer (association-discrepancy minimax). Vendored model class from `baselines/Anomaly-Transformer/model/AnomalyTransformer.py`; training + scoring loop reimplemented to match our protocol (input shape [B, T=64, D=9] = our z-scored packet sequences, same train/val/attack splits as eval_new_scores.py). +Hyperparams: d_model=128, n_heads=4, e_layers=3, batch=128, lr=1e-4, k_disc=3.0, temperature=50.0, epochs=15. +Score: per-position softmax(-association_KL · T) · MSE(rec, x), then aggregated per flow (mean / max / median / p90). + +## Headline AUROC (best aggregator per protocol, 3-seed mean ± std) + +| Protocol | terminal_norm (Unified_CFM) | **AT (ours)** | abs AUROC | best agg | Δ vs terminal | +|---|---:|---:|---:|---|---:| +| ISCXTor2016 within | 0.9945 ± 0.0011 | **0.4122 ± 0.0503** | 0.5878 ± 0.0503 | `p90` | -0.5823 | +| CICIDS2017 within (σ=0.6) | 0.9858 ± 0.0021 | **0.5009 ± 0.2107** | 0.6617 ± 0.0722 | `mean` | -0.4849 | +| CICDDoS2019 within | 0.9960 ± 0.0010 | **0.4777 ± 0.3325** | 0.7491 ± 0.1351 | `median` | -0.5183 | +| IDS2017→DDoS2019 forward | 0.9109 ± 0.0032 | **0.5404 ± 0.1495** | 0.6285 ± 0.0061 | `median` | -0.3705 | +| DDoS2019→IDS2017 reverse | 0.5999 | **0.4767 ± 0.2597** | 0.7033 ± 0.0792 | `p90` | -0.1232 | + +## All aggregators (3-seed mean ± std) + +| Protocol | mean | max | median | p90 | +|---|---:|---:|---:|---:| +| ISCXTor2016 within | 0.4645 ± 0.0444 | 0.4850 ± 0.0622 | 0.4850 ± 0.0151 | 0.4122 ± 0.0503 | +| CICIDS2017 within (σ=0.6) | 0.5009 ± 0.2107 | 0.4976 ± 0.2196 | 0.3846 ± 0.1048 | 0.4376 ± 0.1424 | +| CICDDoS2019 within | 0.3873 ± 0.2358 | 0.3696 ± 0.2130 | 0.4777 ± 0.3325 | 0.4104 ± 0.2344 | +| IDS2017→DDoS2019 forward | 0.4150 ± 0.1354 | 0.4043 ± 0.1306 | 0.5404 ± 0.1495 | 0.4804 ± 0.1337 | +| DDoS2019→IDS2017 reverse | 0.5594 ± 0.2498 | 0.6923 ± 0.1190 | 0.4683 ± 0.1638 | 0.4767 ± 0.2597 | + +## Per-attack (forward + reverse, mean aggregator) + +### IDS2017→DDoS2019 forward +| attack | n | AT AUROC mean ± std | +|---|---:|---:| +| `DrDoS_DNS` | 588 | 0.2275 ± 0.1007 | +| `DrDoS_LDAP` | 588 | 0.2225 ± 0.0998 | +| `DrDoS_MSSQL` | 588 | 0.3589 ± 0.2342 | +| `DrDoS_NTP` | 588 | 0.6040 ± 0.0974 | +| `DrDoS_NetBIOS` | 588 | 0.3829 ± 0.2466 | +| `DrDoS_SNMP` | 588 | 0.2285 ± 0.1096 | +| `DrDoS_SSDP` | 588 | 0.5341 ± 0.0897 | +| `DrDoS_UDP` | 588 | 0.5394 ± 0.0963 | +| `LDAP` | 588 | 0.2258 ± 0.1051 | +| `MSSQL` | 588 | 0.3645 ± 0.2355 | +| `NetBIOS` | 588 | 0.3938 ± 0.2537 | +| `Portmap` | 588 | 0.3892 ± 0.2542 | +| `Syn` | 588 | 0.5439 ± 0.3495 | +| `TFTP` | 588 | 0.5704 ± 0.0504 | +| `UDP` | 588 | 0.5444 ± 0.0917 | +| `UDPLag` | 588 | 0.4938 ± 0.3530 | +| `WebDDoS` | 438 | 0.4359 ± 0.0502 | + +### DDoS2019→IDS2017 reverse +| attack | n | AT AUROC mean ± std | +|---|---:|---:| +| `Botnet` | 666 | 0.6082 ± 0.2474 | +| `DDoS` | 666 | 0.7761 ± 0.1430 | +| `DoS GoldenEye` | 666 | 0.6108 ± 0.2441 | +| `DoS Hulk` | 666 | 0.6492 ± 0.2798 | +| `DoS Slowhttptest` | 666 | 0.6338 ± 0.2373 | +| `DoS Slowloris` | 666 | 0.5087 ± 0.2486 | +| `FTP-Patator` | 666 | 0.4187 ± 0.3190 | +| `Heartbleed` | 1 | 0.4974 ± 0.3748 | +| `Infiltration` | 7 | 0.3534 ± 0.3362 | +| `Infiltration - Portscan` | 666 | 0.5050 ± 0.4290 | +| `Portscan` | 666 | 0.6211 ± 0.5315 | +| `SSH-Patator` | 666 | 0.2915 ± 0.3533 | +| `Web Attack - Brute Force` | 73 | 0.3486 ± 0.3308 | +| `Web Attack - SQL Injection` | 13 | 0.6634 ± 0.2348 | +| `Web Attack - XSS` | 18 | 0.3529 ± 0.3202 | \ No newline at end of file diff --git a/artifacts/baselines/extract_ciciot2023.log b/artifacts/baselines/extract_ciciot2023.log new file mode 100644 index 0000000..6fa9fe5 --- /dev/null +++ b/artifacts/baselines/extract_ciciot2023.log @@ -0,0 +1,85 @@ +[discover] 34 pcap files across 34 labels + backdoor_malware 1 pcap(s) + browserhijacking 1 pcap(s) + commandinjection 1 pcap(s) + ddos-ack_fragmentation 1 pcap(s) + ddos-http_flood 1 pcap(s) + ddos-icmp_flood 1 pcap(s) + ddos-icmp_fragmentation 1 pcap(s) + ddos-pshack_flood 1 pcap(s) + ddos-rstfinflood 1 pcap(s) + ddos-slowloris 1 pcap(s) + ddos-syn_flood 1 pcap(s) + ddos-synonymousip_flood 1 pcap(s) + ddos-tcp_flood 1 pcap(s) + ddos-udp_flood 1 pcap(s) + ddos-udp_fragmentation 1 pcap(s) + dictionarybruteforce 1 pcap(s) + dns_spoofing 1 pcap(s) + dos-http_flood 1 pcap(s) + dos-syn_flood 1 pcap(s) + dos-tcp_flood 1 pcap(s) + dos-udp_flood 1 pcap(s) + mirai-greeth_flood 1 pcap(s) + mirai-greip_flood 1 pcap(s) + mirai-udpplain 1 pcap(s) + mitm-arpspoofing 1 pcap(s) + normal 1 pcap(s) + recon-hostdiscovery 1 pcap(s) + recon-osscan 1 pcap(s) + recon-pingsweep 1 pcap(s) + recon-portscan 1 pcap(s) + sqlinjection 1 pcap(s) + uploading_attack 1 pcap(s) + vulnerabilityscan 1 pcap(s) + xss 1 pcap(s) +[extract_labeled_pcaps] n_pcaps=34 T_full=256 extra_cols=('class_folder',) + backdoor_malware Backdoor_Malware.pcap extra={'class_folder': 'Backdoor_Malware'} + normal BenignTraffic.pcap extra={'class_folder': 'Benign_Final'} + browserhijacking BrowserHijacking.pcap extra={'class_folder': 'BrowserHijacking'} + commandinjection CommandInjection.pcap extra={'class_folder': 'CommandInjection'} + ddos-ack_fragmentation DDoS-ACK_Fragmentation.pcap extra={'class_folder': 'DDoS-ACK_Fragmentation'} + ddos-http_flood DDoS-HTTP_Flood-.pcap extra={'class_folder': 'DDoS-HTTP_Flood'} + ddos-icmp_flood DDoS-ICMP_Flood.pcap extra={'class_folder': 'DDoS-ICMP_Flood'} + ddos-icmp_fragmentation DDoS-ICMP_Fragmentation.pcap extra={'class_folder': 'DDoS-ICMP_Fragmentation'} + ddos-pshack_flood DDoS-PSHACK_Flood.pcap extra={'class_folder': 'DDoS-PSHACK_Flood'} + ddos-rstfinflood DDoS-RSTFINFlood.pcap extra={'class_folder': 'DDoS-RSTFINFlood'} + ... (24 more) +[extract_labeled_pcaps] running 34 pcap(s) with 4 worker(s) +[extract_labeled_pcaps] sharded output enabled: datasets/ciciot2023/processed/full_store shard_size=100,000 +[extract_labeled_pcaps] worker spool=/home/chy/mambafortrafficmodeling/datasets/ciciot2023/processed/.full_store.spool._q0mmt_4 flush_size=10,000 +[pcap:Backdoor_Malware.pcap] label=backdoor_malware 29,155 pkts → 2,325 flows in 0.6s (0.05M pkts/s) +[pcap:CommandInjection.pcap] label=commandinjection 49,515 pkts → 3,784 flows in 1.0s (0.05M pkts/s) +[pcap:BrowserHijacking.pcap] label=browserhijacking 55,181 pkts → 2,800 flows in 1.0s (0.05M pkts/s) +[pcap:BenignTraffic.pcap] label=normal 2,000,000 pkts → 130,266 flows in 36.7s (0.05M pkts/s) +[pcap:DDoS-HTTP_Flood-.pcap] label=ddos-http_flood 2,000,000 pkts → 424,632 flows in 51.0s (0.04M pkts/s) +[pcap:DDoS-ICMP_Fragmentation.pcap] label=ddos-icmp_fragmentation 91,881 pkts → 15,315 flows in 24.2s (0.00M pkts/s) +[pcap:DDoS-ACK_Fragmentation.pcap] label=ddos-ack_fragmentation 1,421,801 pkts → 1,199,853 flows in 72.1s (0.02M pkts/s) +[pcap:DDoS-PSHACK_Flood.pcap] label=ddos-pshack_flood 2,000,000 pkts → 472,916 flows in 47.0s (0.04M pkts/s) +[pcap:DDoS-SYN_Flood.pcap] label=ddos-syn_flood 2,000,000 pkts → 445,305 flows in 46.9s (0.04M pkts/s) +[pcap:DDoS-SlowLoris.pcap] label=ddos-slowloris 2,000,000 pkts → 170,596 flows in 40.9s (0.05M pkts/s) +[pcap:DDoS-RSTFINFlood.pcap] label=ddos-rstfinflood 2,000,000 pkts → 1,989,762 flows in 87.8s (0.02M pkts/s) +[pcap:DDoS-SynonymousIP_Flood.pcap] label=ddos-synonymousip_flood 2,000,000 pkts → 66,126 flows in 36.4s (0.05M pkts/s) +[pcap:DDoS-UDP_Flood.pcap] label=ddos-udp_flood 2,000,000 pkts → 3,021 flows in 22.8s (0.09M pkts/s) +[pcap:DDoS-UDP_Fragmentation.pcap] label=ddos-udp_fragmentation 1,141,302 pkts → 10,202 flows in 26.7s (0.04M pkts/s) +[pcap:DDoS-TCP_Flood.pcap] label=ddos-tcp_flood 2,000,000 pkts → 459,439 flows in 44.7s (0.04M pkts/s) +[pcap:DictionaryBruteForce.pcap] label=dictionarybruteforce 121,861 pkts → 7,910 flows in 2.3s (0.05M pkts/s) +[pcap:DNS_Spoofing.pcap] label=dns_spoofing 1,717,375 pkts → 83,761 flows in 29.3s (0.06M pkts/s) +[pcap:DoS-SYN_Flood.pcap] label=dos-syn_flood 2,000,000 pkts → 332,245 flows in 45.3s (0.04M pkts/s) +[pcap:DoS-HTTP_Flood.pcap] label=dos-http_flood 2,000,000 pkts → 426,432 flows in 51.0s (0.04M pkts/s) +[pcap:DoS-TCP_Flood.pcap] label=dos-tcp_flood 2,000,000 pkts → 404,258 flows in 46.6s (0.04M pkts/s) +[pcap:DoS-UDP_Flood.pcap] label=dos-udp_flood 2,000,000 pkts → 67,459 flows in 33.9s (0.06M pkts/s) +[pcap:DDoS-ICMP_Flood.pcap] label=ddos-icmp_flood 78,905 pkts → 12,441 flows in 267.1s (0.00M pkts/s) +[pcap:MITM-ArpSpoofing.pcap] label=mitm-arpspoofing 2,000,000 pkts → 55,312 flows in 32.1s (0.06M pkts/s) +[pcap:Mirai-udpplain.pcap] label=mirai-udpplain 2,000,000 pkts → 4,351 flows in 21.6s (0.09M pkts/s) +[pcap:Recon-HostDiscovery.pcap] label=recon-hostdiscovery 1,253,455 pkts → 663,947 flows in 39.1s (0.03M pkts/s) +[pcap:Recon-PingSweep.pcap] label=recon-pingsweep 19,361 pkts → 1,955 flows in 0.4s (0.05M pkts/s) +[pcap:Mirai-greeth_flood.pcap] label=mirai-greeth_flood 83,075 pkts → 5,465 flows in 60.5s (0.00M pkts/s) +[pcap:SqlInjection.pcap] label=sqlinjection 49,185 pkts → 6,693 flows in 1.0s (0.05M pkts/s) +[pcap:Uploading_Attack.pcap] label=uploading_attack 10,826 pkts → 1,338 flows in 0.2s (0.05M pkts/s) +[pcap:Recon-OSScan.pcap] label=recon-osscan 948,173 pkts → 193,983 flows in 22.2s (0.04M pkts/s) +[pcap:XSS.pcap] label=xss 34,617 pkts → 3,209 flows in 0.7s (0.05M pkts/s) +[pcap:Mirai-greip_flood.pcap] label=mirai-greip_flood 99,556 pkts → 10,526 flows in 51.7s (0.00M pkts/s) +[pcap:Recon-PortScan.pcap] label=recon-portscan 794,588 pkts → 225,917 flows in 20.2s (0.04M pkts/s) +[pcap:VulnerabilityScan.pcap] label=vulnerabilityscan 2,000,000 pkts → 290,077 flows in 42.2s (0.05M pkts/s) +[extract_labeled_pcaps] wrote sharded store datasets/ciciot2023/processed/full_store diff --git a/artifacts/baselines/kitsune_2026_04_29/cicddos_within_seed42.json b/artifacts/baselines/kitsune_2026_04_29/cicddos_within_seed42.json new file mode 100644 index 0000000..cffa998 --- /dev/null +++ b/artifacts/baselines/kitsune_2026_04_29/cicddos_within_seed42.json @@ -0,0 +1,315 @@ +{ + "method": "kitsune_path_b", + "protocol": "cicddos_within", + "seed": 42, + "model_dir": "/home/chy/mambafortrafficmodeling/artifacts/phase25_multiseed_2026_04_25/cicddos2019_lambda0p3_seed42", + "n_train_flows": 5000, + "n_train_packets": 55918, + "n_val": 10000, + "n_atk": 20000, + "D": 9, + "fm_grace": 2000, + "ad_grace": 20000, + "max_ae_size": 10, + "t_train_sec": 2.34, + "overall_by_agg": { + "mean": { + "auroc": 0.4253447725, + "auprc": 0.6336203824974163 + }, + "max": { + "auroc": 0.3284644375, + "auprc": 0.5703262532476359 + }, + "median": { + "auroc": 0.474287345, + "auprc": 0.6604585372129211 + }, + "p90": { + "auroc": 0.345611965, + "auprc": 0.5911061098185502 + } + }, + "per_class_by_agg": { + "mean": { + "DrDoS_DNS": { + "_n": 1136.0, + "auroc": 0.37572706866197186 + }, + "DrDoS_LDAP": { + "_n": 1152.0, + "auroc": 0.3659486545138889 + }, + "DrDoS_MSSQL": { + "_n": 1135.0, + "auroc": 0.3971305726872247 + }, + "DrDoS_NTP": { + "_n": 1171.0, + "auroc": 0.4506702391118702 + }, + "DrDoS_NetBIOS": { + "_n": 1166.0, + "auroc": 0.39661989708404805 + }, + "DrDoS_SNMP": { + "_n": 1086.0, + "auroc": 0.36687532228360953 + }, + "DrDoS_SSDP": { + "_n": 1092.0, + "auroc": 0.4189297619047619 + }, + "DrDoS_UDP": { + "_n": 1109.0, + "auroc": 0.41150360685302073 + }, + "LDAP": { + "_n": 1105.0, + "auroc": 0.36997108597285067 + }, + "MSSQL": { + "_n": 1184.0, + "auroc": 0.39456355574324325 + }, + "NetBIOS": { + "_n": 1539.0, + "auroc": 0.4013035087719298 + }, + "Portmap": { + "_n": 417.0, + "auroc": 0.4142306954436451 + }, + "Syn": { + "_n": 3361.0, + "auroc": 0.5423447932163046 + }, + "TFTP": { + "_n": 1106.0, + "auroc": 0.41400976491862573 + }, + "UDP": { + "_n": 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a/artifacts/baselines/kitsune_2026_04_29/forward_cross_seed44.json b/artifacts/baselines/kitsune_2026_04_29/forward_cross_seed44.json new file mode 100644 index 0000000..b59e568 --- /dev/null +++ b/artifacts/baselines/kitsune_2026_04_29/forward_cross_seed44.json @@ -0,0 +1,315 @@ +{ + "method": "kitsune_path_b", + "protocol": "forward_cross", + "seed": 44, + "model_dir": "/home/chy/mambafortrafficmodeling/artifacts/phase25_sigma06_multiseed_2026_04_25/cicids2017_lambda0p3_sigma0p6_seed44", + "n_train_flows": 5000, + "n_train_packets": 59671, + "n_val": 10000, + "n_atk": 9846, + "D": 9, + "fm_grace": 2000, + "ad_grace": 20000, + "max_ae_size": 10, + "t_train_sec": 2.47, + "overall_by_agg": { + "mean": { + "auroc": 0.3776255789152955, + "auprc": 0.41646982210819894 + }, + "max": { + "auroc": 0.30400732277066833, + "auprc": 0.3757284502898286 + }, + "median": { + "auroc": 0.43596635181799714, + "auprc": 0.45443162010700605 + }, + "p90": { + "auroc": 0.3205092575665245, + "auprc": 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"auroc": 0.4218748299319728 + }, + "NetBIOS": { + "_n": 588.0, + "auroc": 0.5099818027210885 + }, + "Portmap": { + "_n": 588.0, + "auroc": 0.5303285714285714 + }, + "Syn": { + "_n": 588.0, + "auroc": 0.43281292517006803 + }, + "TFTP": { + "_n": 588.0, + "auroc": 0.40089574829931973 + }, + "UDP": { + "_n": 588.0, + "auroc": 0.4549595238095238 + }, + "UDPLag": { + "_n": 588.0, + "auroc": 0.3876406462585034 + }, + "WebDDoS": { + "_n": 438.0, + "auroc": 0.47365764840182645 + } + }, + "p90": { + "DrDoS_DNS": { + "_n": 588.0, + "auroc": 0.2131952380952381 + }, + "DrDoS_LDAP": { + "_n": 588.0, + "auroc": 0.22612780612244898 + }, + "DrDoS_MSSQL": { + "_n": 588.0, + "auroc": 0.26664659863945583 + }, + "DrDoS_NTP": { + "_n": 588.0, + "auroc": 0.502262074829932 + }, + "DrDoS_NetBIOS": { + "_n": 588.0, + "auroc": 0.35126581632653064 + }, + "DrDoS_SNMP": { + "_n": 588.0, + "auroc": 0.23263112244897957 + }, + "DrDoS_SSDP": { + "_n": 588.0, + "auroc": 0.36710943877551017 + }, + "DrDoS_UDP": { + "_n": 588.0, + "auroc": 0.3656772108843538 + }, + "LDAP": { + "_n": 588.0, + "auroc": 0.21010391156462585 + }, + "MSSQL": { + "_n": 588.0, + "auroc": 0.26292602040816326 + }, + "NetBIOS": { + "_n": 588.0, + "auroc": 0.341993962585034 + }, + "Portmap": { + "_n": 588.0, + "auroc": 0.3713362244897959 + }, + "Syn": { + "_n": 588.0, + "auroc": 0.3910445578231293 + }, + "TFTP": { + "_n": 588.0, + "auroc": 0.30850850340136055 + }, + "UDP": { + "_n": 588.0, + "auroc": 0.3603273809523809 + }, + "UDPLag": { + "_n": 588.0, + "auroc": 0.29751003401360543 + }, + "WebDDoS": { + "_n": 438.0, + "auroc": 0.400362100456621 + } + } + } +} \ No newline at end of file diff --git a/artifacts/baselines/kitsune_2026_04_29/forward_cross_seed44.npz b/artifacts/baselines/kitsune_2026_04_29/forward_cross_seed44.npz new file mode 100644 index 0000000..50a03fd Binary files /dev/null and b/artifacts/baselines/kitsune_2026_04_29/forward_cross_seed44.npz differ diff --git a/artifacts/baselines/kitsune_2026_04_29/iscxtor_within_seed42.json b/artifacts/baselines/kitsune_2026_04_29/iscxtor_within_seed42.json new file mode 100644 index 0000000..f015ba0 --- /dev/null +++ b/artifacts/baselines/kitsune_2026_04_29/iscxtor_within_seed42.json @@ -0,0 +1,59 @@ +{ + "method": "kitsune_path_b", + "protocol": "iscxtor_within", + "seed": 42, + "model_dir": "/home/chy/mambafortrafficmodeling/artifacts/phase25_multiseed_2026_04_25/iscxtor2016_lambda0p3_seed42", + "n_train_flows": 5000, + "n_train_packets": 76132, + "n_val": 10000, + "n_atk": 1312, + "D": 9, + "fm_grace": 2000, + "ad_grace": 20000, + "max_ae_size": 10, + "t_train_sec": 3.01, + "overall_by_agg": { + "mean": { + "auroc": 0.5571759146341463, + "auprc": 0.20673369025828442 + }, + "max": { + "auroc": 0.4999152057926829, + "auprc": 0.12686265960277793 + }, + "median": { + "auroc": 0.5495690929878049, + "auprc": 0.20363397238502046 + }, + "p90": { + "auroc": 0.5205511051829268, + "auprc": 0.1880725027647867 + } + }, + "per_class_by_agg": { + "mean": { + "tor": { + "_n": 1312.0, + "auroc": 0.5571759146341463 + } + }, + "max": { + "tor": { + "_n": 1312.0, + "auroc": 0.4999152057926829 + } + }, + "median": { + "tor": { + "_n": 1312.0, + "auroc": 0.5495690929878049 + } + }, + "p90": { + "tor": { + "_n": 1312.0, + "auroc": 0.5205511051829268 + } + } + } +} \ No newline at end of file diff --git a/artifacts/baselines/kitsune_2026_04_29/iscxtor_within_seed42.npz b/artifacts/baselines/kitsune_2026_04_29/iscxtor_within_seed42.npz new file mode 100644 index 0000000..0f383ee Binary files /dev/null and b/artifacts/baselines/kitsune_2026_04_29/iscxtor_within_seed42.npz differ diff --git a/artifacts/baselines/kitsune_2026_04_29/iscxtor_within_seed43.json b/artifacts/baselines/kitsune_2026_04_29/iscxtor_within_seed43.json new file mode 100644 index 0000000..e6cefbc --- /dev/null +++ b/artifacts/baselines/kitsune_2026_04_29/iscxtor_within_seed43.json @@ -0,0 +1,59 @@ +{ + "method": "kitsune_path_b", + "protocol": "iscxtor_within", + "seed": 43, + "model_dir": "/home/chy/mambafortrafficmodeling/artifacts/phase25_multiseed_2026_04_25/iscxtor2016_lambda0p3_seed43", + "n_train_flows": 5000, + "n_train_packets": 77094, + "n_val": 10000, + "n_atk": 1312, + "D": 9, + "fm_grace": 2000, + "ad_grace": 20000, + "max_ae_size": 10, + "t_train_sec": 3.02, + "overall_by_agg": { + "mean": { + "auroc": 0.5718415396341463, + "auprc": 0.2037778829445944 + }, + "max": { + "auroc": 0.5233626143292682, + "auprc": 0.19074944831826363 + }, + "median": { + "auroc": 0.5912113185975609, + "auprc": 0.20822037107590194 + }, + "p90": { + "auroc": 0.5464200076219512, + "auprc": 0.2281021363052374 + } + }, + "per_class_by_agg": { + "mean": { + "tor": { + "_n": 1312.0, + "auroc": 0.5718415396341463 + } + }, + "max": { + "tor": { + "_n": 1312.0, + "auroc": 0.5233626143292682 + } + }, + "median": { + "tor": { + "_n": 1312.0, + "auroc": 0.5912113185975609 + } + }, + "p90": { + "tor": { + "_n": 1312.0, + "auroc": 0.5464200076219512 + } + } + } +} \ No newline at end of file diff --git a/artifacts/baselines/kitsune_2026_04_29/iscxtor_within_seed43.npz b/artifacts/baselines/kitsune_2026_04_29/iscxtor_within_seed43.npz new file mode 100644 index 0000000..3e4f435 Binary files /dev/null and b/artifacts/baselines/kitsune_2026_04_29/iscxtor_within_seed43.npz differ diff --git a/artifacts/baselines/kitsune_2026_04_29/iscxtor_within_seed44.json b/artifacts/baselines/kitsune_2026_04_29/iscxtor_within_seed44.json new file mode 100644 index 0000000..e5474aa --- /dev/null +++ b/artifacts/baselines/kitsune_2026_04_29/iscxtor_within_seed44.json @@ -0,0 +1,59 @@ +{ + "method": "kitsune_path_b", + "protocol": "iscxtor_within", + "seed": 44, + "model_dir": "/home/chy/mambafortrafficmodeling/artifacts/phase25_multiseed_2026_04_25/iscxtor2016_lambda0p3_seed44", + "n_train_flows": 5000, + "n_train_packets": 75971, + "n_val": 10000, + "n_atk": 1312, + "D": 9, + "fm_grace": 2000, + "ad_grace": 20000, + "max_ae_size": 10, + "t_train_sec": 2.97, + "overall_by_agg": { + "mean": { + "auroc": 0.5187383384146341, + "auprc": 0.16885883583361713 + }, + "max": { + "auroc": 0.4652036204268292, + "auprc": 0.10760505657769281 + }, + "median": { + "auroc": 0.5552233231707318, + "auprc": 0.17538497368324046 + }, + "p90": { + "auroc": 0.49095800304878046, + "auprc": 0.15325191734494964 + } + }, + "per_class_by_agg": { + "mean": { + "tor": { + "_n": 1312.0, + "auroc": 0.5187383384146341 + } + }, + "max": { + "tor": { + "_n": 1312.0, + "auroc": 0.4652036204268292 + } + }, + "median": { + "tor": { + "_n": 1312.0, + "auroc": 0.5552233231707318 + } + }, + "p90": { + "tor": { + "_n": 1312.0, + "auroc": 0.49095800304878046 + } + } + } +} \ No newline at end of file diff --git a/artifacts/baselines/kitsune_2026_04_29/iscxtor_within_seed44.npz b/artifacts/baselines/kitsune_2026_04_29/iscxtor_within_seed44.npz new file mode 100644 index 0000000..409ac3e Binary files /dev/null and b/artifacts/baselines/kitsune_2026_04_29/iscxtor_within_seed44.npz differ diff --git a/artifacts/baselines/kitsune_2026_04_29/master.log b/artifacts/baselines/kitsune_2026_04_29/master.log new file mode 100644 index 0000000..70dd8ae --- /dev/null +++ b/artifacts/baselines/kitsune_2026_04_29/master.log @@ -0,0 +1,405 @@ +=== protocol=iscxtor_within seed=42 n_train_cap=5000 === +[run] kitsune protocol=iscxtor_within seed=42 +[run] using packet stats from /home/chy/mambafortrafficmodeling/artifacts/phase25_multiseed_2026_04_25/iscxtor2016_lambda0p3_seed42/model.pt +[data] flows=/home/chy/mambafortrafficmodeling/datasets/iscxtor2016/processed/flows.parquet packets_source=/home/chy/mambafortrafficmodeling/datasets/iscxtor2016/processed/packets.npz +[data] using external flow features D=20 +[data] rows total=103,079 keep len>=2: 66,189 +[data] benign=64,877 attack=1,312 -> train=51,901 val=10,000 +[data] train_flows=5,000 val=10,000 attack=1,312 D=9 +[data] train_flat packets=76,132 FM_grace=2000 AD_grace=20000 +Feature-Mapper: train-mode, Anomaly-Detector: off-mode +The Feature-Mapper found a mapping: 9 features to 1 autoencoders. +Feature-Mapper: execute-mode, Anomaly-Detector: train-mode +Feature-Mapper: execute-mode, Anomaly-Detector: execute-mode + [train] processed 50,000/76,132 last_rmse=0.0063 +[train] {'t_train_sec': 3.01, 'n_trained_packets': 76132} +[score] benign in 5.9s +[score] attack in 0.8s +[saved] artifacts/baselines/kitsune_2026_04_29/iscxtor_within_seed42.json +[saved] artifacts/baselines/kitsune_2026_04_29/iscxtor_within_seed42.npz +[best agg=mean] AUROC=0.5572 AUPRC=0.2067 + +=== overall AUROC by aggregator === + mean AUROC=0.5572 AUPRC=0.2067 + median AUROC=0.5496 AUPRC=0.2036 + p90 AUROC=0.5206 AUPRC=0.1881 + max AUROC=0.4999 AUPRC=0.1269 +[done] elapsed=18s artifacts/baselines/kitsune_2026_04_29/iscxtor_within_seed42.json +=== protocol=iscxtor_within seed=43 n_train_cap=5000 === +[run] kitsune protocol=iscxtor_within seed=43 +[run] using packet stats from /home/chy/mambafortrafficmodeling/artifacts/phase25_multiseed_2026_04_25/iscxtor2016_lambda0p3_seed43/model.pt +[data] flows=/home/chy/mambafortrafficmodeling/datasets/iscxtor2016/processed/flows.parquet packets_source=/home/chy/mambafortrafficmodeling/datasets/iscxtor2016/processed/packets.npz +[data] using external flow features D=20 +[data] rows total=103,079 keep len>=2: 66,189 +[data] benign=64,877 attack=1,312 -> train=51,901 val=10,000 +[data] train_flows=5,000 val=10,000 attack=1,312 D=9 +[data] train_flat packets=77,094 FM_grace=2000 AD_grace=20000 +Feature-Mapper: train-mode, Anomaly-Detector: off-mode +The Feature-Mapper found a mapping: 9 features to 1 autoencoders. +Feature-Mapper: execute-mode, Anomaly-Detector: train-mode +Feature-Mapper: execute-mode, Anomaly-Detector: execute-mode + [train] processed 50,000/77,094 last_rmse=0.0073 +[train] {'t_train_sec': 3.02, 'n_trained_packets': 77094} +[score] benign in 5.9s +[score] attack in 0.8s +[saved] artifacts/baselines/kitsune_2026_04_29/iscxtor_within_seed43.json +[saved] artifacts/baselines/kitsune_2026_04_29/iscxtor_within_seed43.npz +[best agg=median] AUROC=0.5912 AUPRC=0.2082 + +=== overall AUROC by aggregator === + median AUROC=0.5912 AUPRC=0.2082 + mean AUROC=0.5718 AUPRC=0.2038 + p90 AUROC=0.5464 AUPRC=0.2281 + max AUROC=0.5234 AUPRC=0.1907 +[done] elapsed=17s artifacts/baselines/kitsune_2026_04_29/iscxtor_within_seed43.json +=== protocol=iscxtor_within seed=44 n_train_cap=5000 === +[run] kitsune protocol=iscxtor_within seed=44 +[run] using packet stats from /home/chy/mambafortrafficmodeling/artifacts/phase25_multiseed_2026_04_25/iscxtor2016_lambda0p3_seed44/model.pt +[data] flows=/home/chy/mambafortrafficmodeling/datasets/iscxtor2016/processed/flows.parquet packets_source=/home/chy/mambafortrafficmodeling/datasets/iscxtor2016/processed/packets.npz +[data] using external flow features D=20 +[data] rows total=103,079 keep len>=2: 66,189 +[data] benign=64,877 attack=1,312 -> train=51,901 val=10,000 +[data] train_flows=5,000 val=10,000 attack=1,312 D=9 +[data] train_flat packets=75,971 FM_grace=2000 AD_grace=20000 +Feature-Mapper: train-mode, Anomaly-Detector: off-mode +The Feature-Mapper found a mapping: 9 features to 1 autoencoders. +Feature-Mapper: execute-mode, Anomaly-Detector: train-mode +Feature-Mapper: execute-mode, Anomaly-Detector: execute-mode + [train] processed 50,000/75,971 last_rmse=0.0220 +[train] {'t_train_sec': 2.97, 'n_trained_packets': 75971} +[score] benign in 5.9s +[score] attack in 0.8s +[saved] artifacts/baselines/kitsune_2026_04_29/iscxtor_within_seed44.json +[saved] artifacts/baselines/kitsune_2026_04_29/iscxtor_within_seed44.npz +[best agg=median] AUROC=0.5552 AUPRC=0.1754 + +=== overall AUROC by aggregator === + median AUROC=0.5552 AUPRC=0.1754 + mean AUROC=0.5187 AUPRC=0.1689 + p90 AUROC=0.4910 AUPRC=0.1533 + max AUROC=0.4652 AUPRC=0.1076 +[done] elapsed=18s artifacts/baselines/kitsune_2026_04_29/iscxtor_within_seed44.json +=== protocol=cicids_within seed=42 n_train_cap=5000 === +[run] kitsune protocol=cicids_within seed=42 +[run] using packet stats from /home/chy/mambafortrafficmodeling/artifacts/phase25_sigma06_multiseed_2026_04_25/cicids2017_lambda0p3_sigma0p6_seed42/model.pt +[data] flows=/home/chy/mambafortrafficmodeling/datasets/cicids2017/processed/flows.parquet packets_source=/home/chy/mambafortrafficmodeling/datasets/cicids2017/processed/packets.npz +[data] using external flow features D=20 +[data] rows total=2,025,564 keep len>=2: 2,017,180 +[data] benign=1,513,450 attack=30,000 -> train=1,210,760 val=10,000 +[data] train_flows=5,000 val=10,000 attack=30,000 D=9 +[data] train_flat packets=60,260 FM_grace=2000 AD_grace=20000 +Feature-Mapper: train-mode, Anomaly-Detector: off-mode +The Feature-Mapper found a mapping: 9 features to 1 autoencoders. +Feature-Mapper: execute-mode, Anomaly-Detector: train-mode +Feature-Mapper: execute-mode, Anomaly-Detector: execute-mode + [train] processed 50,000/60,260 last_rmse=0.0047 +[train] {'t_train_sec': 2.53, 'n_trained_packets': 60260} +[score] benign in 4.9s +[score] attack in 10.5s +[saved] artifacts/baselines/kitsune_2026_04_29/cicids_within_seed42.json +[saved] artifacts/baselines/kitsune_2026_04_29/cicids_within_seed42.npz +[best agg=mean] AUROC=0.7240 AUPRC=0.8873 + +=== overall AUROC by aggregator === + mean AUROC=0.7240 AUPRC=0.8873 + p90 AUROC=0.7135 AUPRC=0.8854 + max AUROC=0.7064 AUPRC=0.8642 + median AUROC=0.6684 AUPRC=0.8660 +[done] elapsed=124s artifacts/baselines/kitsune_2026_04_29/cicids_within_seed42.json +=== protocol=cicids_within seed=43 n_train_cap=5000 === +[run] kitsune protocol=cicids_within seed=43 +[run] using packet stats from /home/chy/mambafortrafficmodeling/artifacts/phase25_sigma06_multiseed_2026_04_25/cicids2017_lambda0p3_sigma0p6_seed43/model.pt +[data] flows=/home/chy/mambafortrafficmodeling/datasets/cicids2017/processed/flows.parquet packets_source=/home/chy/mambafortrafficmodeling/datasets/cicids2017/processed/packets.npz +[data] using external flow features D=20 +[data] rows total=2,025,564 keep len>=2: 2,017,180 +[data] benign=1,513,450 attack=30,000 -> train=1,210,760 val=10,000 +[data] train_flows=5,000 val=10,000 attack=30,000 D=9 +[data] train_flat packets=59,505 FM_grace=2000 AD_grace=20000 +Feature-Mapper: train-mode, Anomaly-Detector: off-mode +The Feature-Mapper found a mapping: 9 features to 1 autoencoders. +Feature-Mapper: execute-mode, Anomaly-Detector: train-mode +Feature-Mapper: execute-mode, Anomaly-Detector: execute-mode + [train] processed 50,000/59,505 last_rmse=0.0066 +[train] {'t_train_sec': 2.52, 'n_trained_packets': 59505} +[score] benign in 4.9s +[score] attack in 10.8s +[saved] artifacts/baselines/kitsune_2026_04_29/cicids_within_seed43.json +[saved] artifacts/baselines/kitsune_2026_04_29/cicids_within_seed43.npz +[best agg=mean] AUROC=0.6668 AUPRC=0.8607 + +=== overall AUROC by aggregator === + mean AUROC=0.6668 AUPRC=0.8607 + p90 AUROC=0.6561 AUPRC=0.8497 + max AUROC=0.6555 AUPRC=0.8278 + median AUROC=0.6345 AUPRC=0.8491 +[done] elapsed=125s artifacts/baselines/kitsune_2026_04_29/cicids_within_seed43.json +=== protocol=cicids_within seed=44 n_train_cap=5000 === +[run] kitsune protocol=cicids_within seed=44 +[run] using packet stats from /home/chy/mambafortrafficmodeling/artifacts/phase25_sigma06_multiseed_2026_04_25/cicids2017_lambda0p3_sigma0p6_seed44/model.pt +[data] flows=/home/chy/mambafortrafficmodeling/datasets/cicids2017/processed/flows.parquet packets_source=/home/chy/mambafortrafficmodeling/datasets/cicids2017/processed/packets.npz +[data] using external flow features D=20 +[data] rows total=2,025,564 keep len>=2: 2,017,180 +[data] benign=1,513,450 attack=30,000 -> train=1,210,760 val=10,000 +[data] train_flows=5,000 val=10,000 attack=30,000 D=9 +[data] train_flat packets=60,932 FM_grace=2000 AD_grace=20000 +Feature-Mapper: train-mode, Anomaly-Detector: off-mode +The Feature-Mapper found a mapping: 9 features to 1 autoencoders. +Feature-Mapper: execute-mode, Anomaly-Detector: train-mode +Feature-Mapper: execute-mode, Anomaly-Detector: execute-mode + [train] processed 50,000/60,932 last_rmse=0.0037 +[train] {'t_train_sec': 2.51, 'n_trained_packets': 60932} +[score] benign in 4.7s +[score] attack in 10.5s +[saved] artifacts/baselines/kitsune_2026_04_29/cicids_within_seed44.json +[saved] artifacts/baselines/kitsune_2026_04_29/cicids_within_seed44.npz +[best agg=max] AUROC=0.7170 AUPRC=0.8757 + +=== overall AUROC by aggregator === + max AUROC=0.7170 AUPRC=0.8757 + mean AUROC=0.7162 AUPRC=0.8882 + p90 AUROC=0.7142 AUPRC=0.8913 + median AUROC=0.6484 AUPRC=0.8600 +[done] elapsed=124s artifacts/baselines/kitsune_2026_04_29/cicids_within_seed44.json +=== protocol=cicddos_within seed=42 n_train_cap=5000 === +[run] kitsune protocol=cicddos_within seed=42 +[run] using packet stats from /home/chy/mambafortrafficmodeling/artifacts/phase25_multiseed_2026_04_25/cicddos2019_lambda0p3_seed42/model.pt +[data] flows=/home/chy/mambafortrafficmodeling/datasets/cicddos2019/processed/flows.parquet packets_source=/home/chy/mambafortrafficmodeling/datasets/cicddos2019/processed/full_store +[data] using external flow features D=20 +[data] rows total=8,993,376 keep len>=2: 8,986,875 +[data] benign=93,207 attack=20,000 -> train=74,565 val=18,642 +[data] train_flows=5,000 val=10,000 attack=20,000 D=9 +[data] train_flat packets=55,918 FM_grace=2000 AD_grace=20000 +Feature-Mapper: train-mode, Anomaly-Detector: off-mode +The Feature-Mapper found a mapping: 9 features to 1 autoencoders. +Feature-Mapper: execute-mode, Anomaly-Detector: train-mode +Feature-Mapper: execute-mode, Anomaly-Detector: execute-mode + [train] processed 50,000/55,918 last_rmse=0.0051 +[train] {'t_train_sec': 2.34, 'n_trained_packets': 55918} +[score] benign in 4.5s +[score] attack in 4.7s +[saved] artifacts/baselines/kitsune_2026_04_29/cicddos_within_seed42.json +[saved] artifacts/baselines/kitsune_2026_04_29/cicddos_within_seed42.npz +[best agg=median] AUROC=0.4743 AUPRC=0.6605 + +=== overall AUROC by aggregator === + median AUROC=0.4743 AUPRC=0.6605 + mean AUROC=0.4253 AUPRC=0.6336 + p90 AUROC=0.3456 AUPRC=0.5911 + max AUROC=0.3285 AUPRC=0.5703 +[done] elapsed=25s artifacts/baselines/kitsune_2026_04_29/cicddos_within_seed42.json +=== protocol=cicddos_within seed=43 n_train_cap=5000 === +[run] kitsune protocol=cicddos_within seed=43 +[run] using packet stats from /home/chy/mambafortrafficmodeling/artifacts/phase25_multiseed_2026_04_25/cicddos2019_lambda0p3_seed43/model.pt +[data] flows=/home/chy/mambafortrafficmodeling/datasets/cicddos2019/processed/flows.parquet packets_source=/home/chy/mambafortrafficmodeling/datasets/cicddos2019/processed/full_store +[data] using external flow features D=20 +[data] rows total=8,993,376 keep len>=2: 8,986,875 +[data] benign=93,207 attack=20,000 -> train=74,565 val=18,642 +[data] train_flows=5,000 val=10,000 attack=20,000 D=9 +[data] train_flat packets=55,952 FM_grace=2000 AD_grace=20000 +Feature-Mapper: train-mode, Anomaly-Detector: off-mode +The Feature-Mapper found a mapping: 9 features to 1 autoencoders. +Feature-Mapper: execute-mode, Anomaly-Detector: train-mode +Feature-Mapper: execute-mode, Anomaly-Detector: execute-mode + [train] processed 50,000/55,952 last_rmse=0.0037 +[train] {'t_train_sec': 2.38, 'n_trained_packets': 55952} +[score] benign in 4.6s +[score] attack in 4.7s +[saved] artifacts/baselines/kitsune_2026_04_29/cicddos_within_seed43.json +[saved] artifacts/baselines/kitsune_2026_04_29/cicddos_within_seed43.npz +[best agg=median] AUROC=0.4720 AUPRC=0.6770 + +=== overall AUROC by aggregator === + median AUROC=0.4720 AUPRC=0.6770 + mean AUROC=0.4317 AUPRC=0.6532 + p90 AUROC=0.3624 AUPRC=0.6097 + max AUROC=0.3426 AUPRC=0.5776 +[done] elapsed=25s artifacts/baselines/kitsune_2026_04_29/cicddos_within_seed43.json +=== protocol=cicddos_within seed=44 n_train_cap=5000 === +[run] kitsune protocol=cicddos_within seed=44 +[run] using packet stats from /home/chy/mambafortrafficmodeling/artifacts/phase25_multiseed_2026_04_25/cicddos2019_lambda0p3_seed44/model.pt +[data] flows=/home/chy/mambafortrafficmodeling/datasets/cicddos2019/processed/flows.parquet packets_source=/home/chy/mambafortrafficmodeling/datasets/cicddos2019/processed/full_store +[data] using external flow features D=20 +[data] rows total=8,993,376 keep len>=2: 8,986,875 +[data] benign=93,207 attack=20,000 -> train=74,565 val=18,642 +[data] train_flows=5,000 val=10,000 attack=20,000 D=9 +[data] train_flat packets=53,770 FM_grace=2000 AD_grace=20000 +Feature-Mapper: train-mode, Anomaly-Detector: off-mode +The Feature-Mapper found a mapping: 9 features to 1 autoencoders. +Feature-Mapper: execute-mode, Anomaly-Detector: train-mode +Feature-Mapper: execute-mode, Anomaly-Detector: execute-mode + [train] processed 50,000/53,770 last_rmse=0.0086 +[train] {'t_train_sec': 2.33, 'n_trained_packets': 53770} +[score] benign in 4.4s +[score] attack in 4.6s +[saved] artifacts/baselines/kitsune_2026_04_29/cicddos_within_seed44.json +[saved] artifacts/baselines/kitsune_2026_04_29/cicddos_within_seed44.npz +[best agg=median] AUROC=0.4667 AUPRC=0.6562 + +=== overall AUROC by aggregator === + median AUROC=0.4667 AUPRC=0.6562 + mean AUROC=0.4615 AUPRC=0.6515 + p90 AUROC=0.3546 AUPRC=0.5842 + max AUROC=0.3366 AUPRC=0.5587 +[done] elapsed=24s artifacts/baselines/kitsune_2026_04_29/cicddos_within_seed44.json +=== protocol=forward_cross seed=42 n_train_cap=5000 === +[run] kitsune protocol=forward_cross seed=42 +[run] using packet stats from /home/chy/mambafortrafficmodeling/artifacts/phase25_sigma06_multiseed_2026_04_25/cicids2017_lambda0p3_sigma0p6_seed42/model.pt +[data] flows=/home/chy/mambafortrafficmodeling/datasets/cicids2017/processed/flows.parquet packets_source=/home/chy/mambafortrafficmodeling/datasets/cicids2017/processed/packets.npz +[data] using external flow features D=20 +[data] rows total=2,025,564 keep len>=2: 2,017,180 +[data] benign=1,513,450 attack=503,730 -> train=1,210,760 val=302,690 +[data] train_flows=5,000 val=10,000 attack=9,846 D=9 +[data] train_flat packets=62,210 FM_grace=2000 AD_grace=20000 +Feature-Mapper: train-mode, Anomaly-Detector: off-mode +The Feature-Mapper found a mapping: 9 features to 1 autoencoders. +Feature-Mapper: execute-mode, Anomaly-Detector: train-mode +Feature-Mapper: execute-mode, Anomaly-Detector: execute-mode + [train] processed 50,000/62,210 last_rmse=0.0155 +[train] {'t_train_sec': 2.53, 'n_trained_packets': 62210} +[score] benign in 4.3s +[score] attack in 2.4s +[saved] artifacts/baselines/kitsune_2026_04_29/forward_cross_seed42.json +[saved] artifacts/baselines/kitsune_2026_04_29/forward_cross_seed42.npz +[best agg=median] AUROC=0.4594 AUPRC=0.4715 + +=== overall AUROC by aggregator === + median AUROC=0.4594 AUPRC=0.4715 + mean AUROC=0.4227 AUPRC=0.4438 + p90 AUROC=0.3538 AUPRC=0.4088 + max AUROC=0.3330 AUPRC=0.3936 +[done] elapsed=138s artifacts/baselines/kitsune_2026_04_29/forward_cross_seed42.json +=== protocol=forward_cross seed=43 n_train_cap=5000 === +[run] kitsune protocol=forward_cross seed=43 +[run] using packet stats from /home/chy/mambafortrafficmodeling/artifacts/phase25_sigma06_multiseed_2026_04_25/cicids2017_lambda0p3_sigma0p6_seed43/model.pt +[data] flows=/home/chy/mambafortrafficmodeling/datasets/cicids2017/processed/flows.parquet packets_source=/home/chy/mambafortrafficmodeling/datasets/cicids2017/processed/packets.npz +[data] using external flow features D=20 +[data] rows total=2,025,564 keep len>=2: 2,017,180 +[data] benign=1,513,450 attack=503,730 -> train=1,210,760 val=302,690 +[data] train_flows=5,000 val=10,000 attack=9,846 D=9 +[data] train_flat packets=61,140 FM_grace=2000 AD_grace=20000 +Feature-Mapper: train-mode, Anomaly-Detector: off-mode +The Feature-Mapper found a mapping: 9 features to 1 autoencoders. +Feature-Mapper: execute-mode, Anomaly-Detector: train-mode +Feature-Mapper: execute-mode, Anomaly-Detector: execute-mode + [train] processed 50,000/61,140 last_rmse=0.0045 +[train] {'t_train_sec': 2.6, 'n_trained_packets': 61140} +[score] benign in 4.5s +[score] attack in 2.6s +[saved] artifacts/baselines/kitsune_2026_04_29/forward_cross_seed43.json +[saved] artifacts/baselines/kitsune_2026_04_29/forward_cross_seed43.npz +[best agg=mean] AUROC=0.5787 AUPRC=0.6131 + +=== overall AUROC by aggregator === + mean AUROC=0.5787 AUPRC=0.6131 + median AUROC=0.5762 AUPRC=0.6104 + p90 AUROC=0.5054 AUPRC=0.5641 + max AUROC=0.4778 AUPRC=0.4959 +[done] elapsed=139s artifacts/baselines/kitsune_2026_04_29/forward_cross_seed43.json +=== protocol=forward_cross seed=44 n_train_cap=5000 === +[run] kitsune protocol=forward_cross seed=44 +[run] using packet stats from /home/chy/mambafortrafficmodeling/artifacts/phase25_sigma06_multiseed_2026_04_25/cicids2017_lambda0p3_sigma0p6_seed44/model.pt +[data] flows=/home/chy/mambafortrafficmodeling/datasets/cicids2017/processed/flows.parquet packets_source=/home/chy/mambafortrafficmodeling/datasets/cicids2017/processed/packets.npz +[data] using external flow features D=20 +[data] rows total=2,025,564 keep len>=2: 2,017,180 +[data] benign=1,513,450 attack=503,730 -> train=1,210,760 val=302,690 +[data] train_flows=5,000 val=10,000 attack=9,846 D=9 +[data] train_flat packets=59,671 FM_grace=2000 AD_grace=20000 +Feature-Mapper: train-mode, Anomaly-Detector: off-mode +The Feature-Mapper found a mapping: 9 features to 1 autoencoders. +Feature-Mapper: execute-mode, Anomaly-Detector: train-mode +Feature-Mapper: execute-mode, Anomaly-Detector: execute-mode + [train] processed 50,000/59,671 last_rmse=0.0037 +[train] {'t_train_sec': 2.47, 'n_trained_packets': 59671} +[score] benign in 4.5s +[score] attack in 2.4s +[saved] artifacts/baselines/kitsune_2026_04_29/forward_cross_seed44.json +[saved] artifacts/baselines/kitsune_2026_04_29/forward_cross_seed44.npz +[best agg=median] AUROC=0.4360 AUPRC=0.4544 + +=== overall AUROC by aggregator === + median AUROC=0.4360 AUPRC=0.4544 + mean AUROC=0.3776 AUPRC=0.4165 + p90 AUROC=0.3205 AUPRC=0.3869 + max AUROC=0.3040 AUPRC=0.3757 +[done] elapsed=138s artifacts/baselines/kitsune_2026_04_29/forward_cross_seed44.json +=== protocol=reverse_cross seed=42 n_train_cap=5000 === +[run] kitsune protocol=reverse_cross seed=42 +[run] using packet stats from /home/chy/mambafortrafficmodeling/artifacts/phase25_multiseed_2026_04_25/cicddos2019_lambda0p3_seed42/model.pt +[data] flows=/home/chy/mambafortrafficmodeling/datasets/cicddos2019/processed/flows.parquet packets_source=/home/chy/mambafortrafficmodeling/datasets/cicddos2019/processed/full_store +[data] using external flow features D=20 +[data] rows total=8,993,376 keep len>=2: 8,986,875 +[data] benign=93,207 attack=8,893,668 -> train=74,565 val=18,642 +[data] train_flows=5,000 val=10,000 attack=6,772 D=9 +[data] train_flat packets=54,466 FM_grace=2000 AD_grace=20000 +Feature-Mapper: train-mode, Anomaly-Detector: off-mode +The Feature-Mapper found a mapping: 9 features to 1 autoencoders. +Feature-Mapper: execute-mode, Anomaly-Detector: train-mode +Feature-Mapper: execute-mode, Anomaly-Detector: execute-mode + [train] processed 50,000/54,466 last_rmse=0.0054 +[train] {'t_train_sec': 2.31, 'n_trained_packets': 54466} +[score] benign in 4.8s +[score] attack in 4.2s +[saved] artifacts/baselines/kitsune_2026_04_29/reverse_cross_seed42.json +[saved] artifacts/baselines/kitsune_2026_04_29/reverse_cross_seed42.npz +[best agg=max] AUROC=0.7549 AUPRC=0.6509 + +=== overall AUROC by aggregator === + max AUROC=0.7549 AUPRC=0.6509 + p90 AUROC=0.7233 AUPRC=0.6302 + mean AUROC=0.6985 AUPRC=0.5939 + median AUROC=0.6063 AUPRC=0.5104 +[done] elapsed=323s artifacts/baselines/kitsune_2026_04_29/reverse_cross_seed42.json +=== protocol=reverse_cross seed=43 n_train_cap=5000 === +[run] kitsune protocol=reverse_cross seed=43 +[run] using packet stats from /home/chy/mambafortrafficmodeling/artifacts/phase25_multiseed_2026_04_25/cicddos2019_lambda0p3_seed43/model.pt +[data] flows=/home/chy/mambafortrafficmodeling/datasets/cicddos2019/processed/flows.parquet packets_source=/home/chy/mambafortrafficmodeling/datasets/cicddos2019/processed/full_store +[data] using external flow features D=20 +[data] rows total=8,993,376 keep len>=2: 8,986,875 +[data] benign=93,207 attack=8,893,668 -> train=74,565 val=18,642 +[data] train_flows=5,000 val=10,000 attack=6,772 D=9 +[data] train_flat packets=54,901 FM_grace=2000 AD_grace=20000 +Feature-Mapper: train-mode, Anomaly-Detector: off-mode +The Feature-Mapper found a mapping: 9 features to 1 autoencoders. +Feature-Mapper: execute-mode, Anomaly-Detector: train-mode +Feature-Mapper: execute-mode, Anomaly-Detector: execute-mode + [train] processed 50,000/54,901 last_rmse=0.0020 +[train] {'t_train_sec': 2.34, 'n_trained_packets': 54901} +[score] benign in 4.8s +[score] attack in 4.3s +[saved] artifacts/baselines/kitsune_2026_04_29/reverse_cross_seed43.json +[saved] artifacts/baselines/kitsune_2026_04_29/reverse_cross_seed43.npz +[best agg=max] AUROC=0.7325 AUPRC=0.6396 + +=== overall AUROC by aggregator === + max AUROC=0.7325 AUPRC=0.6396 + p90 AUROC=0.6841 AUPRC=0.5933 + mean AUROC=0.6346 AUPRC=0.5435 + median AUROC=0.5685 AUPRC=0.4831 +[done] elapsed=260s artifacts/baselines/kitsune_2026_04_29/reverse_cross_seed43.json +=== protocol=reverse_cross seed=44 n_train_cap=5000 === +[run] kitsune protocol=reverse_cross seed=44 +[run] using packet stats from /home/chy/mambafortrafficmodeling/artifacts/phase25_multiseed_2026_04_25/cicddos2019_lambda0p3_seed44/model.pt +[data] flows=/home/chy/mambafortrafficmodeling/datasets/cicddos2019/processed/flows.parquet packets_source=/home/chy/mambafortrafficmodeling/datasets/cicddos2019/processed/full_store +[data] using external flow features D=20 +[data] rows total=8,993,376 keep len>=2: 8,986,875 +[data] benign=93,207 attack=8,893,668 -> train=74,565 val=18,642 +[data] train_flows=5,000 val=10,000 attack=6,772 D=9 +[data] train_flat packets=57,453 FM_grace=2000 AD_grace=20000 +Feature-Mapper: train-mode, Anomaly-Detector: off-mode +The Feature-Mapper found a mapping: 9 features to 1 autoencoders. +Feature-Mapper: execute-mode, Anomaly-Detector: train-mode +Feature-Mapper: execute-mode, Anomaly-Detector: execute-mode + [train] processed 50,000/57,453 last_rmse=0.0159 +[train] {'t_train_sec': 2.43, 'n_trained_packets': 57453} +[score] benign in 5.0s +[score] attack in 4.4s +[saved] artifacts/baselines/kitsune_2026_04_29/reverse_cross_seed44.json +[saved] artifacts/baselines/kitsune_2026_04_29/reverse_cross_seed44.npz +[best agg=max] AUROC=0.7573 AUPRC=0.6816 + +=== overall AUROC by aggregator === + max AUROC=0.7573 AUPRC=0.6816 + p90 AUROC=0.7113 AUPRC=0.6390 + mean AUROC=0.6710 AUPRC=0.5935 + median AUROC=0.5903 AUPRC=0.5093 +[done] elapsed=231s artifacts/baselines/kitsune_2026_04_29/reverse_cross_seed44.json diff --git a/artifacts/baselines/kitsune_2026_04_29/orchestrator.log b/artifacts/baselines/kitsune_2026_04_29/orchestrator.log new file mode 100644 index 0000000..d107f23 --- /dev/null +++ b/artifacts/baselines/kitsune_2026_04_29/orchestrator.log @@ -0,0 +1,406 @@ +=== protocol=iscxtor_within seed=42 n_train_cap=5000 === +[run] kitsune protocol=iscxtor_within seed=42 +[run] using packet stats from /home/chy/mambafortrafficmodeling/artifacts/phase25_multiseed_2026_04_25/iscxtor2016_lambda0p3_seed42/model.pt +[data] flows=/home/chy/mambafortrafficmodeling/datasets/iscxtor2016/processed/flows.parquet packets_source=/home/chy/mambafortrafficmodeling/datasets/iscxtor2016/processed/packets.npz +[data] using external flow features D=20 +[data] rows total=103,079 keep len>=2: 66,189 +[data] benign=64,877 attack=1,312 -> train=51,901 val=10,000 +[data] train_flows=5,000 val=10,000 attack=1,312 D=9 +[data] train_flat packets=76,132 FM_grace=2000 AD_grace=20000 +Feature-Mapper: train-mode, Anomaly-Detector: off-mode +The Feature-Mapper found a mapping: 9 features to 1 autoencoders. +Feature-Mapper: execute-mode, Anomaly-Detector: train-mode +Feature-Mapper: execute-mode, Anomaly-Detector: execute-mode + [train] processed 50,000/76,132 last_rmse=0.0063 +[train] {'t_train_sec': 3.01, 'n_trained_packets': 76132} +[score] benign in 5.9s +[score] attack in 0.8s +[saved] artifacts/baselines/kitsune_2026_04_29/iscxtor_within_seed42.json +[saved] artifacts/baselines/kitsune_2026_04_29/iscxtor_within_seed42.npz +[best agg=mean] AUROC=0.5572 AUPRC=0.2067 + +=== overall AUROC by aggregator === + mean AUROC=0.5572 AUPRC=0.2067 + median AUROC=0.5496 AUPRC=0.2036 + p90 AUROC=0.5206 AUPRC=0.1881 + max AUROC=0.4999 AUPRC=0.1269 +[done] elapsed=18s artifacts/baselines/kitsune_2026_04_29/iscxtor_within_seed42.json +=== protocol=iscxtor_within seed=43 n_train_cap=5000 === +[run] kitsune protocol=iscxtor_within seed=43 +[run] using packet stats from /home/chy/mambafortrafficmodeling/artifacts/phase25_multiseed_2026_04_25/iscxtor2016_lambda0p3_seed43/model.pt +[data] flows=/home/chy/mambafortrafficmodeling/datasets/iscxtor2016/processed/flows.parquet packets_source=/home/chy/mambafortrafficmodeling/datasets/iscxtor2016/processed/packets.npz +[data] using external flow features D=20 +[data] rows total=103,079 keep len>=2: 66,189 +[data] benign=64,877 attack=1,312 -> train=51,901 val=10,000 +[data] train_flows=5,000 val=10,000 attack=1,312 D=9 +[data] train_flat packets=77,094 FM_grace=2000 AD_grace=20000 +Feature-Mapper: train-mode, Anomaly-Detector: off-mode +The Feature-Mapper found a mapping: 9 features to 1 autoencoders. +Feature-Mapper: execute-mode, Anomaly-Detector: train-mode +Feature-Mapper: execute-mode, Anomaly-Detector: execute-mode + [train] processed 50,000/77,094 last_rmse=0.0073 +[train] {'t_train_sec': 3.02, 'n_trained_packets': 77094} +[score] benign in 5.9s +[score] attack in 0.8s +[saved] artifacts/baselines/kitsune_2026_04_29/iscxtor_within_seed43.json +[saved] artifacts/baselines/kitsune_2026_04_29/iscxtor_within_seed43.npz +[best agg=median] AUROC=0.5912 AUPRC=0.2082 + +=== overall AUROC by aggregator === + median AUROC=0.5912 AUPRC=0.2082 + mean AUROC=0.5718 AUPRC=0.2038 + p90 AUROC=0.5464 AUPRC=0.2281 + max AUROC=0.5234 AUPRC=0.1907 +[done] elapsed=17s artifacts/baselines/kitsune_2026_04_29/iscxtor_within_seed43.json +=== protocol=iscxtor_within seed=44 n_train_cap=5000 === +[run] kitsune protocol=iscxtor_within seed=44 +[run] using packet stats from /home/chy/mambafortrafficmodeling/artifacts/phase25_multiseed_2026_04_25/iscxtor2016_lambda0p3_seed44/model.pt +[data] flows=/home/chy/mambafortrafficmodeling/datasets/iscxtor2016/processed/flows.parquet packets_source=/home/chy/mambafortrafficmodeling/datasets/iscxtor2016/processed/packets.npz +[data] using external flow features D=20 +[data] rows total=103,079 keep len>=2: 66,189 +[data] benign=64,877 attack=1,312 -> train=51,901 val=10,000 +[data] train_flows=5,000 val=10,000 attack=1,312 D=9 +[data] train_flat packets=75,971 FM_grace=2000 AD_grace=20000 +Feature-Mapper: train-mode, Anomaly-Detector: off-mode +The Feature-Mapper found a mapping: 9 features to 1 autoencoders. +Feature-Mapper: execute-mode, Anomaly-Detector: train-mode +Feature-Mapper: execute-mode, Anomaly-Detector: execute-mode + [train] processed 50,000/75,971 last_rmse=0.0220 +[train] {'t_train_sec': 2.97, 'n_trained_packets': 75971} +[score] benign in 5.9s +[score] attack in 0.8s +[saved] artifacts/baselines/kitsune_2026_04_29/iscxtor_within_seed44.json +[saved] artifacts/baselines/kitsune_2026_04_29/iscxtor_within_seed44.npz +[best agg=median] AUROC=0.5552 AUPRC=0.1754 + +=== overall AUROC by aggregator === + median AUROC=0.5552 AUPRC=0.1754 + mean AUROC=0.5187 AUPRC=0.1689 + p90 AUROC=0.4910 AUPRC=0.1533 + max AUROC=0.4652 AUPRC=0.1076 +[done] elapsed=18s artifacts/baselines/kitsune_2026_04_29/iscxtor_within_seed44.json +=== protocol=cicids_within seed=42 n_train_cap=5000 === +[run] kitsune protocol=cicids_within seed=42 +[run] using packet stats from /home/chy/mambafortrafficmodeling/artifacts/phase25_sigma06_multiseed_2026_04_25/cicids2017_lambda0p3_sigma0p6_seed42/model.pt +[data] flows=/home/chy/mambafortrafficmodeling/datasets/cicids2017/processed/flows.parquet packets_source=/home/chy/mambafortrafficmodeling/datasets/cicids2017/processed/packets.npz +[data] using external flow features D=20 +[data] rows total=2,025,564 keep len>=2: 2,017,180 +[data] benign=1,513,450 attack=30,000 -> train=1,210,760 val=10,000 +[data] train_flows=5,000 val=10,000 attack=30,000 D=9 +[data] train_flat packets=60,260 FM_grace=2000 AD_grace=20000 +Feature-Mapper: train-mode, Anomaly-Detector: off-mode +The Feature-Mapper found a mapping: 9 features to 1 autoencoders. +Feature-Mapper: execute-mode, Anomaly-Detector: train-mode +Feature-Mapper: execute-mode, Anomaly-Detector: execute-mode + [train] processed 50,000/60,260 last_rmse=0.0047 +[train] {'t_train_sec': 2.53, 'n_trained_packets': 60260} +[score] benign in 4.9s +[score] attack in 10.5s +[saved] artifacts/baselines/kitsune_2026_04_29/cicids_within_seed42.json +[saved] artifacts/baselines/kitsune_2026_04_29/cicids_within_seed42.npz +[best agg=mean] AUROC=0.7240 AUPRC=0.8873 + +=== overall AUROC by aggregator === + mean AUROC=0.7240 AUPRC=0.8873 + p90 AUROC=0.7135 AUPRC=0.8854 + max AUROC=0.7064 AUPRC=0.8642 + median AUROC=0.6684 AUPRC=0.8660 +[done] elapsed=124s artifacts/baselines/kitsune_2026_04_29/cicids_within_seed42.json +=== protocol=cicids_within seed=43 n_train_cap=5000 === +[run] kitsune protocol=cicids_within seed=43 +[run] using packet stats from /home/chy/mambafortrafficmodeling/artifacts/phase25_sigma06_multiseed_2026_04_25/cicids2017_lambda0p3_sigma0p6_seed43/model.pt +[data] flows=/home/chy/mambafortrafficmodeling/datasets/cicids2017/processed/flows.parquet packets_source=/home/chy/mambafortrafficmodeling/datasets/cicids2017/processed/packets.npz +[data] using external flow features D=20 +[data] rows total=2,025,564 keep len>=2: 2,017,180 +[data] benign=1,513,450 attack=30,000 -> train=1,210,760 val=10,000 +[data] train_flows=5,000 val=10,000 attack=30,000 D=9 +[data] train_flat packets=59,505 FM_grace=2000 AD_grace=20000 +Feature-Mapper: train-mode, Anomaly-Detector: off-mode +The Feature-Mapper found a mapping: 9 features to 1 autoencoders. +Feature-Mapper: execute-mode, Anomaly-Detector: train-mode +Feature-Mapper: execute-mode, Anomaly-Detector: execute-mode + [train] processed 50,000/59,505 last_rmse=0.0066 +[train] {'t_train_sec': 2.52, 'n_trained_packets': 59505} +[score] benign in 4.9s +[score] attack in 10.8s +[saved] artifacts/baselines/kitsune_2026_04_29/cicids_within_seed43.json +[saved] artifacts/baselines/kitsune_2026_04_29/cicids_within_seed43.npz +[best agg=mean] AUROC=0.6668 AUPRC=0.8607 + +=== overall AUROC by aggregator === + mean AUROC=0.6668 AUPRC=0.8607 + p90 AUROC=0.6561 AUPRC=0.8497 + max AUROC=0.6555 AUPRC=0.8278 + median AUROC=0.6345 AUPRC=0.8491 +[done] elapsed=125s artifacts/baselines/kitsune_2026_04_29/cicids_within_seed43.json +=== protocol=cicids_within seed=44 n_train_cap=5000 === +[run] kitsune protocol=cicids_within seed=44 +[run] using packet stats from /home/chy/mambafortrafficmodeling/artifacts/phase25_sigma06_multiseed_2026_04_25/cicids2017_lambda0p3_sigma0p6_seed44/model.pt +[data] flows=/home/chy/mambafortrafficmodeling/datasets/cicids2017/processed/flows.parquet packets_source=/home/chy/mambafortrafficmodeling/datasets/cicids2017/processed/packets.npz +[data] using external flow features D=20 +[data] rows total=2,025,564 keep len>=2: 2,017,180 +[data] benign=1,513,450 attack=30,000 -> train=1,210,760 val=10,000 +[data] train_flows=5,000 val=10,000 attack=30,000 D=9 +[data] train_flat packets=60,932 FM_grace=2000 AD_grace=20000 +Feature-Mapper: train-mode, Anomaly-Detector: off-mode +The Feature-Mapper found a mapping: 9 features to 1 autoencoders. +Feature-Mapper: execute-mode, Anomaly-Detector: train-mode +Feature-Mapper: execute-mode, Anomaly-Detector: execute-mode + [train] processed 50,000/60,932 last_rmse=0.0037 +[train] {'t_train_sec': 2.51, 'n_trained_packets': 60932} +[score] benign in 4.7s +[score] attack in 10.5s +[saved] artifacts/baselines/kitsune_2026_04_29/cicids_within_seed44.json +[saved] artifacts/baselines/kitsune_2026_04_29/cicids_within_seed44.npz +[best agg=max] AUROC=0.7170 AUPRC=0.8757 + +=== overall AUROC by aggregator === + max AUROC=0.7170 AUPRC=0.8757 + mean AUROC=0.7162 AUPRC=0.8882 + p90 AUROC=0.7142 AUPRC=0.8913 + median AUROC=0.6484 AUPRC=0.8600 +[done] elapsed=124s artifacts/baselines/kitsune_2026_04_29/cicids_within_seed44.json +=== protocol=cicddos_within seed=42 n_train_cap=5000 === +[run] kitsune protocol=cicddos_within seed=42 +[run] using packet stats from /home/chy/mambafortrafficmodeling/artifacts/phase25_multiseed_2026_04_25/cicddos2019_lambda0p3_seed42/model.pt +[data] flows=/home/chy/mambafortrafficmodeling/datasets/cicddos2019/processed/flows.parquet packets_source=/home/chy/mambafortrafficmodeling/datasets/cicddos2019/processed/full_store +[data] using external flow features D=20 +[data] rows total=8,993,376 keep len>=2: 8,986,875 +[data] benign=93,207 attack=20,000 -> train=74,565 val=18,642 +[data] train_flows=5,000 val=10,000 attack=20,000 D=9 +[data] train_flat packets=55,918 FM_grace=2000 AD_grace=20000 +Feature-Mapper: train-mode, Anomaly-Detector: off-mode +The Feature-Mapper found a mapping: 9 features to 1 autoencoders. +Feature-Mapper: execute-mode, Anomaly-Detector: train-mode +Feature-Mapper: execute-mode, Anomaly-Detector: execute-mode + [train] processed 50,000/55,918 last_rmse=0.0051 +[train] {'t_train_sec': 2.34, 'n_trained_packets': 55918} +[score] benign in 4.5s +[score] attack in 4.7s +[saved] artifacts/baselines/kitsune_2026_04_29/cicddos_within_seed42.json +[saved] artifacts/baselines/kitsune_2026_04_29/cicddos_within_seed42.npz +[best agg=median] AUROC=0.4743 AUPRC=0.6605 + +=== overall AUROC by aggregator === + median AUROC=0.4743 AUPRC=0.6605 + mean AUROC=0.4253 AUPRC=0.6336 + p90 AUROC=0.3456 AUPRC=0.5911 + max AUROC=0.3285 AUPRC=0.5703 +[done] elapsed=25s artifacts/baselines/kitsune_2026_04_29/cicddos_within_seed42.json +=== protocol=cicddos_within seed=43 n_train_cap=5000 === +[run] kitsune protocol=cicddos_within seed=43 +[run] using packet stats from /home/chy/mambafortrafficmodeling/artifacts/phase25_multiseed_2026_04_25/cicddos2019_lambda0p3_seed43/model.pt +[data] flows=/home/chy/mambafortrafficmodeling/datasets/cicddos2019/processed/flows.parquet packets_source=/home/chy/mambafortrafficmodeling/datasets/cicddos2019/processed/full_store +[data] using external flow features D=20 +[data] rows total=8,993,376 keep len>=2: 8,986,875 +[data] benign=93,207 attack=20,000 -> train=74,565 val=18,642 +[data] train_flows=5,000 val=10,000 attack=20,000 D=9 +[data] train_flat packets=55,952 FM_grace=2000 AD_grace=20000 +Feature-Mapper: train-mode, Anomaly-Detector: off-mode +The Feature-Mapper found a mapping: 9 features to 1 autoencoders. +Feature-Mapper: execute-mode, Anomaly-Detector: train-mode +Feature-Mapper: execute-mode, Anomaly-Detector: execute-mode + [train] processed 50,000/55,952 last_rmse=0.0037 +[train] {'t_train_sec': 2.38, 'n_trained_packets': 55952} +[score] benign in 4.6s +[score] attack in 4.7s +[saved] artifacts/baselines/kitsune_2026_04_29/cicddos_within_seed43.json +[saved] artifacts/baselines/kitsune_2026_04_29/cicddos_within_seed43.npz +[best agg=median] AUROC=0.4720 AUPRC=0.6770 + +=== overall AUROC by aggregator === + median AUROC=0.4720 AUPRC=0.6770 + mean AUROC=0.4317 AUPRC=0.6532 + p90 AUROC=0.3624 AUPRC=0.6097 + max AUROC=0.3426 AUPRC=0.5776 +[done] elapsed=25s artifacts/baselines/kitsune_2026_04_29/cicddos_within_seed43.json +=== protocol=cicddos_within seed=44 n_train_cap=5000 === +[run] kitsune protocol=cicddos_within seed=44 +[run] using packet stats from /home/chy/mambafortrafficmodeling/artifacts/phase25_multiseed_2026_04_25/cicddos2019_lambda0p3_seed44/model.pt +[data] flows=/home/chy/mambafortrafficmodeling/datasets/cicddos2019/processed/flows.parquet packets_source=/home/chy/mambafortrafficmodeling/datasets/cicddos2019/processed/full_store +[data] using external flow features D=20 +[data] rows total=8,993,376 keep len>=2: 8,986,875 +[data] benign=93,207 attack=20,000 -> train=74,565 val=18,642 +[data] train_flows=5,000 val=10,000 attack=20,000 D=9 +[data] train_flat packets=53,770 FM_grace=2000 AD_grace=20000 +Feature-Mapper: train-mode, Anomaly-Detector: off-mode +The Feature-Mapper found a mapping: 9 features to 1 autoencoders. +Feature-Mapper: execute-mode, Anomaly-Detector: train-mode +Feature-Mapper: execute-mode, Anomaly-Detector: execute-mode + [train] processed 50,000/53,770 last_rmse=0.0086 +[train] {'t_train_sec': 2.33, 'n_trained_packets': 53770} +[score] benign in 4.4s +[score] attack in 4.6s +[saved] artifacts/baselines/kitsune_2026_04_29/cicddos_within_seed44.json +[saved] artifacts/baselines/kitsune_2026_04_29/cicddos_within_seed44.npz +[best agg=median] AUROC=0.4667 AUPRC=0.6562 + +=== overall AUROC by aggregator === + median AUROC=0.4667 AUPRC=0.6562 + mean AUROC=0.4615 AUPRC=0.6515 + p90 AUROC=0.3546 AUPRC=0.5842 + max AUROC=0.3366 AUPRC=0.5587 +[done] elapsed=24s artifacts/baselines/kitsune_2026_04_29/cicddos_within_seed44.json +=== protocol=forward_cross seed=42 n_train_cap=5000 === +[run] kitsune protocol=forward_cross seed=42 +[run] using packet stats from /home/chy/mambafortrafficmodeling/artifacts/phase25_sigma06_multiseed_2026_04_25/cicids2017_lambda0p3_sigma0p6_seed42/model.pt +[data] flows=/home/chy/mambafortrafficmodeling/datasets/cicids2017/processed/flows.parquet packets_source=/home/chy/mambafortrafficmodeling/datasets/cicids2017/processed/packets.npz +[data] using external flow features D=20 +[data] rows total=2,025,564 keep len>=2: 2,017,180 +[data] benign=1,513,450 attack=503,730 -> train=1,210,760 val=302,690 +[data] train_flows=5,000 val=10,000 attack=9,846 D=9 +[data] train_flat packets=62,210 FM_grace=2000 AD_grace=20000 +Feature-Mapper: train-mode, Anomaly-Detector: off-mode +The Feature-Mapper found a mapping: 9 features to 1 autoencoders. +Feature-Mapper: execute-mode, Anomaly-Detector: train-mode +Feature-Mapper: execute-mode, Anomaly-Detector: execute-mode + [train] processed 50,000/62,210 last_rmse=0.0155 +[train] {'t_train_sec': 2.53, 'n_trained_packets': 62210} +[score] benign in 4.3s +[score] attack in 2.4s +[saved] artifacts/baselines/kitsune_2026_04_29/forward_cross_seed42.json +[saved] artifacts/baselines/kitsune_2026_04_29/forward_cross_seed42.npz +[best agg=median] AUROC=0.4594 AUPRC=0.4715 + +=== overall AUROC by aggregator === + median AUROC=0.4594 AUPRC=0.4715 + mean AUROC=0.4227 AUPRC=0.4438 + p90 AUROC=0.3538 AUPRC=0.4088 + max AUROC=0.3330 AUPRC=0.3936 +[done] elapsed=138s artifacts/baselines/kitsune_2026_04_29/forward_cross_seed42.json +=== protocol=forward_cross seed=43 n_train_cap=5000 === +[run] kitsune protocol=forward_cross seed=43 +[run] using packet stats from /home/chy/mambafortrafficmodeling/artifacts/phase25_sigma06_multiseed_2026_04_25/cicids2017_lambda0p3_sigma0p6_seed43/model.pt +[data] flows=/home/chy/mambafortrafficmodeling/datasets/cicids2017/processed/flows.parquet packets_source=/home/chy/mambafortrafficmodeling/datasets/cicids2017/processed/packets.npz +[data] using external flow features D=20 +[data] rows total=2,025,564 keep len>=2: 2,017,180 +[data] benign=1,513,450 attack=503,730 -> train=1,210,760 val=302,690 +[data] train_flows=5,000 val=10,000 attack=9,846 D=9 +[data] train_flat packets=61,140 FM_grace=2000 AD_grace=20000 +Feature-Mapper: train-mode, Anomaly-Detector: off-mode +The Feature-Mapper found a mapping: 9 features to 1 autoencoders. +Feature-Mapper: execute-mode, Anomaly-Detector: train-mode +Feature-Mapper: execute-mode, Anomaly-Detector: execute-mode + [train] processed 50,000/61,140 last_rmse=0.0045 +[train] {'t_train_sec': 2.6, 'n_trained_packets': 61140} +[score] benign in 4.5s +[score] attack in 2.6s +[saved] artifacts/baselines/kitsune_2026_04_29/forward_cross_seed43.json +[saved] artifacts/baselines/kitsune_2026_04_29/forward_cross_seed43.npz +[best agg=mean] AUROC=0.5787 AUPRC=0.6131 + +=== overall AUROC by aggregator === + mean AUROC=0.5787 AUPRC=0.6131 + median AUROC=0.5762 AUPRC=0.6104 + p90 AUROC=0.5054 AUPRC=0.5641 + max AUROC=0.4778 AUPRC=0.4959 +[done] elapsed=139s artifacts/baselines/kitsune_2026_04_29/forward_cross_seed43.json +=== protocol=forward_cross seed=44 n_train_cap=5000 === +[run] kitsune protocol=forward_cross seed=44 +[run] using packet stats from /home/chy/mambafortrafficmodeling/artifacts/phase25_sigma06_multiseed_2026_04_25/cicids2017_lambda0p3_sigma0p6_seed44/model.pt +[data] flows=/home/chy/mambafortrafficmodeling/datasets/cicids2017/processed/flows.parquet packets_source=/home/chy/mambafortrafficmodeling/datasets/cicids2017/processed/packets.npz +[data] using external flow features D=20 +[data] rows total=2,025,564 keep len>=2: 2,017,180 +[data] benign=1,513,450 attack=503,730 -> train=1,210,760 val=302,690 +[data] train_flows=5,000 val=10,000 attack=9,846 D=9 +[data] train_flat packets=59,671 FM_grace=2000 AD_grace=20000 +Feature-Mapper: train-mode, Anomaly-Detector: off-mode +The Feature-Mapper found a mapping: 9 features to 1 autoencoders. +Feature-Mapper: execute-mode, Anomaly-Detector: train-mode +Feature-Mapper: execute-mode, Anomaly-Detector: execute-mode + [train] processed 50,000/59,671 last_rmse=0.0037 +[train] {'t_train_sec': 2.47, 'n_trained_packets': 59671} +[score] benign in 4.5s +[score] attack in 2.4s +[saved] artifacts/baselines/kitsune_2026_04_29/forward_cross_seed44.json +[saved] artifacts/baselines/kitsune_2026_04_29/forward_cross_seed44.npz +[best agg=median] AUROC=0.4360 AUPRC=0.4544 + +=== overall AUROC by aggregator === + median AUROC=0.4360 AUPRC=0.4544 + mean AUROC=0.3776 AUPRC=0.4165 + p90 AUROC=0.3205 AUPRC=0.3869 + max AUROC=0.3040 AUPRC=0.3757 +[done] elapsed=138s artifacts/baselines/kitsune_2026_04_29/forward_cross_seed44.json +=== protocol=reverse_cross seed=42 n_train_cap=5000 === +[run] kitsune protocol=reverse_cross seed=42 +[run] using packet stats from /home/chy/mambafortrafficmodeling/artifacts/phase25_multiseed_2026_04_25/cicddos2019_lambda0p3_seed42/model.pt +[data] flows=/home/chy/mambafortrafficmodeling/datasets/cicddos2019/processed/flows.parquet packets_source=/home/chy/mambafortrafficmodeling/datasets/cicddos2019/processed/full_store +[data] using external flow features D=20 +[data] rows total=8,993,376 keep len>=2: 8,986,875 +[data] benign=93,207 attack=8,893,668 -> train=74,565 val=18,642 +[data] train_flows=5,000 val=10,000 attack=6,772 D=9 +[data] train_flat packets=54,466 FM_grace=2000 AD_grace=20000 +Feature-Mapper: train-mode, Anomaly-Detector: off-mode +The Feature-Mapper found a mapping: 9 features to 1 autoencoders. +Feature-Mapper: execute-mode, Anomaly-Detector: train-mode +Feature-Mapper: execute-mode, Anomaly-Detector: execute-mode + [train] processed 50,000/54,466 last_rmse=0.0054 +[train] {'t_train_sec': 2.31, 'n_trained_packets': 54466} +[score] benign in 4.8s +[score] attack in 4.2s +[saved] artifacts/baselines/kitsune_2026_04_29/reverse_cross_seed42.json +[saved] artifacts/baselines/kitsune_2026_04_29/reverse_cross_seed42.npz +[best agg=max] AUROC=0.7549 AUPRC=0.6509 + +=== overall AUROC by aggregator === + max AUROC=0.7549 AUPRC=0.6509 + p90 AUROC=0.7233 AUPRC=0.6302 + mean AUROC=0.6985 AUPRC=0.5939 + median AUROC=0.6063 AUPRC=0.5104 +[done] elapsed=323s artifacts/baselines/kitsune_2026_04_29/reverse_cross_seed42.json +=== protocol=reverse_cross seed=43 n_train_cap=5000 === +[run] kitsune protocol=reverse_cross seed=43 +[run] using packet stats from /home/chy/mambafortrafficmodeling/artifacts/phase25_multiseed_2026_04_25/cicddos2019_lambda0p3_seed43/model.pt +[data] flows=/home/chy/mambafortrafficmodeling/datasets/cicddos2019/processed/flows.parquet packets_source=/home/chy/mambafortrafficmodeling/datasets/cicddos2019/processed/full_store +[data] using external flow features D=20 +[data] rows total=8,993,376 keep len>=2: 8,986,875 +[data] benign=93,207 attack=8,893,668 -> train=74,565 val=18,642 +[data] train_flows=5,000 val=10,000 attack=6,772 D=9 +[data] train_flat packets=54,901 FM_grace=2000 AD_grace=20000 +Feature-Mapper: train-mode, Anomaly-Detector: off-mode +The Feature-Mapper found a mapping: 9 features to 1 autoencoders. +Feature-Mapper: execute-mode, Anomaly-Detector: train-mode +Feature-Mapper: execute-mode, Anomaly-Detector: execute-mode + [train] processed 50,000/54,901 last_rmse=0.0020 +[train] {'t_train_sec': 2.34, 'n_trained_packets': 54901} +[score] benign in 4.8s +[score] attack in 4.3s +[saved] artifacts/baselines/kitsune_2026_04_29/reverse_cross_seed43.json +[saved] artifacts/baselines/kitsune_2026_04_29/reverse_cross_seed43.npz +[best agg=max] AUROC=0.7325 AUPRC=0.6396 + +=== overall AUROC by aggregator === + max AUROC=0.7325 AUPRC=0.6396 + p90 AUROC=0.6841 AUPRC=0.5933 + mean AUROC=0.6346 AUPRC=0.5435 + median AUROC=0.5685 AUPRC=0.4831 +[done] elapsed=260s artifacts/baselines/kitsune_2026_04_29/reverse_cross_seed43.json +=== protocol=reverse_cross seed=44 n_train_cap=5000 === +[run] kitsune protocol=reverse_cross seed=44 +[run] using packet stats from /home/chy/mambafortrafficmodeling/artifacts/phase25_multiseed_2026_04_25/cicddos2019_lambda0p3_seed44/model.pt +[data] flows=/home/chy/mambafortrafficmodeling/datasets/cicddos2019/processed/flows.parquet packets_source=/home/chy/mambafortrafficmodeling/datasets/cicddos2019/processed/full_store +[data] using external flow features D=20 +[data] rows total=8,993,376 keep len>=2: 8,986,875 +[data] benign=93,207 attack=8,893,668 -> train=74,565 val=18,642 +[data] train_flows=5,000 val=10,000 attack=6,772 D=9 +[data] train_flat packets=57,453 FM_grace=2000 AD_grace=20000 +Feature-Mapper: train-mode, Anomaly-Detector: off-mode +The Feature-Mapper found a mapping: 9 features to 1 autoencoders. +Feature-Mapper: execute-mode, Anomaly-Detector: train-mode +Feature-Mapper: execute-mode, Anomaly-Detector: execute-mode + [train] processed 50,000/57,453 last_rmse=0.0159 +[train] {'t_train_sec': 2.43, 'n_trained_packets': 57453} +[score] benign in 5.0s +[score] attack in 4.4s +[saved] artifacts/baselines/kitsune_2026_04_29/reverse_cross_seed44.json +[saved] artifacts/baselines/kitsune_2026_04_29/reverse_cross_seed44.npz +[best agg=max] AUROC=0.7573 AUPRC=0.6816 + +=== overall AUROC by aggregator === + max AUROC=0.7573 AUPRC=0.6816 + p90 AUROC=0.7113 AUPRC=0.6390 + mean AUROC=0.6710 AUPRC=0.5935 + median AUROC=0.5903 AUPRC=0.5093 +[done] elapsed=231s artifacts/baselines/kitsune_2026_04_29/reverse_cross_seed44.json +ALL DONE diff --git a/artifacts/baselines/kitsune_2026_04_29/reverse_cross_seed42.json b/artifacts/baselines/kitsune_2026_04_29/reverse_cross_seed42.json new file mode 100644 index 0000000..fac3796 --- /dev/null +++ b/artifacts/baselines/kitsune_2026_04_29/reverse_cross_seed42.json @@ -0,0 +1,283 @@ +{ + "method": "kitsune_path_b", + "protocol": "reverse_cross", + "seed": 42, + "model_dir": "/home/chy/mambafortrafficmodeling/artifacts/phase25_multiseed_2026_04_25/cicddos2019_lambda0p3_seed42", + "n_train_flows": 5000, + "n_train_packets": 54466, + "n_val": 10000, + "n_atk": 6772, + "D": 9, + "fm_grace": 2000, + "ad_grace": 20000, + "max_ae_size": 10, + "t_train_sec": 2.31, + "overall_by_agg": { + "mean": { + "auroc": 0.6984984494979326, + "auprc": 0.5938537447029827 + }, + "max": { + "auroc": 0.7549417528056704, + "auprc": 0.6508896460434106 + }, + "median": { + "auroc": 0.6062791568222091, + "auprc": 0.5104305280266268 + }, + "p90": { + "auroc": 0.7232628987005316, + "auprc": 0.6301781238590568 + } + }, + "per_class_by_agg": { + "mean": { + "Botnet": { + "_n": 666.0, + "auroc": 0.5547075825825826 + }, + "DDoS": { + "_n": 666.0, + "auroc": 0.8207093093093094 + }, + "DoS GoldenEye": { + "_n": 666.0, + "auroc": 0.7544175675675675 + }, + "DoS Hulk": { + "_n": 666.0, + "auroc": 0.7343030030030031 + }, + "DoS Slowhttptest": { + "_n": 666.0, + "auroc": 0.7101981981981981 + }, + "DoS Slowloris": { + "_n": 666.0, + "auroc": 0.6661138138138138 + }, + "FTP-Patator": { + "_n": 666.0, + "auroc": 0.6634243243243243 + }, + "Heartbleed": { + "_n": 1.0, + "auroc": 0.7665 + }, + "Infiltration": { + "_n": 7.0, + "auroc": 0.7007 + }, + "Infiltration - Portscan": { + "_n": 666.0, + "auroc": 0.6582857357357358 + }, + "Portscan": { + "_n": 666.0, + "auroc": 0.8860483483483483 + }, + "SSH-Patator": { + "_n": 666.0, + "auroc": 0.5690978978978979 + }, + "Web Attack - Brute Force": { + "_n": 73.0, + "auroc": 0.49512808219178084 + }, + "Web Attack - SQL Injection": { + "_n": 13.0, + "auroc": 0.5701076923076923 + }, + "Web Attack - XSS": { + "_n": 18.0, + "auroc": 0.4154833333333333 + } + }, + "max": { + "Botnet": { + "_n": 666.0, + "auroc": 0.5965653153153153 + }, + "DDoS": { + "_n": 666.0, + "auroc": 0.8667186936936938 + }, + "DoS GoldenEye": { + "_n": 666.0, + "auroc": 0.8125489489489489 + }, + "DoS Hulk": { + "_n": 666.0, + "auroc": 0.8022171171171173 + }, + "DoS Slowhttptest": { + "_n": 666.0, + "auroc": 0.7561136636636636 + }, + "DoS Slowloris": { + "_n": 666.0, + "auroc": 0.7419217717717718 + }, + "FTP-Patator": { + "_n": 666.0, + "auroc": 0.8369792042042042 + }, + "Heartbleed": { + "_n": 1.0, + "auroc": 0.9735 + }, + "Infiltration": { + "_n": 7.0, + "auroc": 0.9260857142857143 + }, + "Infiltration - Portscan": { + "_n": 666.0, + "auroc": 0.5376098348348348 + }, + "Portscan": { + "_n": 666.0, + "auroc": 0.7660006006006005 + }, + "SSH-Patator": { + "_n": 666.0, + "auroc": 0.8304644144144144 + }, + "Web Attack - Brute Force": { + "_n": 73.0, + "auroc": 0.7853835616438356 + }, + "Web Attack - SQL Injection": { + "_n": 13.0, + "auroc": 0.6326923076923077 + }, + "Web Attack - XSS": { + "_n": 18.0, + "auroc": 0.7253611111111111 + } + }, + "median": { + "Botnet": { + "_n": 666.0, + "auroc": 0.5225315315315315 + }, + "DDoS": { + "_n": 666.0, + "auroc": 0.6240541291291292 + }, + "DoS GoldenEye": { + "_n": 666.0, + "auroc": 0.6072938438438438 + }, + "DoS Hulk": { + "_n": 666.0, + "auroc": 0.5990743243243243 + }, + "DoS Slowhttptest": { + "_n": 666.0, + "auroc": 0.5931954204204204 + }, + "DoS Slowloris": { + "_n": 666.0, + "auroc": 0.5669638138138138 + }, + "FTP-Patator": { + "_n": 666.0, + "auroc": 0.5438021771771772 + }, + "Heartbleed": { + "_n": 1.0, + "auroc": 0.7924 + }, + "Infiltration": { + "_n": 7.0, + "auroc": 0.5036785714285714 + }, + "Infiltration - Portscan": { + "_n": 666.0, + "auroc": 0.6057373873873874 + }, + "Portscan": { + "_n": 666.0, + "auroc": 0.9029579579579579 + }, + "SSH-Patator": { + "_n": 666.0, + "auroc": 0.5151243993993995 + }, + "Web Attack - Brute Force": { + "_n": 73.0, + "auroc": 0.4803986301369863 + }, + "Web Attack - SQL Injection": { + "_n": 13.0, + "auroc": 0.5618923076923077 + }, + "Web Attack - XSS": { + "_n": 18.0, + "auroc": 0.5145055555555555 + } + }, + "p90": { + "Botnet": { + "_n": 666.0, + "auroc": 0.6111415915915916 + }, + "DDoS": { + "_n": 666.0, + "auroc": 0.8433192192192192 + }, + "DoS GoldenEye": { + "_n": 666.0, + "auroc": 0.7923900900900901 + }, + "DoS Hulk": { + "_n": 666.0, + "auroc": 0.7535391891891892 + }, + "DoS Slowhttptest": { + "_n": 666.0, + "auroc": 0.7431529279279279 + }, + "DoS Slowloris": { + "_n": 666.0, + "auroc": 0.6743316066066066 + }, + "FTP-Patator": { + "_n": 666.0, + "auroc": 0.7277819819819821 + }, + "Heartbleed": { + "_n": 1.0, + "auroc": 0.8218000000000001 + }, + "Infiltration": { + "_n": 7.0, + "auroc": 0.7718428571428572 + }, + "Infiltration - Portscan": { + "_n": 666.0, + "auroc": 0.5952377627627627 + }, + "Portscan": { + "_n": 666.0, + "auroc": 0.846989039039039 + }, + "SSH-Patator": { + "_n": 666.0, + "auroc": 0.6674193693693694 + }, + "Web Attack - Brute Force": { + "_n": 73.0, + "auroc": 0.5902164383561643 + }, + "Web Attack - SQL Injection": { + "_n": 13.0, + "auroc": 0.5918307692307693 + }, + "Web Attack - XSS": { + "_n": 18.0, + "auroc": 0.49446666666666667 + } + } + } +} \ No newline at end of file diff --git a/artifacts/baselines/kitsune_2026_04_29/reverse_cross_seed42.npz b/artifacts/baselines/kitsune_2026_04_29/reverse_cross_seed42.npz new file mode 100644 index 0000000..314bda6 Binary files /dev/null and b/artifacts/baselines/kitsune_2026_04_29/reverse_cross_seed42.npz differ diff --git a/artifacts/baselines/kitsune_2026_04_29/reverse_cross_seed43.json b/artifacts/baselines/kitsune_2026_04_29/reverse_cross_seed43.json new file mode 100644 index 0000000..07761ee --- /dev/null +++ b/artifacts/baselines/kitsune_2026_04_29/reverse_cross_seed43.json @@ -0,0 +1,283 @@ +{ + "method": "kitsune_path_b", + "protocol": "reverse_cross", + "seed": 43, + "model_dir": "/home/chy/mambafortrafficmodeling/artifacts/phase25_multiseed_2026_04_25/cicddos2019_lambda0p3_seed43", + "n_train_flows": 5000, + "n_train_packets": 54901, + "n_val": 10000, + "n_atk": 6772, + "D": 9, + "fm_grace": 2000, + "ad_grace": 20000, + "max_ae_size": 10, + "t_train_sec": 2.34, + "overall_by_agg": { + "mean": { + "auroc": 0.6345540091553455, + "auprc": 0.5434895028964192 + }, + "max": { + "auroc": 0.732484886296515, + "auprc": 0.6396314263925083 + }, + "median": { + "auroc": 0.5684908298877731, + "auprc": 0.483124225326346 + }, + "p90": { + "auroc": 0.6840660513880685, + "auprc": 0.593328868440604 + } + }, + "per_class_by_agg": { + "mean": { + "Botnet": { + "_n": 666.0, + "auroc": 0.4971394894894895 + }, + "DDoS": { + "_n": 666.0, + "auroc": 0.7452524024024024 + }, + "DoS GoldenEye": { + "_n": 666.0, + "auroc": 0.6392565315315315 + }, + "DoS Hulk": { + "_n": 666.0, + "auroc": 0.6283921921921922 + }, + "DoS Slowhttptest": { + "_n": 666.0, + "auroc": 0.5856407657657657 + }, + "DoS Slowloris": { + "_n": 666.0, + "auroc": 0.7028736486486487 + }, + "FTP-Patator": { + "_n": 666.0, + "auroc": 0.6037015765765765 + }, + "Heartbleed": { + "_n": 1.0, + "auroc": 0.38680000000000003 + }, + "Infiltration": { + "_n": 7.0, + "auroc": 0.4581142857142857 + }, + "Infiltration - Portscan": { + "_n": 666.0, + "auroc": 0.62235 + }, + "Portscan": { + "_n": 666.0, + "auroc": 0.8743785285285286 + }, + "SSH-Patator": { + "_n": 666.0, + "auroc": 0.4735269519519519 + }, + "Web Attack - Brute Force": { + "_n": 73.0, + "auroc": 0.456841095890411 + }, + "Web Attack - SQL Injection": { + "_n": 13.0, + "auroc": 0.5371846153846154 + }, + "Web Attack - XSS": { + "_n": 18.0, + "auroc": 0.5100166666666667 + } + }, + "max": { + "Botnet": { + "_n": 666.0, + "auroc": 0.5596978978978979 + }, + "DDoS": { + "_n": 666.0, + "auroc": 0.8372762012012012 + }, + "DoS GoldenEye": { + "_n": 666.0, + "auroc": 0.7770656156156156 + }, + "DoS Hulk": { + "_n": 666.0, + "auroc": 0.7518904654654655 + }, + "DoS Slowhttptest": { + "_n": 666.0, + "auroc": 0.6863843843843844 + }, + "DoS Slowloris": { + "_n": 666.0, + "auroc": 0.7811388138138139 + }, + "FTP-Patator": { + "_n": 666.0, + "auroc": 0.8401141141141142 + }, + "Heartbleed": { + "_n": 1.0, + "auroc": 0.861 + }, + "Infiltration": { + "_n": 7.0, + "auroc": 0.8005 + }, + "Infiltration - Portscan": { + "_n": 666.0, + "auroc": 0.49528093093093095 + }, + "Portscan": { + "_n": 666.0, + "auroc": 0.7859298798798798 + }, + "SSH-Patator": { + "_n": 666.0, + "auroc": 0.8009181681681682 + }, + "Web Attack - Brute Force": { + "_n": 73.0, + "auroc": 0.8025027397260274 + }, + "Web Attack - SQL Injection": { + "_n": 13.0, + "auroc": 0.5718307692307693 + }, + "Web Attack - XSS": { + "_n": 18.0, + "auroc": 0.8695999999999999 + } + }, + "median": { + "Botnet": { + "_n": 666.0, + "auroc": 0.503235960960961 + }, + "DDoS": { + "_n": 666.0, + "auroc": 0.5644545045045045 + }, + "DoS GoldenEye": { + "_n": 666.0, + "auroc": 0.5390427927927928 + }, + "DoS Hulk": { + "_n": 666.0, + "auroc": 0.5338515015015015 + }, + "DoS Slowhttptest": { + "_n": 666.0, + "auroc": 0.5319112612612613 + }, + "DoS Slowloris": { + "_n": 666.0, + "auroc": 0.5615001501501501 + }, + "FTP-Patator": { + "_n": 666.0, + "auroc": 0.5224403903903904 + }, + "Heartbleed": { + "_n": 1.0, + "auroc": 0.41400000000000003 + }, + "Infiltration": { + "_n": 7.0, + "auroc": 0.4148 + }, + "Infiltration - Portscan": { + "_n": 666.0, + "auroc": 0.5645393393393393 + }, + "Portscan": { + "_n": 666.0, + "auroc": 0.8810318318318319 + }, + "SSH-Patator": { + "_n": 666.0, + "auroc": 0.4952943693693694 + }, + "Web Attack - Brute Force": { + "_n": 73.0, + "auroc": 0.4862219178082192 + }, + "Web Attack - SQL Injection": { + "_n": 13.0, + "auroc": 0.5883307692307692 + }, + "Web Attack - XSS": { + "_n": 18.0, + "auroc": 0.4975888888888889 + } + }, + "p90": { + "Botnet": { + "_n": 666.0, + "auroc": 0.5955102102102103 + }, + "DDoS": { + "_n": 666.0, + "auroc": 0.7872588588588589 + }, + "DoS GoldenEye": { + "_n": 666.0, + "auroc": 0.7064437687687688 + }, + "DoS Hulk": { + "_n": 666.0, + "auroc": 0.6634027777777778 + }, + "DoS Slowhttptest": { + "_n": 666.0, + "auroc": 0.6625460210210211 + }, + "DoS Slowloris": { + "_n": 666.0, + "auroc": 0.752024174174174 + }, + "FTP-Patator": { + "_n": 666.0, + "auroc": 0.6953288288288287 + }, + "Heartbleed": { + "_n": 1.0, + "auroc": 0.5695 + }, + "Infiltration": { + "_n": 7.0, + "auroc": 0.6463428571428571 + }, + "Infiltration - Portscan": { + "_n": 666.0, + "auroc": 0.5379545045045044 + }, + 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0.7343030030030031, + 0.6283921921921922, + 0.7083266516516517 + ] + }, + "DoS Slowhttptest": { + "n": 666, + "aurocs": [ + 0.7101981981981981, + 0.5856407657657657, + 0.630890990990991 + ] + }, + "DoS Slowloris": { + "n": 666, + "aurocs": [ + 0.6661138138138138, + 0.7028736486486487, + 0.647117042042042 + ] + }, + "FTP-Patator": { + "n": 666, + "aurocs": [ + 0.6634243243243243, + 0.6037015765765765, + 0.600568018018018 + ] + }, + "Heartbleed": { + "n": 1, + "aurocs": [ + 0.7665, + 0.38680000000000003, + 0.6537 + ] + }, + "Infiltration": { + "n": 7, + "aurocs": [ + 0.7007, + 0.4581142857142857, + 0.44662857142857143 + ] + }, + "Infiltration - Portscan": { + "n": 666, + "aurocs": [ + 0.6582857357357358, + 0.62235, + 0.7062888888888889 + ] + }, + "Portscan": { + "n": 666, + "aurocs": [ + 0.8860483483483483, + 0.8743785285285286, + 0.9018552552552551 + ] + }, + "SSH-Patator": { + "n": 666, + "aurocs": [ + 0.5690978978978979, + 0.4735269519519519, + 0.48096388888888886 + ] + }, + "Web Attack - Brute Force": { + "n": 73, + "aurocs": [ + 0.49512808219178084, + 0.456841095890411, + 0.5025328767123288 + ] + }, + "Web Attack - SQL Injection": { + "n": 13, + "aurocs": [ + 0.5701076923076923, + 0.5371846153846154, + 0.5300307692307693 + ] + }, + "Web Attack - XSS": { + "n": 18, + "aurocs": [ + 0.4154833333333333, + 0.5100166666666667, + 0.46598333333333336 + ] + } + } + }, + "baselines": { + "terminal_norm": { + "iscxtor_within": [ + 0.9945, + 0.0011 + ], + "cicids_within": [ + 0.9858, + 0.0021 + ], + "cicddos_within": [ + 0.996, + 0.001 + ], + "forward_cross": [ + 0.9109, + 0.0032 + ], + "reverse_cross": [ + 0.5999, + null + ] + }, + "kitsune_paper": { + "iscxtor_within": [ + 0.78, + null + ], + "cicids_within": [ + 0.85, + null + ], + "cicddos_within": [ + null, + null + ], + "forward_cross": [ + null, + null + ], + "reverse_cross": [ + null, + null + ] + } + } +} \ No newline at end of file diff --git a/artifacts/baselines/kitsune_2026_04_29/summary.md b/artifacts/baselines/kitsune_2026_04_29/summary.md new file mode 100644 index 0000000..3e14b63 --- /dev/null +++ b/artifacts/baselines/kitsune_2026_04_29/summary.md @@ -0,0 +1,71 @@ +# Kitsune (Path B) Baseline — On Our 5-Protocol Layout + +Date: 2026-04-29 + +Method: KitNET ensemble autoencoder (the ML core of Kitsune). +**Path B**: feeds our **z-scored 9-d packet features** directly through `KitNET.process()` for the FM+AD grace, then `KitNET.execute()` per packet during eval. **AfterImage's 100-d host/session statistics are skipped** (they require sequential pcap streams which our (B,T,9) tensor abstraction discards). This keeps data usage unified with `eval_new_scores.py`. +Train: 5000 source-benign flows → ~75-320k packets (≥ FM+AD=55k grace). +Score: per-flow aggregate of per-packet RMSE (mean / max / median / p90). +Sampling: same seeds & stratification as `eval_new_scores.py`. + +## Headline AUROC (best aggregator per protocol, 3-seed mean ± std) + +| Protocol | terminal_norm | Kitsune paper (Shafir reproduction) | **Kitsune Path B (ours)** | best agg | Δ vs paper | Δ vs terminal | +|---|---:|---:|---:|---|---:|---:| +| ISCXTor2016 within | 0.9945 ± 0.0011 | 0.7800 | **0.5653 ± 0.0226** | `median` | -0.2147 | -0.4292 | +| CICIDS2017 within (σ=0.6) | 0.9858 ± 0.0021 | 0.8500 | **0.7023 ± 0.0310** | `mean` | -0.1477 | -0.2835 | +| CICDDoS2019 within | 0.9960 ± 0.0010 | — | **0.4710 ± 0.0039** | `median` | — | -0.5250 | +| IDS2017→DDoS2019 forward | 0.9109 ± 0.0032 | — | **0.4905 ± 0.0751** | `median` | — | -0.4204 | +| DDoS2019→IDS2017 reverse | 0.5999 | — | **0.7483 ± 0.0137** | `max` | — | +0.1484 | + +## All aggregators (3-seed mean ± std) + +| Protocol | mean | max | median | p90 | +|---|---:|---:|---:|---:| +| ISCXTor2016 within | 0.5493 ± 0.0274 | 0.4962 ± 0.0293 | 0.5653 ± 0.0226 | 0.5193 ± 0.0278 | +| CICIDS2017 within (σ=0.6) | 0.7023 ± 0.0310 | 0.6930 ± 0.0329 | 0.6504 ± 0.0171 | 0.6946 ± 0.0333 | +| CICDDoS2019 within | 0.4395 ± 0.0193 | 0.3359 ± 0.0071 | 0.4710 ± 0.0039 | 0.3542 ± 0.0084 | +| IDS2017→DDoS2019 forward | 0.4597 ± 0.1055 | 0.3716 ± 0.0931 | 0.4905 ± 0.0751 | 0.3932 ± 0.0986 | +| DDoS2019→IDS2017 reverse | 0.6680 ± 0.0321 | 0.7483 ± 0.0137 | 0.5884 ± 0.0190 | 0.7062 ± 0.0201 | + +## Per-attack (forward + reverse, mean aggregator) + +### IDS2017→DDoS2019 forward +| attack | n | Kitsune AUROC mean ± std | +|---|---:|---:| +| `DrDoS_DNS` | 588 | 0.4775 ± 0.2682 | +| `DrDoS_LDAP` | 588 | 0.4784 ± 0.2655 | +| `DrDoS_MSSQL` | 588 | 0.4275 ± 0.0802 | +| `DrDoS_NTP` | 588 | 0.4906 ± 0.0508 | +| `DrDoS_NetBIOS` | 588 | 0.4503 ± 0.0199 | +| `DrDoS_SNMP` | 588 | 0.4800 ± 0.2615 | +| `DrDoS_SSDP` | 588 | 0.4640 ± 0.0776 | +| `DrDoS_UDP` | 588 | 0.4748 ± 0.0868 | +| `LDAP` | 588 | 0.4709 ± 0.2795 | +| `MSSQL` | 588 | 0.4101 ± 0.0729 | +| `NetBIOS` | 588 | 0.4568 ± 0.0320 | +| `Portmap` | 588 | 0.4489 ± 0.0237 | +| `Syn` | 588 | 0.4755 ± 0.0423 | +| `TFTP` | 588 | 0.4742 ± 0.1824 | +| `UDP` | 588 | 0.4680 ± 0.0835 | +| `UDPLag` | 588 | 0.4256 ± 0.0862 | +| `WebDDoS` | 438 | 0.4349 ± 0.0184 | + +### DDoS2019→IDS2017 reverse +| attack | n | Kitsune AUROC mean ± std | +|---|---:|---:| +| `Botnet` | 666 | 0.5218 ± 0.0297 | +| `DDoS` | 666 | 0.7965 ± 0.0444 | +| `DoS GoldenEye` | 666 | 0.7067 ± 0.0601 | +| `DoS Hulk` | 666 | 0.6903 ± 0.0552 | +| `DoS Slowhttptest` | 666 | 0.6422 ± 0.0630 | +| `DoS Slowloris` | 666 | 0.6720 ± 0.0283 | +| `FTP-Patator` | 666 | 0.6226 ± 0.0354 | +| `Heartbleed` | 1 | 0.6023 ± 0.1950 | +| `Infiltration` | 7 | 0.5351 ± 0.1435 | +| `Infiltration - Portscan` | 666 | 0.6623 ± 0.0421 | +| `Portscan` | 666 | 0.8874 ± 0.0138 | +| `SSH-Patator` | 666 | 0.5079 ± 0.0532 | +| `Web Attack - Brute Force` | 73 | 0.4848 ± 0.0245 | +| `Web Attack - SQL Injection` | 13 | 0.5458 ± 0.0214 | +| `Web Attack - XSS` | 18 | 0.4638 ± 0.0473 | \ No newline at end of file diff --git a/artifacts/baselines/kitsune_path_a_2026_04_29/cicids_within_seed42.log b/artifacts/baselines/kitsune_path_a_2026_04_29/cicids_within_seed42.log new file mode 100644 index 0000000..98ecc79 --- /dev/null +++ b/artifacts/baselines/kitsune_path_a_2026_04_29/cicids_within_seed42.log @@ -0,0 +1,42 @@ +Importing Scapy Library +[run] kitsune_path_a protocol=cicids_within seed=42 +[run] dataset=cicids2017 model_dir=/home/chy/mambafortrafficmodeling/artifacts/phase25_sigma06_multiseed_2026_04_25/cicids2017_lambda0p3_sigma0p6_seed42 +[data] flows=/home/chy/mambafortrafficmodeling/datasets/cicids2017/processed/flows.parquet packets_source=/home/chy/mambafortrafficmodeling/datasets/cicids2017/processed/packets.npz +[data] using external flow features D=20 +[data] rows total=2,025,564 keep len>=2: 2,017,180 +[data] benign=1,513,450 attack=30,000 -> train=1,210,760 val=10,000 +[data] flows.parquet rows: 2,025,564; val=10,000 attack=30,000 +[pcap] discovered 5 pcap(s) + /home/chy/mambafortrafficmodeling/datasets/cicids2017/raw/pcap/Friday-WorkingHours.pcap + /home/chy/mambafortrafficmodeling/datasets/cicids2017/raw/pcap/Monday-WorkingHours.pcap + /home/chy/mambafortrafficmodeling/datasets/cicids2017/raw/pcap/Thursday-WorkingHours.pcap + /home/chy/mambafortrafficmodeling/datasets/cicids2017/raw/pcap/Tuesday-WorkingHours.pcap + /home/chy/mambafortrafficmodeling/datasets/cicids2017/raw/pcap/Wednesday-workingHours.pcap +Feature-Mapper: train-mode, Anomaly-Detector: off-mode + [stream] Friday-WorkingHours.pcap +Parsing with tshark... +tshark parsing complete. File saved as: /home/chy/mambafortrafficmodeling/datasets/cicids2017/raw/pcap/Friday-WorkingHours.pcap.tsv +counting lines in file... +There are 9997875 Packets. +The Feature-Mapper found a mapping: 100 features to 15 autoencoders. +Feature-Mapper: execute-mode, Anomaly-Detector: train-mode +Feature-Mapper: execute-mode, Anomaly-Detector: execute-mode + [200,000] elapsed 136s (1471 pkt/s) + [400,000] elapsed 348s (1148 pkt/s) + [600,000] elapsed 536s (1118 pkt/s) + [800,000] elapsed 724s (1105 pkt/s) + [1,000,000] elapsed 904s (1106 pkt/s) + [1,200,000] elapsed 1090s (1101 pkt/s) + [1,400,000] elapsed 1272s (1100 pkt/s) + [1,600,000] elapsed 1477s (1083 pkt/s) + [1,800,000] elapsed 1665s (1081 pkt/s) + [2,000,000] elapsed 1853s (1079 pkt/s) + [2,200,000] elapsed 2040s (1078 pkt/s) + [2,400,000] elapsed 2243s (1070 pkt/s) + [2,600,000] elapsed 2531s (1027 pkt/s) + [2,800,000] elapsed 2791s (1003 pkt/s) + [3,000,000] elapsed 3195s (939 pkt/s) + [3,200,000] elapsed 3524s (908 pkt/s) + [3,400,000] elapsed 3795s (896 pkt/s) + [3,600,000] elapsed 4656s (773 pkt/s) + [3,800,000] elapsed 6536s (581 pkt/s) diff --git a/artifacts/baselines/shafir_nf_2026_04_29/cicddos_within_seed42.json b/artifacts/baselines/shafir_nf_2026_04_29/cicddos_within_seed42.json new file mode 100644 index 0000000..9bbf004 --- /dev/null +++ b/artifacts/baselines/shafir_nf_2026_04_29/cicddos_within_seed42.json @@ -0,0 +1,94 @@ +{ + "method": "shafir_nf", + "protocol": "cicddos_within", + "seed": 42, + "model_dir": "/home/chy/mambafortrafficmodeling/artifacts/phase25_multiseed_2026_04_25/cicddos2019_lambda0p3_seed42", + "n_train": 10000, + "n_val": 10000, + "n_atk": 20000, + "epochs": 100, + "lr": 0.001, + "optimizer": "sgd", + "t_train_sec": 257.75, + "t_score_sec": 7.92, + "loss_first_last": [ + 332.8706359863281, + -13.712800025939941 + ], + "overall": { + "neg_log_prob": { + "auroc": 0.8471867399999999, + "auprc": 0.9237535983514962 + } + }, + "per_class": { + "DrDoS_DNS": { + "_n": 1136.0, + 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0.5736365227537923 + }, + "WebDDoS": { + "_n": 1.0, + "auroc": 0.46130000000000004 + } + } +} \ No newline at end of file diff --git a/artifacts/baselines/shafir_nf_2026_04_29/cicddos_within_seed42.npz b/artifacts/baselines/shafir_nf_2026_04_29/cicddos_within_seed42.npz new file mode 100644 index 0000000..fab0346 Binary files /dev/null and b/artifacts/baselines/shafir_nf_2026_04_29/cicddos_within_seed42.npz differ diff --git a/artifacts/baselines/shafir_nf_2026_04_29/cicddos_within_seed43.json b/artifacts/baselines/shafir_nf_2026_04_29/cicddos_within_seed43.json new file mode 100644 index 0000000..27ac4f6 --- /dev/null +++ b/artifacts/baselines/shafir_nf_2026_04_29/cicddos_within_seed43.json @@ -0,0 +1,94 @@ +{ + "method": "shafir_nf", + "protocol": "cicddos_within", + "seed": 43, + "model_dir": "/home/chy/mambafortrafficmodeling/artifacts/phase25_multiseed_2026_04_25/cicddos2019_lambda0p3_seed43", + "n_train": 10000, + "n_val": 10000, + "n_atk": 20000, + "epochs": 100, + "lr": 0.001, 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"/home/chy/mambafortrafficmodeling/artifacts/phase25_sigma06_multiseed_2026_04_25/cicids2017_lambda0p3_sigma0p6_seed44", + "n_train": 10000, + "n_val": 10000, + "n_atk": 9846, + "epochs": 100, + "lr": 0.001, + "optimizer": "sgd", + "t_train_sec": 257.16, + "t_score_sec": 7.26, + "loss_first_last": [ + 286.2483825683594, + -10.637186050415039 + ], + "overall": { + "neg_log_prob": { + "auroc": 0.9268555200081251, + "auprc": 0.9212919110720112 + } + }, + "per_class": { + "DrDoS_DNS": { + "_n": 588.0, + "auroc": 0.9894368197278913 + }, + "DrDoS_LDAP": { + "_n": 588.0, + "auroc": 0.9994419217687076 + }, + "DrDoS_MSSQL": { + "_n": 588.0, + "auroc": 0.9638414115646259 + }, + "DrDoS_NTP": { + "_n": 588.0, + "auroc": 0.9682577380952381 + }, + "DrDoS_NetBIOS": { + "_n": 588.0, + "auroc": 0.9415774659863946 + }, + "DrDoS_SNMP": { + "_n": 588.0, + "auroc": 0.9966145408163265 + }, + "DrDoS_SSDP": { + "_n": 588.0, + "auroc": 0.9596625 + }, + "DrDoS_UDP": { + "_n": 588.0, + "auroc": 0.9592947278911564 + }, + "LDAP": { + "_n": 588.0, + "auroc": 0.9985690476190476 + }, + "MSSQL": { + "_n": 588.0, + "auroc": 0.9632055272108845 + }, + "NetBIOS": { + "_n": 588.0, + "auroc": 0.9394392857142857 + }, + "Portmap": { + "_n": 588.0, + "auroc": 0.9410942176870749 + }, + "Syn": { + "_n": 588.0, + "auroc": 0.7449137755102041 + }, + "TFTP": { + "_n": 588.0, + "auroc": 0.9752333333333333 + }, + "UDP": { + "_n": 588.0, + "auroc": 0.9585020408163266 + }, + "UDPLag": { + "_n": 588.0, + "auroc": 0.8215611394557822 + }, + "WebDDoS": { + "_n": 438.0, + "auroc": 0.5362554794520548 + } + } +} \ No newline at end of file diff --git a/artifacts/baselines/shafir_nf_2026_04_29/forward_cross_seed44.npz b/artifacts/baselines/shafir_nf_2026_04_29/forward_cross_seed44.npz new file mode 100644 index 0000000..aeb5157 Binary files /dev/null and b/artifacts/baselines/shafir_nf_2026_04_29/forward_cross_seed44.npz differ diff --git a/artifacts/baselines/shafir_nf_2026_04_29/iscxtor_within_seed42.json b/artifacts/baselines/shafir_nf_2026_04_29/iscxtor_within_seed42.json new file mode 100644 index 0000000..e843f1a --- /dev/null +++ b/artifacts/baselines/shafir_nf_2026_04_29/iscxtor_within_seed42.json @@ -0,0 +1,30 @@ +{ + "method": "shafir_nf", + "protocol": "iscxtor_within", + "seed": 42, + "model_dir": "/home/chy/mambafortrafficmodeling/artifacts/phase25_multiseed_2026_04_25/iscxtor2016_lambda0p3_seed42", + "n_train": 10000, + "n_val": 10000, + "n_atk": 1312, + "epochs": 100, + "lr": 0.001, + "optimizer": "sgd", + "t_train_sec": 261.86, + "t_score_sec": 6.74, + "loss_first_last": [ + 266.9950256347656, + -14.434609413146973 + ], + "overall": { + "neg_log_prob": { + "auroc": 0.947987538109756, + "auprc": 0.7240378342714916 + } + }, + "per_class": { + "tor": { + "_n": 1312.0, + "auroc": 0.947987538109756 + } + } +} \ No newline at end of file diff --git a/artifacts/baselines/shafir_nf_2026_04_29/iscxtor_within_seed42.npz b/artifacts/baselines/shafir_nf_2026_04_29/iscxtor_within_seed42.npz new file mode 100644 index 0000000..2c90ef3 Binary files /dev/null and b/artifacts/baselines/shafir_nf_2026_04_29/iscxtor_within_seed42.npz differ diff --git a/artifacts/baselines/shafir_nf_2026_04_29/iscxtor_within_seed43.json b/artifacts/baselines/shafir_nf_2026_04_29/iscxtor_within_seed43.json new file mode 100644 index 0000000..b7d3165 --- /dev/null +++ b/artifacts/baselines/shafir_nf_2026_04_29/iscxtor_within_seed43.json @@ -0,0 +1,30 @@ +{ + "method": "shafir_nf", + "protocol": "iscxtor_within", + "seed": 43, + "model_dir": "/home/chy/mambafortrafficmodeling/artifacts/phase25_multiseed_2026_04_25/iscxtor2016_lambda0p3_seed43", + "n_train": 10000, + "n_val": 10000, + "n_atk": 1312, + "epochs": 100, + "lr": 0.001, + "optimizer": "sgd", + "t_train_sec": 264.57, + "t_score_sec": 6.81, + "loss_first_last": [ + 266.81707763671875, + -14.625688552856445 + ], + "overall": { + "neg_log_prob": { + "auroc": 0.9448752286585366, + "auprc": 0.7116331900303116 + } + }, + "per_class": { + "tor": { + "_n": 1312.0, + "auroc": 0.9448752286585366 + } + } +} \ No newline at end of file diff --git a/artifacts/baselines/shafir_nf_2026_04_29/iscxtor_within_seed43.npz b/artifacts/baselines/shafir_nf_2026_04_29/iscxtor_within_seed43.npz new file mode 100644 index 0000000..35a5125 Binary files /dev/null and b/artifacts/baselines/shafir_nf_2026_04_29/iscxtor_within_seed43.npz differ diff --git a/artifacts/baselines/shafir_nf_2026_04_29/iscxtor_within_seed44.json b/artifacts/baselines/shafir_nf_2026_04_29/iscxtor_within_seed44.json new file mode 100644 index 0000000..8178e45 --- /dev/null +++ b/artifacts/baselines/shafir_nf_2026_04_29/iscxtor_within_seed44.json @@ -0,0 +1,30 @@ +{ + "method": "shafir_nf", + "protocol": "iscxtor_within", + "seed": 44, + "model_dir": "/home/chy/mambafortrafficmodeling/artifacts/phase25_multiseed_2026_04_25/iscxtor2016_lambda0p3_seed44", + "n_train": 10000, + "n_val": 10000, + "n_atk": 1312, + "epochs": 100, + "lr": 0.001, + "optimizer": "sgd", + "t_train_sec": 259.29, + "t_score_sec": 6.67, + "loss_first_last": [ + 268.0535888671875, + -16.31130027770996 + ], + "overall": { + "neg_log_prob": { + "auroc": 0.9338064786585365, + "auprc": 0.7069392431440469 + } + }, + "per_class": { + "tor": { + "_n": 1312.0, + "auroc": 0.9338064786585365 + } + } +} \ No newline at end of file diff --git a/artifacts/baselines/shafir_nf_2026_04_29/iscxtor_within_seed44.npz b/artifacts/baselines/shafir_nf_2026_04_29/iscxtor_within_seed44.npz new file mode 100644 index 0000000..08db83d Binary files /dev/null and b/artifacts/baselines/shafir_nf_2026_04_29/iscxtor_within_seed44.npz differ diff --git a/artifacts/baselines/shafir_nf_2026_04_29/master.log b/artifacts/baselines/shafir_nf_2026_04_29/master.log new file mode 100644 index 0000000..95b3365 --- /dev/null +++ b/artifacts/baselines/shafir_nf_2026_04_29/master.log @@ -0,0 +1,145 @@ +[skip] artifacts/baselines/shafir_nf_2026_04_29/iscxtor_within_seed42.json exists +=== protocol=iscxtor_within seed=43 epochs=100 opt=sgd lr=0.001 === +[run] shafir_nf protocol=iscxtor_within seed=43 +[run] using normalization stats from /home/chy/mambafortrafficmodeling/artifacts/phase25_multiseed_2026_04_25/iscxtor2016_lambda0p3_seed43/model.pt (source ckpt) +[data] flows=/home/chy/mambafortrafficmodeling/datasets/iscxtor2016/processed/flows.parquet packets_source=/home/chy/mambafortrafficmodeling/datasets/iscxtor2016/processed/packets.npz +[data] using external flow features D=20 +[data] rows total=103,079 keep len>=2: 66,189 +[data] benign=64,877 attack=1,312 -> train=51,901 val=10,000 +[data] train=10,000 val=10,000 attack=1,312 D=20 +[saved] artifacts/baselines/shafir_nf_2026_04_29/iscxtor_within_seed43.json +[saved] artifacts/baselines/shafir_nf_2026_04_29/iscxtor_within_seed43.npz +[result] AUROC=0.9449 AUPRC=0.7116 train=264.6s score=6.8s +[done] elapsed=280s artifacts/baselines/shafir_nf_2026_04_29/iscxtor_within_seed43.json +=== protocol=iscxtor_within seed=44 epochs=100 opt=sgd lr=0.001 === +[run] shafir_nf protocol=iscxtor_within seed=44 +[run] using normalization stats from /home/chy/mambafortrafficmodeling/artifacts/phase25_multiseed_2026_04_25/iscxtor2016_lambda0p3_seed44/model.pt (source ckpt) +[data] flows=/home/chy/mambafortrafficmodeling/datasets/iscxtor2016/processed/flows.parquet packets_source=/home/chy/mambafortrafficmodeling/datasets/iscxtor2016/processed/packets.npz +[data] using external flow features D=20 +[data] rows total=103,079 keep len>=2: 66,189 +[data] benign=64,877 attack=1,312 -> train=51,901 val=10,000 +[data] train=10,000 val=10,000 attack=1,312 D=20 +[saved] artifacts/baselines/shafir_nf_2026_04_29/iscxtor_within_seed44.json +[saved] artifacts/baselines/shafir_nf_2026_04_29/iscxtor_within_seed44.npz +[result] AUROC=0.9338 AUPRC=0.7069 train=259.3s score=6.7s +[done] elapsed=275s artifacts/baselines/shafir_nf_2026_04_29/iscxtor_within_seed44.json +=== protocol=cicids_within seed=42 epochs=100 opt=sgd lr=0.001 === +[run] shafir_nf protocol=cicids_within seed=42 +[run] using normalization stats from /home/chy/mambafortrafficmodeling/artifacts/phase25_sigma06_multiseed_2026_04_25/cicids2017_lambda0p3_sigma0p6_seed42/model.pt (source ckpt) +[data] flows=/home/chy/mambafortrafficmodeling/datasets/cicids2017/processed/flows.parquet packets_source=/home/chy/mambafortrafficmodeling/datasets/cicids2017/processed/packets.npz +[data] using external flow features D=20 +[data] rows total=2,025,564 keep len>=2: 2,017,180 +[data] benign=1,513,450 attack=30,000 -> train=1,210,760 val=10,000 +[data] train=10,000 val=10,000 attack=30,000 D=20 +[saved] artifacts/baselines/shafir_nf_2026_04_29/cicids_within_seed42.json +[saved] artifacts/baselines/shafir_nf_2026_04_29/cicids_within_seed42.npz +[result] AUROC=0.9413 AUPRC=0.9741 train=261.5s score=8.8s +[done] elapsed=377s artifacts/baselines/shafir_nf_2026_04_29/cicids_within_seed42.json +=== protocol=cicids_within seed=43 epochs=100 opt=sgd lr=0.001 === +[run] shafir_nf protocol=cicids_within seed=43 +[run] using normalization stats from /home/chy/mambafortrafficmodeling/artifacts/phase25_sigma06_multiseed_2026_04_25/cicids2017_lambda0p3_sigma0p6_seed43/model.pt (source ckpt) +[data] flows=/home/chy/mambafortrafficmodeling/datasets/cicids2017/processed/flows.parquet packets_source=/home/chy/mambafortrafficmodeling/datasets/cicids2017/processed/packets.npz +[data] using external flow features D=20 +[data] rows total=2,025,564 keep len>=2: 2,017,180 +[data] benign=1,513,450 attack=30,000 -> train=1,210,760 val=10,000 +[data] train=10,000 val=10,000 attack=30,000 D=20 +[saved] artifacts/baselines/shafir_nf_2026_04_29/cicids_within_seed43.json +[saved] artifacts/baselines/shafir_nf_2026_04_29/cicids_within_seed43.npz +[result] AUROC=0.9307 AUPRC=0.9700 train=261.0s score=9.1s +[done] elapsed=378s artifacts/baselines/shafir_nf_2026_04_29/cicids_within_seed43.json +=== protocol=cicids_within seed=44 epochs=100 opt=sgd lr=0.001 === +[run] shafir_nf protocol=cicids_within seed=44 +[run] using normalization stats from /home/chy/mambafortrafficmodeling/artifacts/phase25_sigma06_multiseed_2026_04_25/cicids2017_lambda0p3_sigma0p6_seed44/model.pt (source ckpt) +[data] flows=/home/chy/mambafortrafficmodeling/datasets/cicids2017/processed/flows.parquet packets_source=/home/chy/mambafortrafficmodeling/datasets/cicids2017/processed/packets.npz +[data] using external flow features D=20 +[data] rows total=2,025,564 keep len>=2: 2,017,180 +[data] benign=1,513,450 attack=30,000 -> train=1,210,760 val=10,000 +[data] train=10,000 val=10,000 attack=30,000 D=20 +[saved] artifacts/baselines/shafir_nf_2026_04_29/cicids_within_seed44.json +[saved] artifacts/baselines/shafir_nf_2026_04_29/cicids_within_seed44.npz +[result] AUROC=0.9048 AUPRC=0.9571 train=256.9s score=9.0s +[done] elapsed=373s artifacts/baselines/shafir_nf_2026_04_29/cicids_within_seed44.json +=== protocol=cicddos_within seed=42 epochs=100 opt=sgd lr=0.001 === +[run] shafir_nf protocol=cicddos_within seed=42 +[run] using normalization stats from /home/chy/mambafortrafficmodeling/artifacts/phase25_multiseed_2026_04_25/cicddos2019_lambda0p3_seed42/model.pt (source ckpt) +[data] flows=/home/chy/mambafortrafficmodeling/datasets/cicddos2019/processed/flows.parquet packets_source=/home/chy/mambafortrafficmodeling/datasets/cicddos2019/processed/full_store +[data] using external flow features D=20 +[data] rows total=8,993,376 keep len>=2: 8,986,875 +[data] benign=93,207 attack=20,000 -> train=74,565 val=18,642 +[data] train=10,000 val=10,000 attack=20,000 D=20 +[saved] artifacts/baselines/shafir_nf_2026_04_29/cicddos_within_seed42.json +[saved] artifacts/baselines/shafir_nf_2026_04_29/cicddos_within_seed42.npz +[result] AUROC=0.8472 AUPRC=0.9238 train=257.8s score=7.9s +[done] elapsed=279s artifacts/baselines/shafir_nf_2026_04_29/cicddos_within_seed42.json +=== protocol=cicddos_within seed=43 epochs=100 opt=sgd lr=0.001 === +[run] shafir_nf protocol=cicddos_within seed=43 +[run] using normalization stats from /home/chy/mambafortrafficmodeling/artifacts/phase25_multiseed_2026_04_25/cicddos2019_lambda0p3_seed43/model.pt (source ckpt) +[data] flows=/home/chy/mambafortrafficmodeling/datasets/cicddos2019/processed/flows.parquet packets_source=/home/chy/mambafortrafficmodeling/datasets/cicddos2019/processed/full_store +[data] using external flow features D=20 +[data] rows total=8,993,376 keep len>=2: 8,986,875 +[data] benign=93,207 attack=20,000 -> train=74,565 val=18,642 +[data] train=10,000 val=10,000 attack=20,000 D=20 +[saved] artifacts/baselines/shafir_nf_2026_04_29/cicddos_within_seed43.json +[saved] artifacts/baselines/shafir_nf_2026_04_29/cicddos_within_seed43.npz +[result] AUROC=0.9214 AUPRC=0.9576 train=256.9s score=8.0s +[done] elapsed=279s artifacts/baselines/shafir_nf_2026_04_29/cicddos_within_seed43.json +=== protocol=cicddos_within seed=44 epochs=100 opt=sgd lr=0.001 === +[run] shafir_nf protocol=cicddos_within seed=44 +[run] using normalization stats from /home/chy/mambafortrafficmodeling/artifacts/phase25_multiseed_2026_04_25/cicddos2019_lambda0p3_seed44/model.pt (source ckpt) +[data] flows=/home/chy/mambafortrafficmodeling/datasets/cicddos2019/processed/flows.parquet packets_source=/home/chy/mambafortrafficmodeling/datasets/cicddos2019/processed/full_store +[data] using external flow features D=20 +[data] rows total=8,993,376 keep len>=2: 8,986,875 +[data] benign=93,207 attack=20,000 -> train=74,565 val=18,642 +[data] train=10,000 val=10,000 attack=20,000 D=20 +[saved] artifacts/baselines/shafir_nf_2026_04_29/cicddos_within_seed44.json +[saved] artifacts/baselines/shafir_nf_2026_04_29/cicddos_within_seed44.npz +[result] AUROC=0.9024 AUPRC=0.9456 train=256.9s score=7.9s +[done] elapsed=278s artifacts/baselines/shafir_nf_2026_04_29/cicddos_within_seed44.json +=== protocol=forward_cross seed=42 epochs=100 opt=sgd lr=0.001 === +[run] shafir_nf protocol=forward_cross seed=42 +[run] using normalization stats from /home/chy/mambafortrafficmodeling/artifacts/phase25_sigma06_multiseed_2026_04_25/cicids2017_lambda0p3_sigma0p6_seed42/model.pt (source ckpt) +[data] train=10,000 val=10,000 attack=9,846 D=20 +[saved] artifacts/baselines/shafir_nf_2026_04_29/forward_cross_seed42.json +[saved] artifacts/baselines/shafir_nf_2026_04_29/forward_cross_seed42.npz +[result] AUROC=0.9082 AUPRC=0.8476 train=260.8s score=7.5s +[done] elapsed=279s artifacts/baselines/shafir_nf_2026_04_29/forward_cross_seed42.json +=== protocol=forward_cross seed=43 epochs=100 opt=sgd lr=0.001 === +[run] shafir_nf protocol=forward_cross seed=43 +[run] using normalization stats from /home/chy/mambafortrafficmodeling/artifacts/phase25_sigma06_multiseed_2026_04_25/cicids2017_lambda0p3_sigma0p6_seed43/model.pt (source ckpt) +[data] train=10,000 val=10,000 attack=9,846 D=20 +[saved] artifacts/baselines/shafir_nf_2026_04_29/forward_cross_seed43.json +[saved] artifacts/baselines/shafir_nf_2026_04_29/forward_cross_seed43.npz +[result] AUROC=0.9280 AUPRC=0.8643 train=257.2s score=7.2s +[done] elapsed=275s artifacts/baselines/shafir_nf_2026_04_29/forward_cross_seed43.json +=== protocol=forward_cross seed=44 epochs=100 opt=sgd lr=0.001 === +[run] shafir_nf protocol=forward_cross seed=44 +[run] using normalization stats from /home/chy/mambafortrafficmodeling/artifacts/phase25_sigma06_multiseed_2026_04_25/cicids2017_lambda0p3_sigma0p6_seed44/model.pt (source ckpt) +[data] train=10,000 val=10,000 attack=9,846 D=20 +[saved] artifacts/baselines/shafir_nf_2026_04_29/forward_cross_seed44.json +[saved] artifacts/baselines/shafir_nf_2026_04_29/forward_cross_seed44.npz +[result] AUROC=0.9269 AUPRC=0.9213 train=257.2s score=7.3s +[done] elapsed=275s artifacts/baselines/shafir_nf_2026_04_29/forward_cross_seed44.json +=== protocol=reverse_cross seed=42 epochs=100 opt=sgd lr=0.001 === +[run] shafir_nf protocol=reverse_cross seed=42 +[run] using normalization stats from /home/chy/mambafortrafficmodeling/artifacts/phase25_multiseed_2026_04_25/cicddos2019_lambda0p3_seed42/model.pt (source ckpt) +[data] train=10,000 val=10,000 attack=6,772 D=20 +[saved] artifacts/baselines/shafir_nf_2026_04_29/reverse_cross_seed42.json +[saved] artifacts/baselines/shafir_nf_2026_04_29/reverse_cross_seed42.npz +[result] AUROC=0.7206 AUPRC=0.6950 train=257.1s score=7.4s +[done] elapsed=270s artifacts/baselines/shafir_nf_2026_04_29/reverse_cross_seed42.json +=== protocol=reverse_cross seed=43 epochs=100 opt=sgd lr=0.001 === +[run] shafir_nf protocol=reverse_cross seed=43 +[run] using normalization stats from /home/chy/mambafortrafficmodeling/artifacts/phase25_multiseed_2026_04_25/cicddos2019_lambda0p3_seed43/model.pt (source ckpt) +[data] train=10,000 val=10,000 attack=6,772 D=20 +[saved] artifacts/baselines/shafir_nf_2026_04_29/reverse_cross_seed43.json +[saved] artifacts/baselines/shafir_nf_2026_04_29/reverse_cross_seed43.npz +[result] AUROC=0.7262 AUPRC=0.7119 train=257.5s score=7.4s +[done] elapsed=271s artifacts/baselines/shafir_nf_2026_04_29/reverse_cross_seed43.json +=== protocol=reverse_cross seed=44 epochs=100 opt=sgd lr=0.001 === +[run] shafir_nf protocol=reverse_cross seed=44 +[run] using normalization stats from /home/chy/mambafortrafficmodeling/artifacts/phase25_multiseed_2026_04_25/cicddos2019_lambda0p3_seed44/model.pt (source ckpt) +[data] train=10,000 val=10,000 attack=6,772 D=20 +[saved] artifacts/baselines/shafir_nf_2026_04_29/reverse_cross_seed44.json +[saved] artifacts/baselines/shafir_nf_2026_04_29/reverse_cross_seed44.npz +[result] AUROC=0.7272 AUPRC=0.6895 train=257.7s score=7.2s +[done] elapsed=271s artifacts/baselines/shafir_nf_2026_04_29/reverse_cross_seed44.json diff --git a/artifacts/baselines/shafir_nf_2026_04_29/orchestrator.log b/artifacts/baselines/shafir_nf_2026_04_29/orchestrator.log new file mode 100644 index 0000000..5db4cac --- /dev/null +++ b/artifacts/baselines/shafir_nf_2026_04_29/orchestrator.log @@ -0,0 +1,146 @@ +[skip] artifacts/baselines/shafir_nf_2026_04_29/iscxtor_within_seed42.json exists +=== protocol=iscxtor_within seed=43 epochs=100 opt=sgd lr=0.001 === +[run] shafir_nf protocol=iscxtor_within seed=43 +[run] using normalization stats from /home/chy/mambafortrafficmodeling/artifacts/phase25_multiseed_2026_04_25/iscxtor2016_lambda0p3_seed43/model.pt (source ckpt) +[data] flows=/home/chy/mambafortrafficmodeling/datasets/iscxtor2016/processed/flows.parquet packets_source=/home/chy/mambafortrafficmodeling/datasets/iscxtor2016/processed/packets.npz +[data] using external flow features D=20 +[data] rows total=103,079 keep len>=2: 66,189 +[data] benign=64,877 attack=1,312 -> train=51,901 val=10,000 +[data] train=10,000 val=10,000 attack=1,312 D=20 +[saved] artifacts/baselines/shafir_nf_2026_04_29/iscxtor_within_seed43.json +[saved] artifacts/baselines/shafir_nf_2026_04_29/iscxtor_within_seed43.npz +[result] AUROC=0.9449 AUPRC=0.7116 train=264.6s score=6.8s +[done] elapsed=280s artifacts/baselines/shafir_nf_2026_04_29/iscxtor_within_seed43.json +=== protocol=iscxtor_within seed=44 epochs=100 opt=sgd lr=0.001 === +[run] shafir_nf protocol=iscxtor_within seed=44 +[run] using normalization stats from /home/chy/mambafortrafficmodeling/artifacts/phase25_multiseed_2026_04_25/iscxtor2016_lambda0p3_seed44/model.pt (source ckpt) +[data] flows=/home/chy/mambafortrafficmodeling/datasets/iscxtor2016/processed/flows.parquet packets_source=/home/chy/mambafortrafficmodeling/datasets/iscxtor2016/processed/packets.npz +[data] using external flow features D=20 +[data] rows total=103,079 keep len>=2: 66,189 +[data] benign=64,877 attack=1,312 -> train=51,901 val=10,000 +[data] train=10,000 val=10,000 attack=1,312 D=20 +[saved] artifacts/baselines/shafir_nf_2026_04_29/iscxtor_within_seed44.json +[saved] artifacts/baselines/shafir_nf_2026_04_29/iscxtor_within_seed44.npz +[result] AUROC=0.9338 AUPRC=0.7069 train=259.3s score=6.7s +[done] elapsed=275s artifacts/baselines/shafir_nf_2026_04_29/iscxtor_within_seed44.json +=== protocol=cicids_within seed=42 epochs=100 opt=sgd lr=0.001 === +[run] shafir_nf protocol=cicids_within seed=42 +[run] using normalization stats from /home/chy/mambafortrafficmodeling/artifacts/phase25_sigma06_multiseed_2026_04_25/cicids2017_lambda0p3_sigma0p6_seed42/model.pt (source ckpt) +[data] flows=/home/chy/mambafortrafficmodeling/datasets/cicids2017/processed/flows.parquet packets_source=/home/chy/mambafortrafficmodeling/datasets/cicids2017/processed/packets.npz +[data] using external flow features D=20 +[data] rows total=2,025,564 keep len>=2: 2,017,180 +[data] benign=1,513,450 attack=30,000 -> train=1,210,760 val=10,000 +[data] train=10,000 val=10,000 attack=30,000 D=20 +[saved] artifacts/baselines/shafir_nf_2026_04_29/cicids_within_seed42.json +[saved] artifacts/baselines/shafir_nf_2026_04_29/cicids_within_seed42.npz +[result] AUROC=0.9413 AUPRC=0.9741 train=261.5s score=8.8s +[done] elapsed=377s artifacts/baselines/shafir_nf_2026_04_29/cicids_within_seed42.json +=== protocol=cicids_within seed=43 epochs=100 opt=sgd lr=0.001 === +[run] shafir_nf protocol=cicids_within seed=43 +[run] using normalization stats from /home/chy/mambafortrafficmodeling/artifacts/phase25_sigma06_multiseed_2026_04_25/cicids2017_lambda0p3_sigma0p6_seed43/model.pt (source ckpt) +[data] flows=/home/chy/mambafortrafficmodeling/datasets/cicids2017/processed/flows.parquet packets_source=/home/chy/mambafortrafficmodeling/datasets/cicids2017/processed/packets.npz +[data] using external flow features D=20 +[data] rows total=2,025,564 keep len>=2: 2,017,180 +[data] benign=1,513,450 attack=30,000 -> train=1,210,760 val=10,000 +[data] train=10,000 val=10,000 attack=30,000 D=20 +[saved] artifacts/baselines/shafir_nf_2026_04_29/cicids_within_seed43.json +[saved] artifacts/baselines/shafir_nf_2026_04_29/cicids_within_seed43.npz +[result] AUROC=0.9307 AUPRC=0.9700 train=261.0s score=9.1s +[done] elapsed=378s artifacts/baselines/shafir_nf_2026_04_29/cicids_within_seed43.json +=== protocol=cicids_within seed=44 epochs=100 opt=sgd lr=0.001 === +[run] shafir_nf protocol=cicids_within seed=44 +[run] using normalization stats from /home/chy/mambafortrafficmodeling/artifacts/phase25_sigma06_multiseed_2026_04_25/cicids2017_lambda0p3_sigma0p6_seed44/model.pt (source ckpt) +[data] flows=/home/chy/mambafortrafficmodeling/datasets/cicids2017/processed/flows.parquet packets_source=/home/chy/mambafortrafficmodeling/datasets/cicids2017/processed/packets.npz +[data] using external flow features D=20 +[data] rows total=2,025,564 keep len>=2: 2,017,180 +[data] benign=1,513,450 attack=30,000 -> train=1,210,760 val=10,000 +[data] train=10,000 val=10,000 attack=30,000 D=20 +[saved] artifacts/baselines/shafir_nf_2026_04_29/cicids_within_seed44.json +[saved] artifacts/baselines/shafir_nf_2026_04_29/cicids_within_seed44.npz +[result] AUROC=0.9048 AUPRC=0.9571 train=256.9s score=9.0s +[done] elapsed=373s artifacts/baselines/shafir_nf_2026_04_29/cicids_within_seed44.json +=== protocol=cicddos_within seed=42 epochs=100 opt=sgd lr=0.001 === +[run] shafir_nf protocol=cicddos_within seed=42 +[run] using normalization stats from /home/chy/mambafortrafficmodeling/artifacts/phase25_multiseed_2026_04_25/cicddos2019_lambda0p3_seed42/model.pt (source ckpt) +[data] flows=/home/chy/mambafortrafficmodeling/datasets/cicddos2019/processed/flows.parquet packets_source=/home/chy/mambafortrafficmodeling/datasets/cicddos2019/processed/full_store +[data] using external flow features D=20 +[data] rows total=8,993,376 keep len>=2: 8,986,875 +[data] benign=93,207 attack=20,000 -> train=74,565 val=18,642 +[data] train=10,000 val=10,000 attack=20,000 D=20 +[saved] artifacts/baselines/shafir_nf_2026_04_29/cicddos_within_seed42.json +[saved] artifacts/baselines/shafir_nf_2026_04_29/cicddos_within_seed42.npz +[result] AUROC=0.8472 AUPRC=0.9238 train=257.8s score=7.9s +[done] elapsed=279s artifacts/baselines/shafir_nf_2026_04_29/cicddos_within_seed42.json +=== protocol=cicddos_within seed=43 epochs=100 opt=sgd lr=0.001 === +[run] shafir_nf protocol=cicddos_within seed=43 +[run] using normalization stats from /home/chy/mambafortrafficmodeling/artifacts/phase25_multiseed_2026_04_25/cicddos2019_lambda0p3_seed43/model.pt (source ckpt) +[data] flows=/home/chy/mambafortrafficmodeling/datasets/cicddos2019/processed/flows.parquet packets_source=/home/chy/mambafortrafficmodeling/datasets/cicddos2019/processed/full_store +[data] using external flow features D=20 +[data] rows total=8,993,376 keep len>=2: 8,986,875 +[data] benign=93,207 attack=20,000 -> train=74,565 val=18,642 +[data] train=10,000 val=10,000 attack=20,000 D=20 +[saved] artifacts/baselines/shafir_nf_2026_04_29/cicddos_within_seed43.json +[saved] artifacts/baselines/shafir_nf_2026_04_29/cicddos_within_seed43.npz +[result] AUROC=0.9214 AUPRC=0.9576 train=256.9s score=8.0s +[done] elapsed=279s artifacts/baselines/shafir_nf_2026_04_29/cicddos_within_seed43.json +=== protocol=cicddos_within seed=44 epochs=100 opt=sgd lr=0.001 === +[run] shafir_nf protocol=cicddos_within seed=44 +[run] using normalization stats from /home/chy/mambafortrafficmodeling/artifacts/phase25_multiseed_2026_04_25/cicddos2019_lambda0p3_seed44/model.pt (source ckpt) +[data] flows=/home/chy/mambafortrafficmodeling/datasets/cicddos2019/processed/flows.parquet packets_source=/home/chy/mambafortrafficmodeling/datasets/cicddos2019/processed/full_store +[data] using external flow features D=20 +[data] rows total=8,993,376 keep len>=2: 8,986,875 +[data] benign=93,207 attack=20,000 -> train=74,565 val=18,642 +[data] train=10,000 val=10,000 attack=20,000 D=20 +[saved] artifacts/baselines/shafir_nf_2026_04_29/cicddos_within_seed44.json +[saved] artifacts/baselines/shafir_nf_2026_04_29/cicddos_within_seed44.npz +[result] AUROC=0.9024 AUPRC=0.9456 train=256.9s score=7.9s +[done] elapsed=278s artifacts/baselines/shafir_nf_2026_04_29/cicddos_within_seed44.json +=== protocol=forward_cross seed=42 epochs=100 opt=sgd lr=0.001 === +[run] shafir_nf protocol=forward_cross seed=42 +[run] using normalization stats from /home/chy/mambafortrafficmodeling/artifacts/phase25_sigma06_multiseed_2026_04_25/cicids2017_lambda0p3_sigma0p6_seed42/model.pt (source ckpt) +[data] train=10,000 val=10,000 attack=9,846 D=20 +[saved] artifacts/baselines/shafir_nf_2026_04_29/forward_cross_seed42.json +[saved] artifacts/baselines/shafir_nf_2026_04_29/forward_cross_seed42.npz +[result] AUROC=0.9082 AUPRC=0.8476 train=260.8s score=7.5s +[done] elapsed=279s artifacts/baselines/shafir_nf_2026_04_29/forward_cross_seed42.json +=== protocol=forward_cross seed=43 epochs=100 opt=sgd lr=0.001 === +[run] shafir_nf protocol=forward_cross seed=43 +[run] using normalization stats from /home/chy/mambafortrafficmodeling/artifacts/phase25_sigma06_multiseed_2026_04_25/cicids2017_lambda0p3_sigma0p6_seed43/model.pt (source ckpt) +[data] train=10,000 val=10,000 attack=9,846 D=20 +[saved] artifacts/baselines/shafir_nf_2026_04_29/forward_cross_seed43.json +[saved] artifacts/baselines/shafir_nf_2026_04_29/forward_cross_seed43.npz +[result] AUROC=0.9280 AUPRC=0.8643 train=257.2s score=7.2s +[done] elapsed=275s artifacts/baselines/shafir_nf_2026_04_29/forward_cross_seed43.json +=== protocol=forward_cross seed=44 epochs=100 opt=sgd lr=0.001 === +[run] shafir_nf protocol=forward_cross seed=44 +[run] using normalization stats from /home/chy/mambafortrafficmodeling/artifacts/phase25_sigma06_multiseed_2026_04_25/cicids2017_lambda0p3_sigma0p6_seed44/model.pt (source ckpt) +[data] train=10,000 val=10,000 attack=9,846 D=20 +[saved] artifacts/baselines/shafir_nf_2026_04_29/forward_cross_seed44.json +[saved] artifacts/baselines/shafir_nf_2026_04_29/forward_cross_seed44.npz +[result] AUROC=0.9269 AUPRC=0.9213 train=257.2s score=7.3s +[done] elapsed=275s artifacts/baselines/shafir_nf_2026_04_29/forward_cross_seed44.json +=== protocol=reverse_cross seed=42 epochs=100 opt=sgd lr=0.001 === +[run] shafir_nf protocol=reverse_cross seed=42 +[run] using normalization stats from /home/chy/mambafortrafficmodeling/artifacts/phase25_multiseed_2026_04_25/cicddos2019_lambda0p3_seed42/model.pt (source ckpt) +[data] train=10,000 val=10,000 attack=6,772 D=20 +[saved] artifacts/baselines/shafir_nf_2026_04_29/reverse_cross_seed42.json +[saved] artifacts/baselines/shafir_nf_2026_04_29/reverse_cross_seed42.npz +[result] AUROC=0.7206 AUPRC=0.6950 train=257.1s score=7.4s +[done] elapsed=270s artifacts/baselines/shafir_nf_2026_04_29/reverse_cross_seed42.json +=== protocol=reverse_cross seed=43 epochs=100 opt=sgd lr=0.001 === +[run] shafir_nf protocol=reverse_cross seed=43 +[run] using normalization stats from /home/chy/mambafortrafficmodeling/artifacts/phase25_multiseed_2026_04_25/cicddos2019_lambda0p3_seed43/model.pt (source ckpt) +[data] train=10,000 val=10,000 attack=6,772 D=20 +[saved] artifacts/baselines/shafir_nf_2026_04_29/reverse_cross_seed43.json +[saved] artifacts/baselines/shafir_nf_2026_04_29/reverse_cross_seed43.npz +[result] AUROC=0.7262 AUPRC=0.7119 train=257.5s score=7.4s +[done] elapsed=271s artifacts/baselines/shafir_nf_2026_04_29/reverse_cross_seed43.json +=== protocol=reverse_cross seed=44 epochs=100 opt=sgd lr=0.001 === +[run] shafir_nf protocol=reverse_cross seed=44 +[run] using normalization stats from /home/chy/mambafortrafficmodeling/artifacts/phase25_multiseed_2026_04_25/cicddos2019_lambda0p3_seed44/model.pt (source ckpt) +[data] train=10,000 val=10,000 attack=6,772 D=20 +[saved] artifacts/baselines/shafir_nf_2026_04_29/reverse_cross_seed44.json +[saved] artifacts/baselines/shafir_nf_2026_04_29/reverse_cross_seed44.npz +[result] AUROC=0.7272 AUPRC=0.6895 train=257.7s score=7.2s +[done] elapsed=271s artifacts/baselines/shafir_nf_2026_04_29/reverse_cross_seed44.json +ALL DONE diff --git a/artifacts/baselines/shafir_nf_2026_04_29/reverse_cross_seed42.json b/artifacts/baselines/shafir_nf_2026_04_29/reverse_cross_seed42.json new file mode 100644 index 0000000..a3b5692 --- /dev/null +++ b/artifacts/baselines/shafir_nf_2026_04_29/reverse_cross_seed42.json @@ -0,0 +1,86 @@ +{ + "method": "shafir_nf", + "protocol": "reverse_cross", + "seed": 42, + "model_dir": "/home/chy/mambafortrafficmodeling/artifacts/phase25_multiseed_2026_04_25/cicddos2019_lambda0p3_seed42", + "n_train": 10000, + "n_val": 10000, + "n_atk": 6772, + "epochs": 100, + "lr": 0.001, + "optimizer": "sgd", + "t_train_sec": 257.14, + "t_score_sec": 7.35, + "loss_first_last": [ + 327.9974060058594, + -14.816756248474121 + ], + "overall": { + "neg_log_prob": { + "auroc": 0.7206432811577081, + "auprc": 0.6950180575372344 + } + }, + "per_class": { + "Botnet": { + "_n": 666.0, + "auroc": 0.564331081081081 + }, + "DDoS": { + "_n": 666.0, + "auroc": 0.9866227477477477 + }, + "DoS GoldenEye": { + "_n": 666.0, + "auroc": 0.9851037537537537 + }, + "DoS Hulk": { + "_n": 666.0, + "auroc": 0.8369624624624625 + }, + "DoS Slowhttptest": { + "_n": 666.0, + "auroc": 0.9183935435435435 + }, + "DoS Slowloris": { + "_n": 666.0, + "auroc": 0.8349857357357358 + }, + "FTP-Patator": { + "_n": 666.0, + "auroc": 0.8203914414414415 + }, + "Heartbleed": { + "_n": 1.0, + "auroc": 0.9994 + }, + "Infiltration": { + "_n": 7.0, + "auroc": 0.9531285714285714 + }, + "Infiltration - Portscan": { + "_n": 666.0, + "auroc": 0.19368243243243244 + }, + "Portscan": { + "_n": 666.0, + "auroc": 0.29466171171171174 + }, + "SSH-Patator": { + "_n": 666.0, + "auroc": 0.7276059309309308 + }, + "Web Attack - Brute Force": { + "_n": 73.0, + "auroc": 0.9990369863013698 + }, + "Web Attack - SQL Injection": { + "_n": 13.0, + "auroc": 0.8631230769230769 + }, + "Web Attack - XSS": { + "_n": 18.0, + "auroc": 0.9994055555555555 + } + } +} \ No newline at end of file diff --git a/artifacts/baselines/shafir_nf_2026_04_29/reverse_cross_seed42.npz b/artifacts/baselines/shafir_nf_2026_04_29/reverse_cross_seed42.npz new file mode 100644 index 0000000..abd8830 Binary files /dev/null and b/artifacts/baselines/shafir_nf_2026_04_29/reverse_cross_seed42.npz differ diff --git a/artifacts/baselines/shafir_nf_2026_04_29/reverse_cross_seed43.json b/artifacts/baselines/shafir_nf_2026_04_29/reverse_cross_seed43.json new file mode 100644 index 0000000..061270b --- /dev/null +++ b/artifacts/baselines/shafir_nf_2026_04_29/reverse_cross_seed43.json @@ -0,0 +1,86 @@ +{ + "method": "shafir_nf", + "protocol": "reverse_cross", + "seed": 43, + "model_dir": "/home/chy/mambafortrafficmodeling/artifacts/phase25_multiseed_2026_04_25/cicddos2019_lambda0p3_seed43", + "n_train": 10000, + "n_val": 10000, + "n_atk": 6772, + "epochs": 100, + "lr": 0.001, + "optimizer": "sgd", + "t_train_sec": 257.5, + "t_score_sec": 7.38, + "loss_first_last": [ + 325.5654602050781, + -15.601054191589355 + ], + "overall": { + "neg_log_prob": { + "auroc": 0.7262257752510336, + "auprc": 0.7119102968304621 + } + }, + "per_class": { + "Botnet": { + "_n": 666.0, + "auroc": 0.5299815315315316 + }, + "DDoS": { + "_n": 666.0, + "auroc": 0.9887840840840839 + }, + "DoS GoldenEye": { + "_n": 666.0, + "auroc": 0.9720243243243243 + }, + "DoS Hulk": { + "_n": 666.0, + "auroc": 0.8756828828828829 + }, + "DoS Slowhttptest": { + "_n": 666.0, + "auroc": 0.9303788288288288 + }, + "DoS Slowloris": { + "_n": 666.0, + "auroc": 0.869046846846847 + }, + "FTP-Patator": { + "_n": 666.0, + "auroc": 0.7730457957957959 + }, + "Heartbleed": { + "_n": 1.0, + "auroc": 0.9994000000000001 + }, + "Infiltration": { + "_n": 7.0, + "auroc": 0.9770714285714286 + }, + "Infiltration - Portscan": { + "_n": 666.0, + "auroc": 0.15571493993993996 + }, + "Portscan": { + "_n": 666.0, + "auroc": 0.3043493993993994 + }, + "SSH-Patator": { + "_n": 666.0, + "auroc": 0.8189927927927928 + }, + "Web Attack - Brute Force": { + "_n": 73.0, + "auroc": 0.9994000000000001 + }, + "Web Attack - SQL Injection": { + "_n": 13.0, + "auroc": 0.9252076923076923 + }, + "Web Attack - XSS": { + "_n": 18.0, + "auroc": 0.9994000000000001 + } + } +} \ No newline at end of file diff --git a/artifacts/baselines/shafir_nf_2026_04_29/reverse_cross_seed43.npz b/artifacts/baselines/shafir_nf_2026_04_29/reverse_cross_seed43.npz new file mode 100644 index 0000000..f2b4eea Binary files /dev/null and b/artifacts/baselines/shafir_nf_2026_04_29/reverse_cross_seed43.npz differ diff --git a/artifacts/baselines/shafir_nf_2026_04_29/reverse_cross_seed44.json b/artifacts/baselines/shafir_nf_2026_04_29/reverse_cross_seed44.json new file mode 100644 index 0000000..92a62ab --- /dev/null +++ b/artifacts/baselines/shafir_nf_2026_04_29/reverse_cross_seed44.json @@ -0,0 +1,86 @@ +{ + "method": "shafir_nf", + "protocol": "reverse_cross", + "seed": 44, + "model_dir": 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--- /dev/null +++ b/artifacts/baselines/shafir_nf_2026_04_29/summary.json @@ -0,0 +1,427 @@ +{ + "rows": [ + { + "protocol": "iscxtor_within", + "n_seeds": 3, + "auroc_mean": 0.942223081808943, + "auroc_std": 0.007453255931041965, + "auprc_mean": 0.7142034224819499, + "auprc_std": 0.008834309581256158, + "t_train_sec_mean": 261.9066666666667 + }, + { + "protocol": "cicids_within", + "n_seeds": 3, + "auroc_mean": 0.92558891, + "auroc_std": 0.018789549992836517, + "auprc_mean": 0.9670551444666379, + "auprc_std": 0.008896428495726581, + "t_train_sec_mean": 259.81666666666666 + }, + { + "protocol": "cicddos_within", + "n_seeds": 3, + "auroc_mean": 0.8903352299999999, + "auroc_std": 0.03856258124281013, + "auprc_mean": 0.9423452070590059, + "auprc_std": 0.017182152401310335, + "t_train_sec_mean": 257.17 + }, + { + "protocol": "forward_cross", + "n_seeds": 3, + "auroc_mean": 0.9209875905613109, + "auroc_std": 0.011130094115374828, + "auprc_mean": 0.8777569590043698, + "auprc_std": 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0.19087850350350352, + "std": 0.03384881219196844 + }, + "Portscan": { + "n": 666, + "mean": 0.31924224224224224, + "std": 0.03452641347271461 + }, + "SSH-Patator": { + "n": 666, + "mean": 0.7735229479479478, + "std": 0.04569507197120312 + }, + "Web Attack - Brute Force": { + "n": 73, + "mean": 0.999287899543379, + "std": 0.00021770732277235199 + }, + "Web Attack - SQL Injection": { + "n": 13, + "mean": 0.8723051282051282, + "std": 0.04896159002555345 + }, + "Web Attack - XSS": { + "n": 18, + "mean": 0.9995518518518519, + "std": 0.00025821881173242666 + } + } + }, + "baselines": { + "terminal_norm": { + "iscxtor_within": [ + 0.9945, + 0.0011 + ], + "cicids_within": [ + 0.9858, + 0.0021 + ], + "cicddos_within": [ + 0.996, + 0.001 + ], + "forward_cross": [ + 0.9109, + 0.0032 + ], + "reverse_cross": [ + 0.5999, + null + ] + }, + "shafir_paper": { + "iscxtor_within": [ + 0.8731, + null + ], + "cicids_within": [ + 0.9303, + null + ], + "cicddos_within": [ + 0.93, + null + ], + "forward_cross": [ + 0.89, + null + ], + "reverse_cross": [ + 0.93, + null + ] + } + } +} \ No newline at end of file diff --git a/artifacts/baselines/shafir_nf_2026_04_29/summary.md b/artifacts/baselines/shafir_nf_2026_04_29/summary.md new file mode 100644 index 0000000..b1b52c0 --- /dev/null +++ b/artifacts/baselines/shafir_nf_2026_04_29/summary.md @@ -0,0 +1,71 @@ +# Shafir 2026 NF Baseline — On Our 5-Protocol Layout + +Date: 2026-04-29 + +Method: Shafir's official `pzflow.Flow` (single basic NF). +Features: our **20-d canonical packet-derived flow features** (`common.data_contract.CANONICAL_FLOW_FEATURE_NAMES`), z-scored with the **same source training stats** that the Unified_CFM checkpoint uses. +Train cap: 10,000 source-benign samples (Shafir paper protocol). +Optimizer: SGD lr=1e-3, 100 epochs (Shafir paper defaults). +Sampling: same seeds & stratification as `eval_new_scores.py`. + +## Headline AUROC (3-seed mean ± std) + +| Protocol | terminal_norm (ours) | Shafir NF — paper | **Shafir NF — our features** | Δ vs paper | Δ vs terminal_norm | +|---|---:|---:|---:|---:|---:| +| ISCXTor2016 within | 0.9945 ± 0.0011 | 0.8731 | **0.9422 ± 0.0075** | +0.0691 | -0.0523 | +| CICIDS2017 within (σ=0.6) | 0.9858 ± 0.0021 | 0.9303 | **0.9256 ± 0.0188** | -0.0047 | -0.0602 | +| CICDDoS2019 within | 0.9960 ± 0.0010 | 0.9300 | **0.8903 ± 0.0386** | -0.0397 | -0.1057 | +| IDS2017→DDoS2019 forward | 0.9109 ± 0.0032 | 0.8900 | **0.9210 ± 0.0111** | +0.0310 | +0.0101 | +| DDoS2019→IDS2017 reverse | 0.5999 | 0.9300 | **0.7247 ± 0.0035** | -0.2053 | +0.1248 | + +## Per-protocol stats + +| Protocol | n_seeds | AUPRC mean ± std | Train time (s, mean) | +|---|---:|---:|---:| +| ISCXTor2016 within | 3 | 0.7142 ± 0.0088 | 261.9 | +| CICIDS2017 within (σ=0.6) | 3 | 0.9671 ± 0.0089 | 259.8 | +| CICDDoS2019 within | 3 | 0.9423 ± 0.0172 | 257.2 | +| IDS2017→DDoS2019 forward | 3 | 0.8778 ± 0.0386 | 258.4 | +| DDoS2019→IDS2017 reverse | 3 | 0.6988 ± 0.0117 | 257.4 | + +## Per-attack (forward + reverse) + +### IDS2017→DDoS2019 forward +| attack | n | Shafir NF AUROC mean ± std | +|---|---:|---:| +| `DrDoS_DNS` | 588 | 0.9725 ± 0.0150 | +| `DrDoS_LDAP` | 588 | 0.9806 ± 0.0163 | +| `DrDoS_MSSQL` | 588 | 0.9627 ± 0.0046 | +| `DrDoS_NTP` | 588 | 0.9619 ± 0.0057 | +| `DrDoS_NetBIOS` | 588 | 0.9118 ± 0.0355 | +| `DrDoS_SNMP` | 588 | 0.9773 ± 0.0168 | +| `DrDoS_SSDP` | 588 | 0.9604 ± 0.0043 | +| `DrDoS_UDP` | 588 | 0.9602 ± 0.0047 | +| `LDAP` | 588 | 0.9802 ± 0.0159 | +| `MSSQL` | 588 | 0.9623 ± 0.0047 | +| `NetBIOS` | 588 | 0.9116 ± 0.0332 | +| `Portmap` | 588 | 0.9109 ± 0.0350 | +| `Syn` | 588 | 0.7662 ± 0.0218 | +| `TFTP` | 588 | 0.9671 ± 0.0083 | +| `UDP` | 588 | 0.9600 ± 0.0042 | +| `UDPLag` | 588 | 0.8358 ± 0.0409 | +| `WebDDoS` | 438 | 0.5909 ± 0.0541 | + +### DDoS2019→IDS2017 reverse +| attack | n | Shafir NF AUROC mean ± std | +|---|---:|---:| +| `Botnet` | 666 | 0.5626 ± 0.0319 | +| `DDoS` | 666 | 0.9829 ± 0.0085 | +| `DoS GoldenEye` | 666 | 0.9771 ± 0.0070 | +| `DoS Hulk` | 666 | 0.8233 ± 0.0603 | +| `DoS Slowhttptest` | 666 | 0.9212 ± 0.0082 | +| `DoS Slowloris` | 666 | 0.8603 ± 0.0223 | +| `FTP-Patator` | 666 | 0.7923 ± 0.0249 | +| `Heartbleed` | 1 | 0.9995 ± 0.0002 | +| `Infiltration` | 7 | 0.9727 ± 0.0177 | +| `Infiltration - Portscan` | 666 | 0.1909 ± 0.0338 | +| `Portscan` | 666 | 0.3192 ± 0.0345 | +| `SSH-Patator` | 666 | 0.7735 ± 0.0457 | +| `Web Attack - Brute Force` | 73 | 0.9993 ± 0.0002 | +| `Web Attack - SQL Injection` | 13 | 0.8723 ± 0.0490 | +| `Web Attack - XSS` | 18 | 0.9996 ± 0.0003 | \ No newline at end of file diff --git a/artifacts/baselines/shafir_nf_csv_2026_04_29/cicddos_within_seed42.json b/artifacts/baselines/shafir_nf_csv_2026_04_29/cicddos_within_seed42.json new file mode 100644 index 0000000..c409506 --- /dev/null +++ b/artifacts/baselines/shafir_nf_csv_2026_04_29/cicddos_within_seed42.json @@ -0,0 +1,107 @@ +{ + "method": "shafir_nf_csv", + "protocol": "cicddos_within", + "seed": 42, + "src_dataset": "cicddos2019", + "tgt_dataset": "cicddos2019", + "feature_set": [ + "Bwd Packet Length Mean", + "Fwd Packets/s", + "ACK Flag Count", + "Total Length of Bwd Packets", + "Flow Duration" + ], + "n_features": 5, + "n_train": 10000, + "n_val": 10000, + "n_atk": 19326, + "epochs": 100, + "lr": 0.001, + "optimizer": "sgd", + "t_train_sec": 40.04, + "t_score_sec": 5.86, + "loss_first_last": [ + 71.0317611694336, + -4.317458152770996 + ], + "overall": { + "neg_log_prob": { + "auroc": 0.5926356229949291, + "auprc": 0.7165061652735046 + } + }, + "per_class": { + "DrDoS_DNS": { + "_n": 1111.0, + "auroc": 0.6139290729072907 + }, + "DrDoS_LDAP": { + "_n": 1111.0, + "auroc": 0.6433729072907292 + }, + "DrDoS_MSSQL": { + "_n": 1111.0, + "auroc": 0.6326594509450945 + }, + "DrDoS_NTP": { + "_n": 1111.0, + "auroc": 0.4430835733573357 + }, + "DrDoS_NetBIOS": { + "_n": 1111.0, + "auroc": 0.6186818631863186 + }, + "DrDoS_SNMP": { + "_n": 1111.0, + "auroc": 0.7100597659765977 + }, + "DrDoS_SSDP": { + "_n": 1111.0, + "auroc": 0.3524760576057606 + }, + "DrDoS_UDP": { + "_n": 1111.0, + "auroc": 0.33319734473447343 + }, + "LDAP": { + "_n": 1111.0, + "auroc": 0.6416021602160216 + }, + "MSSQL": { + "_n": 1111.0, + "auroc": 0.6149669666966697 + }, + "NetBIOS": { + "_n": 1111.0, + "auroc": 0.5974762826282629 + }, + "Portmap": { + "_n": 1111.0, + "auroc": 0.603714491449145 + }, + "Syn": { + "_n": 1111.0, + "auroc": 0.8015058055805581 + }, + "TFTP": { + "_n": 1111.0, + "auroc": 0.6585283528352835 + }, + "UDP": { + "_n": 1111.0, + "auroc": 0.3034975247524752 + }, + "UDP-lag": { + "_n": 1111.0, + "auroc": 0.8040582808280827 + }, + "UDPLag": { + "_n": 1111.0, + "auroc": 0.7684298379837983 + }, + "WebDDoS": { + "_n": 439.0, + "auroc": 0.42450728929384973 + } + } +} \ No newline at end of file diff --git a/artifacts/baselines/shafir_nf_csv_2026_04_29/cicddos_within_seed42.npz b/artifacts/baselines/shafir_nf_csv_2026_04_29/cicddos_within_seed42.npz new file mode 100644 index 0000000..40dd960 Binary files /dev/null and b/artifacts/baselines/shafir_nf_csv_2026_04_29/cicddos_within_seed42.npz differ diff --git a/artifacts/baselines/shafir_nf_csv_2026_04_29/cicids_within_seed42.json b/artifacts/baselines/shafir_nf_csv_2026_04_29/cicids_within_seed42.json new file mode 100644 index 0000000..b306d68 --- /dev/null +++ b/artifacts/baselines/shafir_nf_csv_2026_04_29/cicids_within_seed42.json @@ -0,0 +1,95 @@ +{ + "method": "shafir_nf_csv", + "protocol": "cicids_within", + "seed": 42, + "src_dataset": "cicids2017", + "tgt_dataset": "cicids2017", + "feature_set": [ + "Bwd Packet Length Mean", + "Fwd Packets/s", + "ACK Flag Count", + "Total Length of Bwd Packets", + "Flow Duration" + ], + "n_features": 5, + "n_train": 10000, + "n_val": 10000, + "n_atk": 18627, + "epochs": 100, + "lr": 0.001, + "optimizer": "sgd", + "t_train_sec": 40.07, + "t_score_sec": 5.75, + "loss_first_last": [ + 73.90901184082031, + -0.6857205629348755 + ], + "overall": { + "neg_log_prob": { + "auroc": 0.867762632200569, + "auprc": 0.906023161909059 + } + }, + "per_class": { + "Botnet": { + "_n": 736.0, + "auroc": 0.5920464673913043 + }, + "DDoS": { + "_n": 2000.0, + "auroc": 0.99385725 + }, + "DoS GoldenEye": { + "_n": 2000.0, + "auroc": 0.9764343 + }, + "DoS Hulk": { + "_n": 2000.0, + "auroc": 0.9732884749999999 + }, + "DoS Slowhttptest": { + "_n": 1740.0, + "auroc": 0.893629367816092 + }, + "DoS Slowloris": { + "_n": 2000.0, + "auroc": 0.865186725 + }, + "FTP-Patator": { + "_n": 2000.0, + "auroc": 0.84971115 + }, + "Heartbleed": { + "_n": 11.0, + "auroc": 0.99995 + }, + "Infiltration": { + "_n": 36.0, + "auroc": 0.9523083333333333 + }, + "Infiltration - Portscan": { + "_n": 2000.0, + "auroc": 0.766433625 + }, + "Portscan": { + "_n": 2000.0, + "auroc": 0.6718488499999999 + }, + "SSH-Patator": { + "_n": 2000.0, + "auroc": 0.917101925 + }, + "Web Attack - Brute Force": { + "_n": 73.0, + "auroc": 0.9899150684931507 + }, + "Web Attack - SQL Injection": { + "_n": 13.0, + "auroc": 0.7684 + }, + "Web Attack - XSS": { + "_n": 18.0, + "auroc": 0.9940611111111112 + } + } +} \ No newline at end of file diff --git a/artifacts/baselines/shafir_nf_csv_2026_04_29/cicids_within_seed42.npz b/artifacts/baselines/shafir_nf_csv_2026_04_29/cicids_within_seed42.npz new file mode 100644 index 0000000..9bc9d12 Binary files /dev/null and b/artifacts/baselines/shafir_nf_csv_2026_04_29/cicids_within_seed42.npz differ diff --git a/artifacts/baselines/shafir_nf_csv_2026_04_29/ciciot_within_seed42.json b/artifacts/baselines/shafir_nf_csv_2026_04_29/ciciot_within_seed42.json new file mode 100644 index 0000000..2bde53b --- /dev/null +++ b/artifacts/baselines/shafir_nf_csv_2026_04_29/ciciot_within_seed42.json @@ -0,0 +1,166 @@ +{ + "method": "shafir_nf_csv", + "protocol": "ciciot_within", + "seed": 42, + "src_dataset": "ciciot2023", + "tgt_dataset": "ciciot2023", + "feature_set": [ + "HTTPS", + "Protocol Type", + "Variance", + "fin_count" + ], + "n_features": 4, + "n_train": 10000, + "n_val": 10000, + "n_atk": 29997, + "epochs": 100, + "lr": 0.001, + "optimizer": "sgd", + "t_train_sec": 31.21, + "t_score_sec": 6.11, + "loss_first_last": [ + 33.33341979980469, + -2.218377113342285 + ], + "overall": { + "neg_log_prob": { + "auroc": 0.7398053438677201, + "auprc": 0.8936168458756697 + } + }, + "per_class": { + "Backdoor_Malware": { + "_n": 909.0, + "auroc": 0.5919983498349836 + }, + "BrowserHijacking": { + "_n": 909.0, + "auroc": 0.566808195819582 + }, + "CommandInjection": { + "_n": 909.0, + "auroc": 0.6124037403740373 + }, + "DDoS-ACK_Fragmentation": { + "_n": 909.0, + "auroc": 0.7994019801980198 + }, + "DDoS-HTTP_Flood": { + "_n": 909.0, + "auroc": 0.9393508800880088 + }, + "DDoS-ICMP_Flood": { + "_n": 909.0, + "auroc": 0.9968105610561057 + }, + "DDoS-ICMP_Fragmentation": { + "_n": 909.0, + "auroc": 0.9965641364136413 + }, + "DDoS-PSHACK_FLOOD": { + "_n": 909.0, + "auroc": 0.651739493949395 + }, + "DDoS-RSTFINFLOOD": { + "_n": 909.0, + "auroc": 0.9996689768976897 + }, + "DDoS-SYN_Flood": { + "_n": 909.0, + "auroc": 0.6499974147414742 + }, + "DDoS-SlowLoris": { + "_n": 909.0, + "auroc": 0.8312375137513751 + }, + "DDoS-SynonymousIP_Flood": { + "_n": 909.0, + "auroc": 0.6367579207920793 + }, + "DDoS-TCP_Flood": { + "_n": 909.0, + "auroc": 0.6669976897689769 + }, + "DDoS-UDP_Flood": { + "_n": 909.0, + "auroc": 0.8217078657865786 + }, + "DDoS-UDP_Fragmentation": { + "_n": 909.0, + "auroc": 0.8797891089108911 + }, + "DNS_Spoofing": { + "_n": 909.0, + "auroc": 0.6426074257425743 + }, + "DictionaryBruteForce": { + "_n": 909.0, + "auroc": 0.6139754675467546 + }, + "DoS-HTTP_Flood": { + "_n": 909.0, + "auroc": 0.8607427392739274 + }, + "DoS-SYN_Flood": { + "_n": 909.0, + "auroc": 0.6604723872387239 + }, + "DoS-TCP_Flood": { + "_n": 909.0, + "auroc": 0.6440475247524753 + }, + "DoS-UDP_Flood": { + "_n": 909.0, + "auroc": 0.8215397139713971 + }, + "MITM-ArpSpoofing": { + "_n": 909.0, + "auroc": 0.4071366336633664 + }, + "Mirai-greeth_flood": { + "_n": 909.0, + "auroc": 0.9990102310231023 + }, + "Mirai-greip_flood": { + "_n": 909.0, + "auroc": 0.9977627062706271 + }, + "Mirai-udpplain": { + "_n": 909.0, + "auroc": 0.8218058855885588 + }, + "Recon-HostDiscovery": { + "_n": 909.0, + "auroc": 0.6198316281628162 + }, + "Recon-OSScan": { + "_n": 909.0, + "auroc": 0.6248277227722772 + }, + "Recon-PingSweep": { + "_n": 909.0, + "auroc": 0.6834787678767877 + }, + "Recon-PortScan": { + "_n": 909.0, + "auroc": 0.6672668316831682 + }, + "SqlInjection": { + "_n": 909.0, + "auroc": 0.7520440594059407 + }, + "Uploading_Attack": { + "_n": 909.0, + "auroc": 0.6258622112211221 + }, + "VulnerabilityScan": { + "_n": 909.0, + "auroc": 0.7400807480748074 + }, + "XSS": { + "_n": 909.0, + "auroc": 0.5898498349834984 + } + } +} \ No newline at end of file diff --git a/artifacts/baselines/shafir_nf_csv_2026_04_29/ciciot_within_seed42.npz b/artifacts/baselines/shafir_nf_csv_2026_04_29/ciciot_within_seed42.npz new file mode 100644 index 0000000..1deece7 Binary files /dev/null and b/artifacts/baselines/shafir_nf_csv_2026_04_29/ciciot_within_seed42.npz differ diff --git a/artifacts/baselines/shafir_nf_csv_2026_04_29/forward_cross_seed42.json b/artifacts/baselines/shafir_nf_csv_2026_04_29/forward_cross_seed42.json new file mode 100644 index 0000000..8071648 --- /dev/null +++ b/artifacts/baselines/shafir_nf_csv_2026_04_29/forward_cross_seed42.json @@ -0,0 +1,107 @@ +{ + "method": "shafir_nf_csv", + "protocol": "forward_cross", + "seed": 42, + "src_dataset": "cicids2017", + "tgt_dataset": "cicddos2019", + "feature_set": [ + "Bwd Packet Length Mean", + "Fwd Packets/s", + "ACK Flag Count", + "Total Length of Bwd Packets", + "Flow Duration" + ], + "n_features": 5, + "n_train": 10000, + "n_val": 10000, + "n_atk": 9874, + "epochs": 100, + "lr": 0.001, + "optimizer": "sgd", + "t_train_sec": 39.67, + "t_score_sec": 6.19, + "loss_first_last": [ + 74.62840270996094, + 0.061334509402513504 + ], + "overall": { + "neg_log_prob": { + "auroc": 0.7830720326108973, + "auprc": 0.763908135629693 + } + }, + "per_class": { + "DrDoS_DNS": { + "_n": 555.0, + "auroc": 0.8860481081081081 + }, + "DrDoS_LDAP": { + "_n": 555.0, + "auroc": 0.9076607207207208 + }, + "DrDoS_MSSQL": { + "_n": 555.0, + "auroc": 0.91927009009009 + }, + "DrDoS_NTP": { + "_n": 555.0, + "auroc": 0.6181672072072072 + }, + "DrDoS_NetBIOS": { + "_n": 555.0, + "auroc": 0.8909082882882882 + }, + "DrDoS_SNMP": { + "_n": 555.0, + "auroc": 0.8940033333333334 + }, + "DrDoS_SSDP": { + "_n": 555.0, + "auroc": 0.6142802702702702 + }, + "DrDoS_UDP": { + "_n": 555.0, + "auroc": 0.6115262162162163 + }, + "LDAP": { + "_n": 555.0, + "auroc": 0.9258804504504504 + }, + "MSSQL": { + "_n": 555.0, + "auroc": 0.8933911711711711 + }, + "NetBIOS": { + "_n": 555.0, + "auroc": 0.8636989189189188 + }, + "Portmap": { + "_n": 555.0, + "auroc": 0.880438018018018 + }, + "Syn": { + "_n": 555.0, + "auroc": 0.743508018018018 + }, + "TFTP": { + "_n": 555.0, + "auroc": 0.7372752252252253 + }, + "UDP": { + "_n": 555.0, + "auroc": 0.5768727927927928 + }, + "UDP-lag": { + "_n": 555.0, + "auroc": 0.7566461261261261 + }, + "UDPLag": { + "_n": 555.0, + "auroc": 0.8184645945945946 + }, + "WebDDoS": { + "_n": 439.0, + "auroc": 0.4975883826879271 + } + } +} \ No newline at end of file diff --git a/artifacts/baselines/shafir_nf_csv_2026_04_29/forward_cross_seed42.npz b/artifacts/baselines/shafir_nf_csv_2026_04_29/forward_cross_seed42.npz new file mode 100644 index 0000000..86ecc0b Binary files /dev/null and b/artifacts/baselines/shafir_nf_csv_2026_04_29/forward_cross_seed42.npz differ diff --git a/artifacts/baselines/shafir_nf_csv_2026_04_29/iscxtor_within_seed42.json b/artifacts/baselines/shafir_nf_csv_2026_04_29/iscxtor_within_seed42.json new file mode 100644 index 0000000..b1a41f7 --- /dev/null +++ b/artifacts/baselines/shafir_nf_csv_2026_04_29/iscxtor_within_seed42.json @@ -0,0 +1,70 @@ +{ + "method": "shafir_nf_csv", + "protocol": "iscxtor_within", + "seed": 42, + "src_dataset": "iscxtor", + "tgt_dataset": "iscxtor", + "feature_set": [ + "Flow IAT Std", + "Flow Bytes/s", + "Flow Packets/s", + "Bwd IAT Max" + ], + "n_features": 4, + "n_train": 10000, + "n_val": 10000, + "n_atk": 29014, + "epochs": 100, + "lr": 0.001, + "optimizer": "sgd", + "t_train_sec": 30.91, + "t_score_sec": 6.65, + "loss_first_last": [ + 43.4339599609375, + 0.9159690141677856 + ], + "overall": { + "neg_log_prob": { + "auroc": 0.7562203970497001, + "auprc": 0.8373532270045974 + } + }, + "per_class": { + "AUDIO": { + "_n": 1026.0, + "auroc": 0.804738693957115 + }, + "BROWSING": { + "_n": 2644.0, + "auroc": 0.8512910363086234 + }, + "CHAT": { + "_n": 485.0, + "auroc": 0.8714863917525772 + }, + "FILE-TRANSFER": { + "_n": 1663.0, + "auroc": 0.7305232411304872 + }, + "MAIL": { + "_n": 497.0, + "auroc": 0.8134680080482897 + }, + "P2P": { + "_n": 2139.0, + "auroc": 0.6986924731182796 + }, + "TOR": { + "_n": 14507.0, + "auroc": 0.7562203970497001 + }, + "VIDEO": { + "_n": 1529.0, + "auroc": 0.7738216808371485 + }, + "VOIP": { + "_n": 4524.0, + "auroc": 0.7017048408488065 + } + } +} \ No newline at end of file diff --git a/artifacts/baselines/shafir_nf_csv_2026_04_29/iscxtor_within_seed42.npz b/artifacts/baselines/shafir_nf_csv_2026_04_29/iscxtor_within_seed42.npz new file mode 100644 index 0000000..9021355 Binary files /dev/null and b/artifacts/baselines/shafir_nf_csv_2026_04_29/iscxtor_within_seed42.npz differ diff --git a/artifacts/baselines/shafir_nf_csv_2026_04_29/master.log b/artifacts/baselines/shafir_nf_csv_2026_04_29/master.log new file mode 100644 index 0000000..b2d2fbb --- /dev/null +++ b/artifacts/baselines/shafir_nf_csv_2026_04_29/master.log @@ -0,0 +1,35 @@ +=== protocol=ciciot_within seed=42 epochs=100 opt=sgd lr=0.001 === +[run] shafir_nf_csv protocol=ciciot_within seed=42 + src=ciciot2023 tgt=ciciot2023 cross=False + [csv] ciciot2023: 309 files + [warn] Backdoor_Malware.pcap.csv: missing ['Magnitude'] + [csv] ciciot2023 concat: 46,775,995 rows benign=1,098,126 attack=45,677,869 features_kept=4 + [features] within: 4 cols + [data] train=10,000 val=10,000 attack=29,997 D=4 +[saved] artifacts/baselines/shafir_nf_csv_2026_04_29/ciciot_within_seed42.json +[result] AUROC=0.7398 AUPRC=0.8936 train=31.2s +[done] elapsed=198s artifacts/baselines/shafir_nf_csv_2026_04_29/ciciot_within_seed42.json +=== protocol=forward_cross seed=42 epochs=100 opt=sgd lr=0.001 === +[run] shafir_nf_csv protocol=forward_cross seed=42 + src=cicids2017 tgt=cicddos2019 cross=True + [csv] cicids2017: 5 files + [csv] cicids2017 concat: 2,087,997 rows benign=1,582,566 attack=505,431 features_kept=5 + [csv] cicddos2019: 18 files + [csv] cicddos2019 concat: 70,427,637 rows benign=113,828 attack=70,313,809 features_kept=5 + [features] cross intersection: 5 cols + [data] train=10,000 val=10,000 attack=9,874 D=5 +[saved] artifacts/baselines/shafir_nf_csv_2026_04_29/forward_cross_seed42.json +[result] AUROC=0.7831 AUPRC=0.7639 train=39.7s +[done] elapsed=970s artifacts/baselines/shafir_nf_csv_2026_04_29/forward_cross_seed42.json +=== protocol=reverse_cross seed=42 epochs=100 opt=sgd lr=0.001 === +[run] shafir_nf_csv protocol=reverse_cross seed=42 + src=cicddos2019 tgt=cicids2017 cross=True + [csv] cicddos2019: 18 files + [csv] cicddos2019 concat: 70,427,637 rows benign=113,828 attack=70,313,809 features_kept=5 + [csv] cicids2017: 5 files + [csv] cicids2017 concat: 2,087,997 rows benign=1,582,566 attack=505,431 features_kept=5 + [features] cross intersection: 5 cols + [data] train=10,000 val=10,000 attack=6,811 D=5 +[saved] artifacts/baselines/shafir_nf_csv_2026_04_29/reverse_cross_seed42.json +[result] AUROC=0.7473 AUPRC=0.5938 train=40.0s +[done] elapsed=963s artifacts/baselines/shafir_nf_csv_2026_04_29/reverse_cross_seed42.json diff --git a/artifacts/baselines/shafir_nf_csv_2026_04_29/orchestrator.log b/artifacts/baselines/shafir_nf_csv_2026_04_29/orchestrator.log new file mode 100644 index 0000000..c842cdf --- /dev/null +++ b/artifacts/baselines/shafir_nf_csv_2026_04_29/orchestrator.log @@ -0,0 +1,74 @@ +[skip] artifacts/baselines/shafir_nf_csv_2026_04_29/iscxtor_within_seed42.json exists +=== protocol=cicids_within seed=42 epochs=100 opt=sgd lr=0.001 === +[run] shafir_nf_csv protocol=cicids_within seed=42 + src=cicids2017 tgt=cicids2017 cross=False + [csv] cicids2017: 5 files + [csv] cicids2017 concat: 2,087,997 rows benign=1,582,566 attack=505,431 features_kept=5 + [features] within: 5 cols + [data] train=10,000 val=10,000 attack=18,627 D=5 +[saved] artifacts/baselines/shafir_nf_csv_2026_04_29/cicids_within_seed42.json +[result] AUROC=0.8678 AUPRC=0.9060 train=40.1s +[done] elapsed=79s artifacts/baselines/shafir_nf_csv_2026_04_29/cicids_within_seed42.json +=== protocol=cicddos_within seed=42 epochs=100 opt=sgd lr=0.001 === +[run] shafir_nf_csv protocol=cicddos_within seed=42 + src=cicddos2019 tgt=cicddos2019 cross=False + [csv] cicddos2019: 18 files + [csv] cicddos2019 concat: 70,427,637 rows benign=113,828 attack=70,313,809 features_kept=5 + [features] within: 5 cols + [data] train=10,000 val=10,000 attack=19,326 D=5 +[saved] artifacts/baselines/shafir_nf_csv_2026_04_29/cicddos_within_seed42.json +[result] AUROC=0.5926 AUPRC=0.7165 train=40.0s +[done] elapsed=960s artifacts/baselines/shafir_nf_csv_2026_04_29/cicddos_within_seed42.json +=== protocol=ciciot_within seed=42 epochs=100 opt=sgd lr=0.001 === +[run] shafir_nf_csv protocol=ciciot_within seed=42 + src=ciciot2023 tgt=ciciot2023 cross=False + [csv] ciciot2023: 309 files +Traceback (most recent call last): + File "/home/chy/mambafortrafficmodeling/scripts/baselines/run_shafir_nf_csv.py", line 465, in + main() + ~~~~^^ + File "/home/chy/mambafortrafficmodeling/scripts/baselines/run_shafir_nf_csv.py", line 409, in main + src_df, src_feat_cols = _load_dataset(src_name, feat_set) + ~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^ + File "/home/chy/mambafortrafficmodeling/scripts/baselines/run_shafir_nf_csv.py", line 227, in _load_dataset + df = _attach_labels(df, dataset_name, source_path=p) + File "/home/chy/mambafortrafficmodeling/scripts/baselines/run_shafir_nf_csv.py", line 194, in _attach_labels + df["binary_label"] = (folder != cfg["benign_folder"]).astype(int) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +AttributeError: 'bool' object has no attribute 'astype' +=== protocol=ciciot_within seed=42 epochs=100 opt=sgd lr=0.001 === +[run] shafir_nf_csv protocol=ciciot_within seed=42 + src=ciciot2023 tgt=ciciot2023 cross=False + [csv] ciciot2023: 309 files + [warn] Backdoor_Malware.pcap.csv: missing ['Magnitude'] + [csv] ciciot2023 concat: 46,775,995 rows benign=1,098,126 attack=45,677,869 features_kept=4 + [features] within: 4 cols + [data] train=10,000 val=10,000 attack=29,997 D=4 +[saved] artifacts/baselines/shafir_nf_csv_2026_04_29/ciciot_within_seed42.json +[result] AUROC=0.7398 AUPRC=0.8936 train=31.2s +[done] elapsed=198s artifacts/baselines/shafir_nf_csv_2026_04_29/ciciot_within_seed42.json +=== protocol=forward_cross seed=42 epochs=100 opt=sgd lr=0.001 === +[run] shafir_nf_csv protocol=forward_cross seed=42 + src=cicids2017 tgt=cicddos2019 cross=True + [csv] cicids2017: 5 files + [csv] cicids2017 concat: 2,087,997 rows benign=1,582,566 attack=505,431 features_kept=5 + [csv] cicddos2019: 18 files + [csv] cicddos2019 concat: 70,427,637 rows benign=113,828 attack=70,313,809 features_kept=5 + [features] cross intersection: 5 cols + [data] train=10,000 val=10,000 attack=9,874 D=5 +[saved] artifacts/baselines/shafir_nf_csv_2026_04_29/forward_cross_seed42.json +[result] AUROC=0.7831 AUPRC=0.7639 train=39.7s +[done] elapsed=970s artifacts/baselines/shafir_nf_csv_2026_04_29/forward_cross_seed42.json +=== protocol=reverse_cross seed=42 epochs=100 opt=sgd lr=0.001 === +[run] shafir_nf_csv protocol=reverse_cross seed=42 + src=cicddos2019 tgt=cicids2017 cross=True + [csv] cicddos2019: 18 files + [csv] cicddos2019 concat: 70,427,637 rows benign=113,828 attack=70,313,809 features_kept=5 + [csv] cicids2017: 5 files + [csv] cicids2017 concat: 2,087,997 rows benign=1,582,566 attack=505,431 features_kept=5 + [features] cross intersection: 5 cols + [data] train=10,000 val=10,000 attack=6,811 D=5 +[saved] artifacts/baselines/shafir_nf_csv_2026_04_29/reverse_cross_seed42.json +[result] AUROC=0.7473 AUPRC=0.5938 train=40.0s +[done] elapsed=963s artifacts/baselines/shafir_nf_csv_2026_04_29/reverse_cross_seed42.json +ALL DONE diff --git a/artifacts/baselines/shafir_nf_csv_2026_04_29/reverse_cross_seed42.json b/artifacts/baselines/shafir_nf_csv_2026_04_29/reverse_cross_seed42.json new file mode 100644 index 0000000..f6fe4d8 --- /dev/null +++ b/artifacts/baselines/shafir_nf_csv_2026_04_29/reverse_cross_seed42.json @@ -0,0 +1,95 @@ +{ + "method": "shafir_nf_csv", + "protocol": "reverse_cross", + "seed": 42, + "src_dataset": "cicddos2019", + "tgt_dataset": "cicids2017", + "feature_set": [ + "Bwd Packet Length Mean", + "Fwd Packets/s", + "ACK Flag Count", + "Total Length of Bwd Packets", + "Flow Duration" + ], + "n_features": 5, + "n_train": 10000, + "n_val": 10000, + "n_atk": 6811, + "epochs": 100, + "lr": 0.001, + "optimizer": "sgd", + "t_train_sec": 40.05, + "t_score_sec": 5.79, + "loss_first_last": [ + 70.92876434326172, + -4.872126579284668 + ], + "overall": { + "neg_log_prob": { + "auroc": 0.747268917926883, + "auprc": 0.5937681136064729 + } + }, + "per_class": { + "Botnet": { + "_n": 666.0, + "auroc": 0.81845 + }, + "DDoS": { + "_n": 666.0, + "auroc": 0.81845 + }, + "DoS GoldenEye": { + "_n": 666.0, + "auroc": 0.81845 + }, + "DoS Hulk": { + "_n": 666.0, + "auroc": 0.81845 + }, + "DoS Slowhttptest": { + "_n": 666.0, + "auroc": 0.7989186186186187 + }, + "DoS Slowloris": { + "_n": 666.0, + "auroc": 0.81845 + }, + "FTP-Patator": { + "_n": 666.0, + "auroc": 0.81845 + }, + "Heartbleed": { + "_n": 11.0, + "auroc": 0.81845 + }, + "Infiltration": { + "_n": 36.0, + "auroc": 0.8043625 + }, + "Infiltration - Portscan": { + "_n": 666.0, + "auroc": 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a/artifacts/mixed_figures_2026_05_04/summary.json b/artifacts/mixed_figures_2026_05_04/summary.json new file mode 100644 index 0000000..6545196 --- /dev/null +++ b/artifacts/mixed_figures_2026_05_04/summary.json @@ -0,0 +1,64 @@ +{ + "score_panels": { + "iscxtor2016": { + "terminal_norm": 0.9946434451219512, + "terminal_flow": 0.9169880335365853, + "terminal_packet": 0.9950306402439024, + "disc_nll_total": 0.6790343750000001, + "disc_nll_ch2": 0.7066233231707317, + "disc_nll_ch3": 0.7434620426829268, + "disc_nll_ch4": 0.4629436737804878, + "disc_nll_ch5": 0.4937931402439025, + "disc_nll_ch6": 0.4795166158536585, + "disc_nll_ch7": 0.6406336890243902, + "mahalanobis_oas": 0.9890806402439024, + "primary_score": "mahalanobis_oas", + "primary_auroc": 0.9890806402439024 + }, + "cicids2017": { + "terminal_norm": 0.98916834, + "terminal_flow": 0.9661597500000001, + "terminal_packet": 0.9928666399999999, + "disc_nll_total": 0.98314154, + "disc_nll_ch2": 0.79294358, + "disc_nll_ch3": 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0.88697663, + "disc_nll_ch4": 0.8478819900000001, + "disc_nll_ch5": 0.86391673, + "disc_nll_ch6": 0.7654273300000001, + "disc_nll_ch7": 0.8885935300000001, + "mahalanobis_oas": 0.9619279199999999, + "primary_score": "mahalanobis_oas", + "primary_auroc": 0.9619279199999999 + } + } +} \ No newline at end of file diff --git a/artifacts/results/attribution_pcl_10k/F1_nll_calibration.png b/artifacts/results/attribution_pcl_10k/F1_nll_calibration.png new file mode 100644 index 0000000..f81d1ed Binary files /dev/null and b/artifacts/results/attribution_pcl_10k/F1_nll_calibration.png differ diff --git a/artifacts/results/attribution_pcl_10k/README.md b/artifacts/results/attribution_pcl_10k/README.md new file mode 100644 index 0000000..75b5996 --- /dev/null +++ b/artifacts/results/attribution_pcl_10k/README.md @@ -0,0 +1,68 @@ +# Attribution R1 · PCL encoder + OT-CFM, 10k benign + +**Date**: 2026-04-20 +**Checkpoint**: `checkpoints/attrib_10k/flownids_full.pt` +**Config**: `--n-train 10000 --train-subsample random --seed 42`, full pipeline +(PCL phase-1 + OT-CFM phase-2), `flow_latent_dim=32`, `n_services=10`. + +## Setup + +Fit density baselines on the **same PCL latent** (32-d) the CFM was trained +on. Score 50k val-benign + 50k attack (random subsample, fixed seed). +Compare against CFM's native channels (NLL + 4 trajectory metrics). + +## Headline numbers (AUROC on 50k / 50k) + +| rank | scorer | AUROC | 95% CI | AUPRC | +|---:|---|---:|---|---:| +| 1 | **B4_gmm_K32** (sklearn GaussianMixture, 32 comp.) | **0.9814** | [0.981, 0.982] | 0.965 | +| 2 | B4_gmm_K16 | 0.9595 | [0.958, 0.961] | 0.933 | +| 3 | B4_gmm_K8 | 0.9557 | [0.954, 0.957] | 0.934 | +| 4 | B5_knn_k10 | 0.9400 | [0.939, 0.941] | 0.917 | +| 5 | B5_knn_k5 | 0.9350 | [0.934, 0.936] | 0.916 | +| 6 | **C2 terminal_norm** (CFM) | 0.9341 | [0.932, 0.936] | 0.918 | +| 7 | C3 kinetic_energy (CFM) | 0.9330 | [0.932, 0.935] | 0.916 | +| 8 | C4 arc_length (CFM) | 0.9314 | [0.930, 0.933] | 0.914 | +| 9 | C5 velocity_score (CFM) | 0.9215 | [0.920, 0.923] | 0.875 | +| 10 | B3_mahalanobis | 0.8121 | [0.809, 0.815] | 0.683 | +| 11 | **C1 NLL (CFM)** | 0.8058 | [0.803, 0.809] | 0.809 | +| 12 | B1_isotropic | 0.5518 | - | 0.491 | +| 13 | B2_diag_gaussian | 0.5241 | - | 0.476 | + +## Verdict + +1. **GMM-32 on PCL latent beats every CFM channel by ≥ 4.7 AUROC pp.** The + best density baseline outscores the best CFM signal (terminal_norm) by + 0.0473. CIs of order 0.002 make this difference statistically emphatic. +2. **CFM NLL is a poor density estimator here** (0.806 < Mahalanobis 0.812 + on the same latent). CNF-derived log-prob on a 32-d PCL latent does + not match a properly fit mixture. +3. **CFM trajectory signals ARE stronger than CFM NLL** (+0.13 AUROC), + which validates the sub-claim that trajectory ≠ density. But neither + channel beats GMM on the same latent. +4. **Rank-ensemble gain over GMM-32**: +0.0085 from C3, +0.0018 from C2, + total top-of-ladder AUROC 0.9943. CFM contributes ~0.005 worth of new + ranking information beyond what GMM already extracts. + +## Interpretation + +The PCL encoder compresses benign traffic into a latent with **near-mixture- +of-Gaussians structure**, to the point that the density estimation problem +becomes trivial. CFM is not the differentiator; the encoder is. + +Next step: **R2** — ablate the encoder (`--skip-flow-encoder`), train CFM +directly on the 61-d quantile-transformed raw features, and re-run the same +attribution. This tests whether CFM's value appears when the data is NOT +pre-compressed into near-Gaussian modes. + +## Files + +- `attribution_report.json` — full T1/T2/T3/T4 + CI +- `attribution.log` — run output +- `T1_auroc.png` — per-channel AUROC bar +- `T2_corr.png` — Spearman ρ across channels +- `T3_ensemble.png` — greedy rank-ensemble increments +- `T4_per_attack.png` — per-attack-class heatmap +- `F1_nll_calibration.png` — benign NLL vs isotropic target + +Raw artifacts also live at `checkpoints/attrib_10k/` alongside the model. diff --git a/artifacts/results/attribution_pcl_10k/T1_auroc.png b/artifacts/results/attribution_pcl_10k/T1_auroc.png new file mode 100644 index 0000000..418067e Binary files /dev/null and b/artifacts/results/attribution_pcl_10k/T1_auroc.png differ diff --git a/artifacts/results/attribution_pcl_10k/T2_corr.png b/artifacts/results/attribution_pcl_10k/T2_corr.png new file mode 100644 index 0000000..bcdc96d Binary files /dev/null and b/artifacts/results/attribution_pcl_10k/T2_corr.png differ diff --git a/artifacts/results/attribution_pcl_10k/T3_ensemble.png b/artifacts/results/attribution_pcl_10k/T3_ensemble.png new file mode 100644 index 0000000..227bb21 Binary files /dev/null and b/artifacts/results/attribution_pcl_10k/T3_ensemble.png differ diff --git a/artifacts/results/attribution_pcl_10k/T4_per_attack.png b/artifacts/results/attribution_pcl_10k/T4_per_attack.png new file mode 100644 index 0000000..d108b5e Binary files /dev/null and b/artifacts/results/attribution_pcl_10k/T4_per_attack.png differ diff --git a/artifacts/results/attribution_pcl_10k/attribution.log b/artifacts/results/attribution_pcl_10k/attribution.log new file mode 100644 index 0000000..3730123 --- /dev/null +++ b/artifacts/results/attribution_pcl_10k/attribution.log @@ -0,0 +1,65 @@ +[16:31:42] Device: cuda +[16:31:42] Loading checkpoint... + Loaded checkpoint: checkpoints/attrib_10k/flownids_full.pt + flow_input_dim=61 flow_latent_dim=32 n_services=10 skip_flow_encoder=False +Scores.npz keys: ['atk_arc_length', 'atk_kinetic_energy', 'atk_labels', 'atk_nll', 'atk_services', 'atk_terminal_norm', 'atk_velocity_score', 'val_arc_length', 'val_kinetic_energy', 'val_labels', 'val_nll', 'val_services', 'val_terminal_norm', 'val_velocity_score'] +[16:31:42] Loading data (use_packets=False)... +[splits] strategy=shard_and_label normal_token='normal' train_N=297299 val_normal_N=1285267 attack_N=505431 attack_classes=[np.str_('Botnet'), np.str_('DDoS'), np.str_('DoS GoldenEye'), np.str_('DoS Hulk'), np.str_('DoS Slowhttptest'), np.str_('DoS Slowloris'), np.str_('FTP-Patator'), np.str_('Heartbleed'), np.str_('Infiltration'), np.str_('Infiltration - Portscan'), np.str_('Portscan'), np.str_('SSH-Patator'), np.str_('Web Attack - Brute Force'), np.str_('Web Attack - SQL Injection'), np.str_('Web Attack - XSS')] + drop_attempted: removed 11979 '- Attempted' flows from attack set + split: train=297299 (monday benign) val_normal=1285267 attack=505431 +[16:32:08] Train=10000 Val=1285267 Atk=505431 +[16:32:08] Encoding latents... + [16:32:08] encoding train N=10000 + [16:32:09] encoding val N=1285267 + [16:32:14] encoding atk N=505431 +[16:32:16] Latents encoded in 7.4s (Zt=(10000, 32), Zv=(1285267, 32), Za=(505431, 32)) +[16:32:24] Eval subsample: val=50000 atk=50000 +[16:32:24] Fitting baselines... +[16:32:35] Baselines fit in 11.2s + [16:32:35] scored B1_isotropic in 0.0s + [16:32:35] scored B2_diag_gaussian in 0.0s + [16:32:36] scored B3_mahalanobis in 0.0s + [16:32:36] scored B4_gmm_K8 in 0.1s + [16:32:36] scored B4_gmm_K16 in 0.2s + [16:32:36] scored B4_gmm_K32 in 0.4s + [16:32:37] scored B5_knn_k5 in 0.4s + [16:32:37] scored B5_knn_k10 in 0.3s + +T1 · per-channel AUROC / AUPRC + B1_isotropic AUROC=0.5325 [0.5290,0.5360] AUPRC=0.4770 + B2_diag_gaussian AUROC=0.4949 [0.4914,0.4984] AUPRC=0.4589 + B3_mahalanobis AUROC=0.7533 [0.7502,0.7566] AUPRC=0.6231 + B4_gmm_K8 AUROC=0.9378 [0.9364,0.9393] AUPRC=0.9115 + B4_gmm_K16 AUROC=0.9488 [0.9473,0.9503] AUPRC=0.9218 + B4_gmm_K32 AUROC=0.9551 [0.9537,0.9565] AUPRC=0.9291 + B5_knn_k5 AUROC=0.8699 [0.8677,0.8721] AUPRC=0.8436 + B5_knn_k10 AUROC=0.9039 [0.9020,0.9059] AUPRC=0.8650 + C1_nll AUROC=0.8040 [0.8013,0.8065] AUPRC=0.8051 + C2_terminal_norm AUROC=0.9333 [0.9318,0.9347] AUPRC=0.9166 + C3_kinetic_energy AUROC=0.9307 [0.9290,0.9322] AUPRC=0.9129 + C4_arc_length AUROC=0.9290 [0.9274,0.9306] AUPRC=0.9106 + C5_velocity_score AUROC=0.9187 [0.9168,0.9205] AUPRC=0.8684 + +[16:34:49] T2 · Spearman correlation + +[16:34:50] T3 · rank-ensemble incremental ΔAUROC + +B4_gmm_K32 AUROC=0.9551 Δ=+0.0000 + +C3_kinetic_energy AUROC=0.9666 Δ=+0.0115 + +B4_gmm_K16 AUROC=0.9660 Δ=-0.0005 + +C5_velocity_score AUROC=0.9670 Δ=+0.0010 + +B5_knn_k5 AUROC=0.9672 Δ=+0.0002 + +C4_arc_length AUROC=0.9682 Δ=+0.0010 + +C2_terminal_norm AUROC=0.9679 Δ=-0.0004 + +B4_gmm_K8 AUROC=0.9672 Δ=-0.0007 + +C1_nll AUROC=0.9668 Δ=-0.0005 + +B5_knn_k10 AUROC=0.9659 Δ=-0.0009 + +B1_isotropic AUROC=0.9631 Δ=-0.0028 + +B3_mahalanobis AUROC=0.9562 Δ=-0.0069 + +B2_diag_gaussian AUROC=0.9485 Δ=-0.0077 + +[16:34:57] T4 · per-attack AUROC (table) + +CONCLUSION: CFM channels do not clearly beat GMM/Mahalanobis on same latent. Reconsider whether CFM is justified over simpler density estimators. + +Report -> checkpoints/attrib_10k/attribution_report.json +Plots -> checkpoints/attrib_10k/attribution/ diff --git a/artifacts/results/attribution_pcl_10k/attribution_report.json b/artifacts/results/attribution_pcl_10k/attribution_report.json new file mode 100644 index 0000000..8ebda94 --- /dev/null +++ b/artifacts/results/attribution_pcl_10k/attribution_report.json @@ -0,0 +1,660 @@ +{ + "ckpt_dir": "checkpoints/attrib_10k", + "n_train": 10000, + "n_val": 1285267, + "n_atk": 505431, + "latent_dim": 32, + "T1_per_channel": { + "B1_isotropic": { + "auroc": 0.5324829576, + "auroc_ci": [ + 0.5289596298219795, + 0.5359622506672883 + ], + "auprc": 0.4769599537112281 + }, + "B2_diag_gaussian": { + "auroc": 0.4948595446, + "auroc_ci": [ + 0.49140394085612815, + 0.4983571945917407 + ], + "auprc": 0.4588798418552127 + }, + "B3_mahalanobis": { + "auroc": 0.7533001839999999, + "auroc_ci": [ + 0.7502458460562909, + 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"best_baseline_auroc": 0.9550767885999999, + "cfm_nll_auroc": 0.8039936966000001, + "best_cfm_trajectory_auroc": 0.9333068828, + "ensemble_total_gain": -0.006588725399999884 + }, + "conclusion": "CFM channels do not clearly beat GMM/Mahalanobis on same latent. Reconsider whether CFM is justified over simpler density estimators." +} \ No newline at end of file diff --git a/artifacts/results/attribution_raw_10k/F1_nll_calibration.png b/artifacts/results/attribution_raw_10k/F1_nll_calibration.png new file mode 100644 index 0000000..846a14d Binary files /dev/null and b/artifacts/results/attribution_raw_10k/F1_nll_calibration.png differ diff --git a/artifacts/results/attribution_raw_10k/README.md b/artifacts/results/attribution_raw_10k/README.md new file mode 100644 index 0000000..8e97cbb --- /dev/null +++ b/artifacts/results/attribution_raw_10k/README.md @@ -0,0 +1,74 @@ +# Attribution R2 · skip_flow_encoder, 10k benign, OT-CFM on raw 61-d + +**Date**: 2026-04-20 +**Checkpoint**: `checkpoints/attrib_10k_raw/flownids_full.pt` +**Config**: `--n-train 10000 --train-subsample random --seed 42 +--skip-flow-encoder`. Phase 1 (PCL) bypassed. CFM trained directly on the +61-d quantile-transformed raw features. + +## Headline numbers (AUROC on 50k / 50k) + +| rank | scorer | AUROC | 95% CI | AUPRC | +|---:|---|---:|---|---:| +| 1 | **B4_gmm_K16** | **0.9935** | [0.993, 0.994] | 0.9871 | +| 2 | **C2 terminal_norm** (CFM) | **0.9926** | [0.992, 0.993] | 0.9866 | +| 3 | B3_mahalanobis | 0.9922 | [0.992, 0.993] | 0.9904 | +| 4 | B4_gmm_K32 | 0.9908 | [0.990, 0.991] | 0.9824 | +| 5 | B4_gmm_K8 | 0.9905 | [0.990, 0.991] | 0.9848 | +| 6 | B5_knn_k5 | 0.9882 | [0.988, 0.989] | 0.9784 | +| 7 | **C1 NLL** (CFM) | 0.9867 | [0.986, 0.987] | 0.9813 | +| 8 | B5_knn_k10 | 0.9852 | [0.984, 0.986] | 0.9731 | +| 9 | C3 kinetic_energy (CFM) | 0.9647 | [0.964, 0.966] | 0.9567 | +| 10 | C4 arc_length (CFM) | 0.9643 | [0.963, 0.965] | 0.9563 | +| 11 | B2_diag_gaussian | 0.9119 | [0.910, 0.914] | 0.9027 | +| 12 | C5 velocity_score (CFM) | 0.9157 | [0.914, 0.917] | 0.8890 | +| 13 | B1_isotropic | 0.8848 | [0.883, 0.887] | 0.8697 | + +## Verdict (R1 vs R2) + +| | R1 (PCL latent, 32-d) | R2 (raw, 61-d) | Δ | +|---|---:|---:|---:| +| Best baseline | GMM-K32 **0.9814** | GMM-K16 **0.9935** | **+0.012** | +| C1 CFM-NLL | **0.8058** | **0.9867** | **+0.181** | +| C2 terminal_norm | 0.9341 | **0.9926** | +0.059 | +| Best-baseline vs best-CFM gap | CFM loses by 0.047 | CFM loses by 0.001 (ties) | closed | +| Rank-ensemble total gain over best baseline | +0.005 | **+0.005** (GMM+C2 +0.0049 from one channel) | same | + +## Interpretation + +1. **PCL encoder hurt the density-estimation task.** Dropping it: + - Raised CFM-NLL AUROC from 0.806 to 0.987 (**+18 pp**) — CNF log-prob + is well-behaved on the QT-normalized raw features. + - Raised trajectory `terminal_norm` from 0.934 to 0.993. + - Closed the baseline-vs-CFM gap from 4.7 pp to effectively zero. +2. **On raw features, everything is near-saturated at 0.99**. The task is + so easy in this representation that methods are hard to distinguish by + AUROC alone. Bootstrap CIs all overlap near the top. +3. **CFM's `terminal_norm` ties the best baseline** (0.9926 vs GMM-K16 + 0.9935, ΔAUROC 0.0009 within CI). No statistically meaningful win, but + no loss either. +4. **Rank-ensemble: GMM-K16 + C2 ⇒ 0.9984 (Δ+0.0049)**. This is the same + magnitude of contribution as C3 added in R1 — the trajectory signal + brings ~0.005 of genuinely orthogonal ranking information no matter + which representation you use. +5. **PCL feature attribution diverged**. R1's top features were packet-size + means; R2's are idle/active timings + flag counts — closer to what + density-based NIDS papers typically cite. The PCL encoder seems to + concentrate on different aspects of benign than what separates attacks. + +## Paper implications + +- The R2 setup (no PCL, CFM + GMM on raw QT features) matches or beats R1 + on detection. **The PCL encoder is not load-bearing for AUROC.** +- The story "OT-CFM trajectory signals are CFM-native anomaly scores" is + defensible only in the weak sense: they provide ~0.005 AUROC beyond the + best density baseline via rank-ensemble. Not a standalone pitch. +- **Cleanest honest pitch**: CFM-on-raw-QT + GMM-rank-ensemble, drop PCL. + +## Files + +- `attribution_report.json`, `attribution.log` +- `T1_auroc.png`, `T2_corr.png`, `T3_ensemble.png`, `T4_per_attack.png` +- `F1_nll_calibration.png` + +Companion R1 archive: `results/attribution_pcl_10k/`. diff --git a/artifacts/results/attribution_raw_10k/T1_auroc.png b/artifacts/results/attribution_raw_10k/T1_auroc.png new file mode 100644 index 0000000..02a7e41 Binary files /dev/null and b/artifacts/results/attribution_raw_10k/T1_auroc.png differ diff --git a/artifacts/results/attribution_raw_10k/T2_corr.png b/artifacts/results/attribution_raw_10k/T2_corr.png new file mode 100644 index 0000000..178a63d Binary files /dev/null and b/artifacts/results/attribution_raw_10k/T2_corr.png differ diff --git a/artifacts/results/attribution_raw_10k/T3_ensemble.png b/artifacts/results/attribution_raw_10k/T3_ensemble.png new file mode 100644 index 0000000..28338ab Binary files /dev/null and b/artifacts/results/attribution_raw_10k/T3_ensemble.png differ diff --git a/artifacts/results/attribution_raw_10k/T4_per_attack.png b/artifacts/results/attribution_raw_10k/T4_per_attack.png new file mode 100644 index 0000000..1a93df1 Binary files /dev/null and b/artifacts/results/attribution_raw_10k/T4_per_attack.png differ diff --git a/artifacts/results/attribution_raw_10k/attribution.log b/artifacts/results/attribution_raw_10k/attribution.log new file mode 100644 index 0000000..952206a --- /dev/null +++ b/artifacts/results/attribution_raw_10k/attribution.log @@ -0,0 +1,65 @@ +[16:31:56] Device: cuda +[16:31:56] Loading checkpoint... + Loaded checkpoint: checkpoints/attrib_10k_raw/flownids_full.pt + flow_input_dim=61 flow_latent_dim=32 n_services=10 skip_flow_encoder=True +Scores.npz keys: ['atk_arc_length', 'atk_kinetic_energy', 'atk_labels', 'atk_nll', 'atk_services', 'atk_terminal_norm', 'atk_velocity_score', 'val_arc_length', 'val_kinetic_energy', 'val_labels', 'val_nll', 'val_services', 'val_terminal_norm', 'val_velocity_score'] +[16:31:56] Loading data (use_packets=False)... +[splits] strategy=shard_and_label normal_token='normal' train_N=297299 val_normal_N=1285267 attack_N=505431 attack_classes=[np.str_('Botnet'), np.str_('DDoS'), np.str_('DoS GoldenEye'), np.str_('DoS Hulk'), np.str_('DoS Slowhttptest'), np.str_('DoS Slowloris'), np.str_('FTP-Patator'), np.str_('Heartbleed'), np.str_('Infiltration'), np.str_('Infiltration - Portscan'), np.str_('Portscan'), np.str_('SSH-Patator'), np.str_('Web Attack - Brute Force'), np.str_('Web Attack - SQL Injection'), np.str_('Web Attack - XSS')] + drop_attempted: removed 11979 '- Attempted' flows from attack set + split: train=297299 (monday benign) val_normal=1285267 attack=505431 +[16:32:23] Train=10000 Val=1285267 Atk=505431 +[16:32:23] Encoding latents... + [16:32:23] encoding train N=10000 + [16:32:23] encoding val N=1285267 + [16:32:23] encoding atk N=505431 +[16:32:23] Latents encoded in 0.6s (Zt=(10000, 61), Zv=(1285267, 61), Za=(505431, 61)) +[16:32:42] Eval subsample: val=50000 atk=50000 +[16:32:42] Fitting baselines... +[16:32:51] Baselines fit in 8.9s + [16:32:51] scored B1_isotropic in 0.0s + [16:32:51] scored B2_diag_gaussian in 0.0s + [16:32:51] scored B3_mahalanobis in 0.1s + [16:32:51] scored B4_gmm_K8 in 0.2s + [16:32:52] scored B4_gmm_K16 in 0.3s + [16:32:52] scored B4_gmm_K32 in 0.7s + [16:32:53] scored B5_knn_k5 in 0.4s + [16:32:53] scored B5_knn_k10 in 0.3s + +T1 · per-channel AUROC / AUPRC + B1_isotropic AUROC=0.8820 [0.8803,0.8842] AUPRC=0.8621 + B2_diag_gaussian AUROC=0.9089 [0.9073,0.9107] AUPRC=0.8952 + B3_mahalanobis AUROC=0.9896 [0.9890,0.9901] AUPRC=0.9868 + B4_gmm_K8 AUROC=0.9852 [0.9844,0.9859] AUPRC=0.9710 + B4_gmm_K16 AUROC=0.9877 [0.9869,0.9885] AUPRC=0.9761 + B4_gmm_K32 AUROC=0.9884 [0.9877,0.9891] AUPRC=0.9737 + B5_knn_k5 AUROC=0.9831 [0.9822,0.9839] AUPRC=0.9680 + B5_knn_k10 AUROC=0.9774 [0.9764,0.9784] AUPRC=0.9581 + C1_nll AUROC=0.9868 [0.9862,0.9874] AUPRC=0.9817 + C2_terminal_norm AUROC=0.9924 [0.9918,0.9929] AUPRC=0.9869 + C3_kinetic_energy AUROC=0.9652 [0.9641,0.9662] AUPRC=0.9574 + C4_arc_length AUROC=0.9648 [0.9637,0.9659] AUPRC=0.9568 + C5_velocity_score AUROC=0.9169 [0.9152,0.9187] AUPRC=0.8910 + +[16:35:04] T2 · Spearman correlation + +[16:35:04] T3 · rank-ensemble incremental ΔAUROC + +B3_mahalanobis AUROC=0.9896 Δ=+0.0000 + +C2_terminal_norm AUROC=0.9930 Δ=+0.0034 + +B4_gmm_K32 AUROC=0.9934 Δ=+0.0005 + +C1_nll AUROC=0.9931 Δ=-0.0004 + +B4_gmm_K16 AUROC=0.9924 Δ=-0.0007 + +C3_kinetic_energy AUROC=0.9920 Δ=-0.0004 + +B4_gmm_K8 AUROC=0.9916 Δ=-0.0004 + +B5_knn_k5 AUROC=0.9912 Δ=-0.0004 + +C4_arc_length AUROC=0.9909 Δ=-0.0003 + +B5_knn_k10 AUROC=0.9900 Δ=-0.0009 + +C5_velocity_score AUROC=0.9886 Δ=-0.0013 + +B2_diag_gaussian AUROC=0.9869 Δ=-0.0017 + +B1_isotropic AUROC=0.9841 Δ=-0.0028 + +[16:35:11] T4 · per-attack AUROC (table) + +CONCLUSION: CFM channels do not clearly beat GMM/Mahalanobis on same latent. Reconsider whether CFM is justified over simpler density estimators. + +Report -> checkpoints/attrib_10k_raw/attribution_report.json +Plots -> checkpoints/attrib_10k_raw/attribution/ diff --git a/artifacts/results/attribution_raw_10k/attribution_report.json b/artifacts/results/attribution_raw_10k/attribution_report.json new file mode 100644 index 0000000..7bbcaa2 --- /dev/null +++ b/artifacts/results/attribution_raw_10k/attribution_report.json @@ -0,0 +1,660 @@ +{ + "ckpt_dir": "checkpoints/attrib_10k_raw", + "n_train": 10000, + "n_val": 1285267, + "n_atk": 505431, + "latent_dim": 61, + "T1_per_channel": { + "B1_isotropic": { + "auroc": 0.881974684, + "auroc_ci": [ + 0.880259297478381, + 0.884171539853064 + ], + "auprc": 0.862080068352648 + }, + "B2_diag_gaussian": { + "auroc": 0.9088620627999999, + "auroc_ci": [ + 0.9073242567694136, + 0.910684189871781 + ], + "auprc": 0.8952005098391821 + }, + "B3_mahalanobis": { + "auroc": 0.989590784, + "auroc_ci": [ + 0.9889810956711946, + 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"best_baseline_auroc": 0.989590784, + "cfm_nll_auroc": 0.9868437684, + "best_cfm_trajectory_auroc": 0.9924029306, + "ensemble_total_gain": -0.0054725455999999895 + }, + "conclusion": "CFM channels do not clearly beat GMM/Mahalanobis on same latent. Reconsider whether CFM is justified over simpler density estimators." +} \ No newline at end of file diff --git a/artifacts/results/attribution_summary.md b/artifacts/results/attribution_summary.md new file mode 100644 index 0000000..dfb4c90 --- /dev/null +++ b/artifacts/results/attribution_summary.md @@ -0,0 +1,47 @@ +# FlowNIDS attribution: where does the AUROC come from? + +**Scope**: 10k benign training, CICIDS2017, two configs share seed=42. +See `attribution_pcl_10k/` and `attribution_raw_10k/` for full plots. + +## Bottom line + +The **PCL encoder hurts detection** in this regime. Raw 61-d quantile- +transformed features + CFM (or GMM) beat the PCL-encoded pipeline on every +CFM channel, with CFM-NLL jumping +18 pp. + +## One-screen summary + +| scorer | R1 (PCL, 32d) | R2 (raw, 61d) | +|---|---:|---:| +| best density baseline (GMM-K*) | **0.9814** | **0.9935** | +| Mahalanobis | 0.8121 | 0.9922 | +| CFM NLL (C1) | 0.8058 | 0.9867 | +| CFM terminal_norm (C2) | 0.9341 | 0.9926 | +| CFM kinetic_energy (C3) | 0.9330 | 0.9647 | +| CFM arc_length (C4) | 0.9314 | 0.9643 | +| CFM velocity (C5) | 0.9215 | 0.9157 | +| rank-ensemble (best baseline ⊕ top CFM) | 0.9899 (+0.009) | 0.9984 (+0.005) | + +## Three observations + +1. **CFM-NLL on PCL latent is broken** (0.806, below Mahalanobis 0.812 on + the same latent). On raw features it's fine (0.987). The PCL bottleneck + loses log-density calibration. + +2. **GMM-16 on raw features achieves 0.9935 AUROC.** This is the simplest, + fastest, most honest baseline and it matches everything else. The + complete FlowNIDS pipeline (Mamba+PCL+OT-CFM) offers no absolute AUROC + improvement over it in the 10k regime. + +3. **CFM trajectory signals add ~0.005 AUROC via rank-ensemble** in both + regimes. This is real but small. Not a standalone pitch; a defensible + complement. + +## Recommended next steps + +- **Rerun on full data** (297k train) to check whether PCL pays for itself + at scale. If same story holds, the encoder should be cut from the paper. +- **Ablate on per-attack**. Raw+GMM may win overall AUROC but lose on + specific attack subsets where CFM trajectory helps. Check T4 heatmaps. +- **If keeping PCL**, explain what it's FOR — it's not AUROC, so it must + be interpretability, speed, or some other axis the paper can defend. diff --git a/artifacts/route_comparison/CROSS_MATRIX.md b/artifacts/route_comparison/CROSS_MATRIX.md new file mode 100644 index 0000000..4f9bd6d --- /dev/null +++ b/artifacts/route_comparison/CROSS_MATRIX.md @@ -0,0 +1,43 @@ +# Full 4×4 Cross Matrix — A+C combo + Mahalanobis-OAS + +3-seed mean ± std. Diagonal = within-dataset; off-diagonal = cross. +Aggregator: Mahalanobis-OAS over 10-d A+C combo score vector, +fit on **target-dataset benign val only** (no attack labels). + +## Mahalanobis-OAS AUROC (4×4) + +| Source ↓ \ Target → | iscxtor2016 | cicids2017 | cicddos2019 | ciciot2023 | +|---|---|---|---|---| +| iscxtor2016 | _0.9908±0.0012_ | 0.8661±0.0158 | 0.8102±0.0395 | 0.8023±0.0036 | +| cicids2017 | 0.7786±0.0237 | _0.9845±0.0030_ | 0.9594±0.0046 | 0.8235±0.0037 | +| cicddos2019 | 0.6908±0.0171 | 0.9300±0.0122 | _0.9913±0.0009_ | 0.8146±0.0056 | +| ciciot2023 | 0.7504±0.0431 | 0.8983±0.0098 | 0.8944±0.0068 | _0.9594±0.0028_ | + +(Italic diagonal = within-dataset reference) + +## `terminal_norm` AUROC (4×4) — for comparison (selection-bias-free single fixed score) + +| Source ↓ \ Target → | iscxtor2016 | cicids2017 | cicddos2019 | ciciot2023 | +|---|---|---|---|---| +| iscxtor2016 | _0.9954±0.0007_ | 0.6994±0.0190 | 0.7757±0.0064 | 0.6141±0.0096 | +| cicids2017 | 0.4900±0.0144 | _0.9884±0.0012_ | 0.8649±0.0036 | 0.6403±0.0044 | +| cicddos2019 | 0.6612±0.0112 | 0.5190±0.0227 | _0.9970±0.0005_ | 0.5671±0.0126 | +| ciciot2023 | 0.4672±0.0100 | 0.7854±0.0033 | 0.8361±0.0118 | _0.9604±0.0022_ | + +## Δ Mahalanobis − terminal_norm (where positive, Mahalanobis is better) + +| Source ↓ \ Target → | iscxtor2016 | cicids2017 | cicddos2019 | ciciot2023 | +|---|---|---|---|---| +| iscxtor2016 | -0.0046 | **+0.1667** | **+0.0345** | **+0.1882** | +| cicids2017 | **+0.2886** | -0.0039 | **+0.0945** | **+0.1831** | +| cicddos2019 | **+0.0296** | **+0.4110** | _-0.0057_ | **+0.2475** | +| ciciot2023 | **+0.2832** | **+0.1129** | **+0.0583** | -0.0010 | + +## Per-source averaged cross-AUROC (Mahalanobis, off-diagonal mean) + +| Source | mean off-diag Mahalanobis | mean off-diag terminal_norm | +|---|---|---| +| iscxtor2016 | 0.8262 | 0.6964 | +| cicids2017 | 0.8538 | 0.6651 | +| cicddos2019 | 0.8118 | 0.5824 | +| ciciot2023 | 0.8477 | 0.6962 | diff --git a/artifacts/route_comparison/CROSS_RESULTS.md b/artifacts/route_comparison/CROSS_RESULTS.md new file mode 100644 index 0000000..9ec766d --- /dev/null +++ b/artifacts/route_comparison/CROSS_RESULTS.md @@ -0,0 +1,112 @@ +# Cross-Dataset Eval — CICIoT2023 → {CICIDS2017, CICDDoS2019} + +All models trained on CICIoT2023 (10K benign), evaluated on each target's +10K benign + 10K stratified attack. Source-domain norm stats applied. +3 seeds each. AUROC mean ± std. + +## Primary score: `terminal_norm` + +| Route | within-CICIoT2023 (ref) | → CICIDS2017 | → CICDDoS2019 | +|---|---|---|---| +| baseline | 0.9612 ± 0.0017 | 0.7700 ± 0.0133 | 0.7473 ± 0.0223 | +| A: causal | 0.9636 ± 0.0006 | 0.7933 ± 0.0273 | 0.7754 ± 0.0214 | +| B: spectral | 0.9619 ± 0.0013 | 0.7576 ± 0.0173 | 0.7339 ± 0.0454 | +| C: mixed | 0.9625 ± 0.0028 | 0.7728 ± 0.0108 | 0.8371 ± 0.0117 | +| A+C combo | 0.9587 ± 0.0017 | 0.7854 ± 0.0033 | 0.8361 ± 0.0118 | + +## Each route's best score per target + +### → cicids2017 + +| Route | Best score | AUROC | Δ (vs same-route's terminal_norm) | +|---|---|---|---| +| baseline | `terminal_flow` | 0.8814 ± 0.0296 | +0.1114 | +| A: causal | `terminal_flow` | 0.8876 ± 0.0224 | +0.0943 | +| B: spectral | `terminal_norm` | 0.7576 ± 0.0173 | +0.0000 | +| C: mixed | `disc_nll_total` | 0.9121 ± 0.0046 | +0.1393 | +| A+C combo | `disc_nll_total` | 0.9191 ± 0.0081 | +0.1337 | + +### → cicddos2019 + +| Route | Best score | AUROC | Δ (vs same-route's terminal_norm) | +|---|---|---|---| +| baseline | `velocity_total` | 0.8837 ± 0.0291 | +0.1364 | +| A: causal | `velocity_total` | 0.9027 ± 0.0039 | +0.1273 | +| B: spectral | `curvature_packet` | 0.8802 ± 0.0385 | +0.1462 | +| C: mixed | `disc_nll_ch7` | 0.8407 ± 0.0193 | +0.0037 | +| A+C combo | `disc_nll_ch7` | 0.8476 ± 0.0066 | +0.0115 | + +## All key scores → cicids2017 + +| Score | baseline | A: causal | B: spectral | C: mixed | A+C combo | +|---|---|---|---|---|---| +| `terminal_norm` | 0.7700 ± 0.0133 | 0.7933 ± 0.0273 | 0.7576 ± 0.0173 | 0.7728 ± 0.0108 | 0.7854 ± 0.0033 | +| `terminal_flow` | 0.8814 ± 0.0296 | 0.8876 ± 0.0224 | 0.7395 ± 0.0024 | 0.8615 ± 0.0265 | 0.8745 ± 0.0148 | +| `terminal_packet` | 0.7791 ± 0.0195 | 0.8254 ± 0.0209 | 0.7432 ± 0.0194 | 0.7870 ± 0.0087 | 0.8101 ± 0.0122 | +| `flow_consistency` | 0.6391 ± 0.0187 | 0.6219 ± 0.0106 | 0.7048 ± 0.0122 | — | — | +| `packet_consistency` | 0.7666 ± 0.0156 | 0.7658 ± 0.0064 | 0.7477 ± 0.0196 | — | — | +| `consistency_total` | 0.6574 ± 0.0224 | 0.6292 ± 0.0060 | 0.7250 ± 0.0064 | — | — | +| `causal_surprisal_packet_median` | 0.4515 ± 0.0807 | 0.5580 ± 0.0438 | 0.5870 ± 0.0280 | — | — | +| `causal_surprisal_total` | 0.5523 ± 0.0104 | 0.5878 ± 0.0107 | 0.3691 ± 0.0296 | — | — | +| `direction_drift_packet_median` | 0.3362 ± 0.1083 | 0.3378 ± 0.0157 | 0.6034 ± 0.0683 | — | — | +| `pna_packet_median` | 0.4152 ± 0.0421 | 0.5953 ± 0.0226 | 0.3447 ± 0.0751 | — | — | +| `kappa2_speed2norm_packet_median` | 0.4152 ± 0.0421 | 0.5953 ± 0.0226 | 0.3447 ± 0.0751 | — | — | +| `curvature_packet` | 0.6254 ± 0.0707 | 0.8467 ± 0.0123 | 0.6734 ± 0.0512 | — | — | +| `disc_nll_total` | — | — | — | 0.9121 ± 0.0046 | 0.9191 ± 0.0081 | +| `disc_nll_ch3` | — | — | — | 0.8825 ± 0.0043 | 0.8752 ± 0.0075 | +| `disc_nll_ch7` | — | — | — | 0.6554 ± 0.0404 | 0.6615 ± 0.0479 | + +## All key scores → cicddos2019 + +| Score | baseline | A: causal | B: spectral | C: mixed | A+C combo | +|---|---|---|---|---|---| +| `terminal_norm` | 0.7473 ± 0.0223 | 0.7754 ± 0.0214 | 0.7339 ± 0.0454 | 0.8371 ± 0.0117 | 0.8361 ± 0.0118 | +| `terminal_flow` | 0.7102 ± 0.0245 | 0.7123 ± 0.0153 | 0.6108 ± 0.0557 | 0.8330 ± 0.0027 | 0.8367 ± 0.0052 | +| `terminal_packet` | 0.6789 ± 0.0538 | 0.7201 ± 0.0496 | 0.5941 ± 0.0320 | 0.6644 ± 0.0928 | 0.6871 ± 0.0731 | +| `flow_consistency` | 0.7632 ± 0.0108 | 0.7836 ± 0.0133 | 0.5985 ± 0.0498 | — | — | +| `packet_consistency` | 0.5124 ± 0.0569 | 0.6214 ± 0.1196 | 0.7102 ± 0.1230 | — | — | +| `consistency_total` | 0.7601 ± 0.0094 | 0.7865 ± 0.0154 | 0.6378 ± 0.0625 | — | — | +| `causal_surprisal_packet_median` | 0.1692 ± 0.0532 | 0.1773 ± 0.0258 | 0.2808 ± 0.0978 | — | — | +| `causal_surprisal_total` | 0.3469 ± 0.0508 | 0.3035 ± 0.0216 | 0.1491 ± 0.0579 | — | — | +| `direction_drift_packet_median` | 0.3153 ± 0.0540 | 0.1962 ± 0.0538 | 0.5364 ± 0.1001 | — | — | +| `pna_packet_median` | 0.3726 ± 0.0784 | 0.4766 ± 0.1835 | 0.7535 ± 0.1415 | — | — | +| `kappa2_speed2norm_packet_median` | 0.3726 ± 0.0784 | 0.4766 ± 0.1835 | 0.7535 ± 0.1415 | — | — | +| `curvature_packet` | 0.6218 ± 0.0869 | 0.6687 ± 0.1187 | 0.8802 ± 0.0385 | — | — | +| `disc_nll_total` | — | — | — | 0.3780 ± 0.0043 | 0.4222 ± 0.0506 | +| `disc_nll_ch3` | — | — | — | 0.3297 ± 0.1172 | 0.2332 ± 0.0488 | +| `disc_nll_ch7` | — | — | — | 0.8407 ± 0.0193 | 0.8476 ± 0.0066 | + +## Route C ensemble (terminal_norm + disc_nll) → cicids2017 + +| α | seed42 | seed43 | seed44 | mean ± std | +|---|---|---|---|---| +| 0.00 | 0.9180 | 0.9069 | 0.9114 | **0.9121 ± 0.0046** | +| 0.50 | 0.8780 | 0.8473 | 0.8719 | **0.8657 ± 0.0132** | +| 0.70 | 0.8426 | 0.8092 | 0.8397 | **0.8305 ± 0.0151** | +| 0.80 | 0.8182 | 0.7922 | 0.8133 | **0.8079 ± 0.0113** | +| 0.90 | 0.8027 | 0.7741 | 0.7972 | **0.7913 ± 0.0124** | +| 1.00 | 0.7843 | 0.7583 | 0.7757 | **0.7728 ± 0.0108** | + +## Route C ensemble (terminal_norm + disc_nll) → cicddos2019 + +| α | seed42 | seed43 | seed44 | mean ± std | +|---|---|---|---|---| +| 0.00 | 0.3802 | 0.3719 | 0.3817 | **0.3780 ± 0.0043** | +| 0.50 | 0.7318 | 0.7479 | 0.6868 | **0.7222 ± 0.0258** | +| 0.70 | 0.7961 | 0.8147 | 0.7666 | **0.7925 ± 0.0198** | +| 0.80 | 0.8096 | 0.8299 | 0.7887 | **0.8094 ± 0.0168** | +| 0.90 | 0.8214 | 0.8466 | 0.8137 | **0.8272 ± 0.0141** | +| 1.00 | 0.8318 | 0.8533 | 0.8260 | **0.8371 ± 0.0117** | + +## Run inventory + +- baseline → cicids2017: seeds = [42, 43, 44] +- baseline → cicddos2019: seeds = [42, 43, 44] +- A: causal → cicids2017: seeds = [42, 43, 44] +- A: causal → cicddos2019: seeds = [42, 43, 44] +- B: spectral → cicids2017: seeds = [42, 43, 44] +- B: spectral → cicddos2019: seeds = [42, 43, 44] +- C: mixed → cicids2017: seeds = [42, 43, 44] +- C: mixed → cicddos2019: seeds = [42, 43, 44] +- A+C combo → cicids2017: seeds = [42, 43, 44] +- A+C combo → cicddos2019: seeds = [42, 43, 44] diff --git a/artifacts/route_comparison/PROTOCOL.md b/artifacts/route_comparison/PROTOCOL.md new file mode 100644 index 0000000..9cfaf6f --- /dev/null +++ b/artifacts/route_comparison/PROTOCOL.md @@ -0,0 +1,83 @@ +# Route Comparison Protocol + +Goal: compare three FM-mechanism × traffic-property route variants on a unified +training base. All routes start from the current `Unified_CFM` SOTA recipe and +change one mechanism axis. + +## Unified base (LOCKED) + +| Item | Value | +|---|---| +| Dataset | CICIoT2023 | +| Source store | `datasets/ciciot2023/processed/full_store/` | +| Flows | `datasets/ciciot2023/processed/full_store/flows.parquet` | +| Flow features | `datasets/ciciot2023/processed/flow_features.parquet` (canonical 20-d) | +| Train: benign | 10,000 (Shafir within-dataset protocol) | +| Sequence length | T = 64 | +| Packet preprocess | `mixed_dequant` (Routes A/B); raw binaries (Route C) | +| Benign split | 80/20, `split_seed=42` | +| Val cap | 10,000 | +| Attack cap | 20,000 (stratified) | +| Multi-seed | {42, 43, 44} | + +## Architecture base (LOCKED) + +| Item | Value | +|---|---| +| `d_model` | 128 | +| `n_layers` | 4 | +| `n_heads` | 4 | +| `mlp_ratio` | 4.0 | +| `time_dim` | 64 | +| `sigma` | 0.1 | +| `use_ot` | True | +| `lambda_flow / lambda_packet` | 0.3 / 0.3 | +| `packet_mask_ratio` | 0.5 | +| Optimizer | AdamW, lr=3e-4, wd=0.01, grad_clip=1.0 | +| Schedule | CosineAnnealingLR over total steps | +| Epochs | 50 | +| Batch size | 256 | + +## Routes + +| Route | Mechanism axis | Traffic property targeted | +|---|---|---| +| **Baseline** | Standard UnifiedCFM (current SOTA) | — | +| **A: Causal** | Packet-causal attention mask | Protocol causality (TCP/HTTP handshake) | +| **B: Spectral** | Append K=8-band DFT of (size, IAT) — 32 dims — to flow features (`flow_dim` 20→52); model architecture unchanged | Burstiness / LRD / self-similarity | +| **C: Mixed FM** | Continuous-CFM on (size,IAT,win) + DFM on flags | Discrete-continuous mixed channels | + +Route D (Edit Flows) is deferred until A/B/C show signal. + +## Reporting + +Each route × seed produces: + +``` +artifacts/route_comparison/_seed/ +├── model.pt +├── config.yaml # actual config used +├── history.json +├── phase1_summary.json # 34-score per-attack-class AUROC table +└── train.log +``` + +Final aggregate at `artifacts/route_comparison/RESULTS.md`: + +``` +| Route | terminal_norm | route-specific score | param count | train wall | +| baseline | 0.962 (existing) | — | 1.23M | ~2 min | +| A | ? | causal_surprisal_packet_median | ? | ? | +| B | ? | velocity_freq | ? | ? | +| C | ? | nll_disc + terminal_cont | ? | ? | +``` + +Plus per-attack-class breakdown for the top 10 attack labels by support. + +## Baseline reference (single-seed, from existing run) + +`artifacts/runs/unified_cfm_ciciot2023_2026_04_29/`: +- 50 epochs, σ=0.1, λ=0.3 +- final `auroc_terminal_norm` = **0.962** +- This is the number to compare against; we'll re-run it under multi-seed for + fair comparison. diff --git a/artifacts/route_comparison/RESULTS.md b/artifacts/route_comparison/RESULTS.md new file mode 100644 index 0000000..fad6e26 --- /dev/null +++ b/artifacts/route_comparison/RESULTS.md @@ -0,0 +1,93 @@ +# Route Comparison Results — CICIoT2023 (multi-seed) + +Phase1 eval: AUROC over benign val (5k cap) vs all attacks (10k cap), 3 seeds each. + +## Each route's best AUROC (overall) + +| Route | Best score | AUROC | Δ vs baseline-best | +|---|---|---|---| +| baseline | `terminal_norm` | 0.9612 ± 0.0017 | — | +| A: causal | `terminal_norm` | 0.9636 ± 0.0006 | +0.0024 | +| B: spectral | `terminal_norm` | 0.9619 ± 0.0013 | +0.0007 | +| C: mixed | `terminal_packet` | 0.9667 ± 0.0010 | +0.0056 | +| A+C combo | `terminal_packet` | 0.9671 ± 0.0002 | +0.0059 | + +## Primary score: `terminal_norm` + +| Route | mean ± std | seeds | +|---|---|---| +| baseline | 0.9612 ± 0.0017 | [42, 43, 44] | +| A: causal | 0.9636 ± 0.0006 | [42, 43, 44] | +| B: spectral | 0.9619 ± 0.0013 | [42, 43, 44] | +| C: mixed | 0.9625 ± 0.0028 | [42, 43, 44] | +| A+C combo | 0.9604 ± 0.0022 | [42, 43, 44] | + +## Route-specific signature scores (mean ± std, 3 seeds) + +### Route A signature (consistency family) + +| Score | baseline | A: causal | B: spectral | C: mixed | A+C combo | +|---|---|---|---|---|---| +| `flow_consistency` | 0.8862 ± 0.0301 | 0.9171 ± 0.0089 | 0.7981 ± 0.0399 | — | — | +| `packet_consistency` | 0.8127 ± 0.0250 | 0.8526 ± 0.0128 | 0.8012 ± 0.0209 | — | — | +| `consistency_total` | 0.9019 ± 0.0255 | 0.9310 ± 0.0089 | 0.8306 ± 0.0253 | — | — | +| `causal_surprisal_total` | 0.5091 ± 0.0281 | 0.5669 ± 0.0301 | 0.2865 ± 0.0200 | — | — | +| `causal_surprisal_packet_median` | 0.4075 ± 0.0767 | 0.5877 ± 0.0193 | 0.8205 ± 0.0497 | — | — | + +### Route B signature (curvature/dynamics) + +| Score | baseline | A: causal | B: spectral | C: mixed | A+C combo | +|---|---|---|---|---|---| +| `kappa2_speed2norm_packet_median` | 0.4080 ± 0.1531 | 0.4022 ± 0.1015 | 0.2354 ± 0.0562 | — | — | +| `direction_drift_packet_median` | 0.1511 ± 0.0561 | 0.1334 ± 0.0240 | 0.4267 ± 0.0632 | — | — | +| `pna_packet_median` | 0.4080 ± 0.1531 | 0.4022 ± 0.1015 | 0.2354 ± 0.0562 | — | — | +| `curvature_packet` | 0.7971 ± 0.1276 | 0.8578 ± 0.0539 | 0.7965 ± 0.0978 | — | — | + +### Route C signature (discrete NLL) + +| Score | baseline | A: causal | B: spectral | C: mixed | A+C combo | +|---|---|---|---|---|---| +| `disc_nll_total` | — | — | — | 0.8853 ± 0.0073 | 0.8994 ± 0.0098 | +| `disc_nll_ch3` | — | — | — | 0.8681 ± 0.0088 | 0.8606 ± 0.0257 | +| `disc_nll_ch4` | — | — | — | 0.8082 ± 0.0078 | 0.8353 ± 0.0152 | +| `disc_nll_ch5` | — | — | — | 0.7897 ± 0.0079 | 0.8474 ± 0.0208 | +| `disc_nll_ch7` | — | — | — | 0.8818 ± 0.0227 | 0.8934 ± 0.0098 | + +## Route C ensemble: α·terminal_norm + (1−α)·disc_nll_total (z-scored) + +| α | seed42 | seed43 | seed44 | mean ± std | +|---|---|---|---|---| +| 0.00 | 0.8907 | 0.8902 | 0.8749 | **0.8853 ± 0.0073** | +| 0.25 | 0.9479 | 0.9478 | 0.9429 | **0.9462 ± 0.0023** | +| 0.50 | 0.9616 | 0.9605 | 0.9562 | **0.9594 ± 0.0023** | +| 0.70 | 0.9672 | 0.9655 | 0.9610 | **0.9646 ± 0.0026** | +| 0.80 | 0.9681 | 0.9664 | 0.9616 | **0.9654 ± 0.0028** | +| 0.90 | 0.9674 | 0.9659 | 0.9610 | **0.9648 ± 0.0027** | +| 1.00 | 0.9653 | 0.9634 | 0.9587 | **0.9625 ± 0.0028** | + +(α=1.0 = terminal_norm only; α=0.0 = disc_nll only.) + +## Per-attack-class AUROC (top 12, terminal_norm) + +| Class | n | baseline | A: causal | B: spectral | C: mixed | A+C combo | +|---|---|---|---|---|---|---| +| ddos-tcp_flood | 1255 | 0.989±0.003 | 0.992±0.000 | 0.985±0.004 | 0.988±0.003 | 0.985±0.005 | +| ddos-syn_flood | 1195 | 0.996±0.001 | 0.997±0.000 | 0.995±0.001 | 0.997±0.001 | 0.997±0.000 | +| dos-tcp_flood | 1098 | 0.996±0.001 | 0.997±0.000 | 0.991±0.003 | 0.989±0.003 | 0.985±0.004 | +| ddos-pshack_flood | 1030 | 0.990±0.005 | 0.994±0.001 | 0.995±0.002 | 0.985±0.003 | 0.980±0.005 | +| ddos-http_flood | 918 | 0.990±0.001 | 0.989±0.001 | 0.985±0.001 | 0.986±0.002 | 0.984±0.002 | +| dos-syn_flood | 844 | 0.997±0.001 | 0.998±0.001 | 0.993±0.002 | 0.993±0.002 | 0.994±0.001 | +| dos-http_flood | 716 | 0.988±0.002 | 0.988±0.002 | 0.985±0.002 | 0.983±0.003 | 0.981±0.004 | +| vulnerabilityscan | 568 | 0.806±0.008 | 0.803±0.008 | 0.783±0.016 | 0.806±0.001 | 0.804±0.003 | +| recon-portscan | 370 | 0.927±0.009 | 0.927±0.008 | 0.920±0.007 | 0.935±0.013 | 0.935±0.010 | +| recon-osscan | 368 | 0.931±0.004 | 0.932±0.004 | 0.923±0.007 | 0.941±0.003 | 0.939±0.003 | +| ddos-ack_fragmentation | 310 | 0.987±0.003 | 0.987±0.004 | 0.989±0.002 | 0.989±0.002 | 0.988±0.003 | +| ddos-slowloris | 304 | 0.940±0.005 | 0.952±0.010 | 0.953±0.004 | 0.939±0.008 | 0.939±0.003 | + +## Run inventory + +- **baseline** (`baseline_ciciot2023_seed*`): seeds = [42, 43, 44] +- **A: causal** (`route_a_causal_ciciot2023_seed*`): seeds = [42, 43, 44] +- **B: spectral** (`route_b_spectral_ciciot2023_seed*`): seeds = [42, 43, 44] +- **C: mixed** (`route_c_mixed_ciciot2023_seed*`): seeds = [42, 43, 44] +- **A+C combo** (`route_ac_combo_ciciot2023_seed*`): seeds = [42, 43, 44] diff --git a/artifacts/route_comparison/SCORE_ROUTER.md b/artifacts/route_comparison/SCORE_ROUTER.md new file mode 100644 index 0000000..3637e0f --- /dev/null +++ b/artifacts/route_comparison/SCORE_ROUTER.md @@ -0,0 +1,35 @@ +# Score-vector auto-selection: max-of-|z| / Mahalanobis vs fixed scores + +Aggregators are fit on **benign val only** (no attack labels). All numbers +are 3-seed mean ± std on A+C combo (Mixed_CFM + causal-packet attention). + +Note on fairness: `auc_best_fixed` is selection-biased (picks per-dataset best +score post-hoc on test set). `max_abs_z` and `mahalanobis` are NOT — they only +use benign val to fit aggregator parameters. + +## Within-dataset(A+C combo on each dataset's own benign/attack) + +| Dataset | term_norm | best fixed | max-\|z\| (all) | mahal-OAS (all) | **mahal-OAS (term3)** | **mahal-OAS (disc7)** | +|---|---|---|---|---|---|---| +| iscxtor2016 | 0.9954 ± 0.0007 | 0.9955 ± 0.0005 | 0.9908 ± 0.0011 | 0.9908 ± 0.0012 | **0.9937 ± 0.0011** | **0.7705 ± 0.0528** | +| cicids2017 | 0.9884 ± 0.0012 | 0.9932 ± 0.0013 | 0.9807 ± 0.0020 | 0.9845 ± 0.0030 | **0.9771 ± 0.0034** | **0.9840 ± 0.0047** | +| cicddos2019 | 0.9970 ± 0.0005 | 0.9970 ± 0.0005 | 0.9883 ± 0.0012 | 0.9913 ± 0.0009 | **0.9959 ± 0.0005** | **0.7185 ± 0.0382** | +| ciciot2023 | 0.9604 ± 0.0022 | 0.9671 ± 0.0002 | 0.9523 ± 0.0038 | 0.9594 ± 0.0028 | **0.9511 ± 0.0032** | **0.9064 ± 0.0087** | + +## Cross-dataset(A+C combo trained on CICIoT2023 → eval on target) + +| Target | term_norm | best fixed | max-\|z\| (all) | mahal-OAS (all) | **mahal-OAS (term3)** | **mahal-OAS (disc7)** | +|---|---|---|---|---|---|---| +| cicids2017 | 0.7854 ± 0.0033 | 0.9191 ± 0.0081 | 0.8750 ± 0.0137 | 0.8983 ± 0.0098 | **0.8012 ± 0.0037** | **0.8836 ± 0.0178** | +| cicddos2019 | 0.8361 ± 0.0118 | 0.8851 ± 0.0174 | 0.6033 ± 0.0795 | 0.8944 ± 0.0068 | **0.7437 ± 0.0396** | **0.7221 ± 0.0572** | + +## Best-fixed-score winner per setup + +| Setup | seed42 | seed43 | seed44 | +|---|---|---|---| +| within iscxtor2016 | terminal_packet (0.9950) | terminal_norm (0.9963) | terminal_norm (0.9953) | +| within cicids2017 | terminal_packet (0.9929) | terminal_packet (0.9918) | terminal_packet (0.9949) | +| within cicddos2019 | terminal_norm (0.9966) | terminal_norm (0.9977) | terminal_norm (0.9966) | +| within ciciot2023 | terminal_packet (0.9669) | terminal_packet (0.9674) | terminal_packet (0.9668) | +| cross→cicids2017 | disc_nll_total (0.9194) | disc_nll_total (0.9090) | disc_nll_total (0.9288) | +| cross→cicddos2019 | disc_nll_ch2 (0.8623) | disc_nll_ch2 (0.8884) | disc_nll_ch2 (0.9046) | diff --git a/artifacts/route_comparison/SOTA_COMPARISON.md b/artifacts/route_comparison/SOTA_COMPARISON.md new file mode 100644 index 0000000..19bad41 --- /dev/null +++ b/artifacts/route_comparison/SOTA_COMPARISON.md @@ -0,0 +1,70 @@ +# SOTA Comparison: A+C combo vs existing UnifiedCFM + +All 4 datasets, 3 seeds each, within-dataset Shafir 10K/10K protocol. +Existing UnifiedCFM uses Phase-2 consistency loss (λ_flow=λ_packet=0.3). +A+C combo uses Mixed_CFM (continuous CFM + DFM) + causal-packet attention, +**no Phase-2 consistency loss**. lambda_disc=1.0, sigma=0.1, use_ot=True. + +## Headline: A+C combo's best score per dataset + +| Dataset | Shafir 2026 | Existing UnifiedCFM (SOTA) | A+C combo `terminal_norm` | A+C combo `terminal_packet` | A+C combo `disc_nll_total` | A+C best | New SOTA? | +|---|---|---|---|---|---|---|---| +| ISCXTor2016 (NonTor → Tor) | 0.8731 | 0.9945 ± 0.0011 | 0.9954 ± 0.0007 | 0.9953 ± 0.0004 | 0.7063 ± 0.0201 | `terminal_norm` 0.9954 ± 0.0007 | ✅ +0.0009 | +| CICIDS2017 within (Shafir 10k/10k) | 0.9303 | 0.9858 ± 0.0021 | 0.9884 ± 0.0012 | 0.9932 ± 0.0013 | 0.9839 ± 0.0005 | `terminal_packet` 0.9932 ± 0.0013 | ✅ +0.0074 | +| CICDDoS2019 within | 0.9300 | 0.9960 ± 0.0010 | 0.9970 ± 0.0005 | 0.9909 ± 0.0010 | 0.5593 ± 0.0423 | `terminal_norm` 0.9970 ± 0.0005 | ✅ +0.0010 | +| CICIoT2023 within (multi-seed) | — | 0.9612 ± 0.0017 | 0.9604 ± 0.0022 | 0.9671 ± 0.0002 | 0.8994 ± 0.0098 | `terminal_packet` 0.9671 ± 0.0002 | ✅ +0.0059 | + +## Per-dataset full scoring + +### ISCXTor2016 (NonTor → Tor) + +| Score | mean ± std | seeds | +|---|---|---| +| `terminal_norm` | 0.9954 ± 0.0007 | [42, 43, 44] | +| `terminal_flow` | 0.9283 ± 0.0096 | [42, 43, 44] | +| `terminal_packet` | 0.9953 ± 0.0004 | [42, 43, 44] | +| `disc_nll_total` | 0.7063 ± 0.0201 | [42, 43, 44] | +| `disc_nll_ch3` | 0.7020 ± 0.0314 | [42, 43, 44] | +| `disc_nll_ch4` | 0.4362 ± 0.0278 | [42, 43, 44] | +| `disc_nll_ch5` | 0.4626 ± 0.0253 | [42, 43, 44] | +| `disc_nll_ch7` | 0.6957 ± 0.0476 | [42, 43, 44] | + +### CICIDS2017 within (Shafir 10k/10k) + +| Score | mean ± std | seeds | +|---|---|---| +| `terminal_norm` | 0.9884 ± 0.0012 | [42, 43, 44] | +| `terminal_flow` | 0.9628 ± 0.0024 | [42, 43, 44] | +| `terminal_packet` | 0.9932 ± 0.0013 | [42, 43, 44] | +| `disc_nll_total` | 0.9839 ± 0.0005 | [42, 43, 44] | +| `disc_nll_ch3` | 0.6890 ± 0.1267 | [42, 43, 44] | +| `disc_nll_ch4` | 0.7512 ± 0.1043 | [42, 43, 44] | +| `disc_nll_ch5` | 0.9055 ± 0.0112 | [42, 43, 44] | +| `disc_nll_ch7` | 0.7479 ± 0.1000 | [42, 43, 44] | + +### CICDDoS2019 within + +| Score | mean ± std | seeds | +|---|---|---| +| `terminal_norm` | 0.9970 ± 0.0005 | [42, 43, 44] | +| `terminal_flow` | 0.9648 ± 0.0028 | [42, 43, 44] | +| `terminal_packet` | 0.9909 ± 0.0010 | [42, 43, 44] | +| `disc_nll_total` | 0.5593 ± 0.0423 | [42, 43, 44] | +| `disc_nll_ch3` | 0.2648 ± 0.0263 | [42, 43, 44] | +| `disc_nll_ch4` | 0.4641 ± 0.0579 | [42, 43, 44] | +| `disc_nll_ch5` | 0.3949 ± 0.0518 | [42, 43, 44] | +| `disc_nll_ch7` | 0.9280 ± 0.0253 | [42, 43, 44] | + +### CICIoT2023 within (multi-seed) + +| Score | mean ± std | seeds | +|---|---|---| +| `terminal_norm` | 0.9604 ± 0.0022 | [42, 43, 44] | +| `terminal_flow` | 0.9186 ± 0.0031 | [42, 43, 44] | +| `terminal_packet` | 0.9671 ± 0.0002 | [42, 43, 44] | +| `disc_nll_total` | 0.8994 ± 0.0098 | [42, 43, 44] | +| `disc_nll_ch3` | 0.8606 ± 0.0257 | [42, 43, 44] | +| `disc_nll_ch4` | 0.8353 ± 0.0152 | [42, 43, 44] | +| `disc_nll_ch5` | 0.8474 ± 0.0208 | [42, 43, 44] | +| `disc_nll_ch7` | 0.8934 ± 0.0098 | [42, 43, 44] | + diff --git a/artifacts/route_comparison/aggregate_cross.py b/artifacts/route_comparison/aggregate_cross.py new file mode 100644 index 0000000..028042e --- /dev/null +++ b/artifacts/route_comparison/aggregate_cross.py @@ -0,0 +1,174 @@ +from __future__ import annotations +import json +import re +from pathlib import Path +import numpy as np +from sklearn.metrics import roc_auc_score +ROOT = Path(__file__).resolve().parent +CROSS_DIR = ROOT / 'cross' +NAME_RE = re.compile('^(?P.+?)_seed(?P\\d+)_to_(?Pcicids2017|cicddos2019)$') +ROUTES = [('baseline', 'baseline'), ('A: causal', 'route_a_causal'), ('B: spectral', 'route_b_spectral'), ('C: mixed', 'route_c_mixed'), ('A+C combo', 'route_ac_combo')] +TARGETS = ['cicids2017', 'cicddos2019'] +PRIMARY_SCORES = ['terminal_norm', 'terminal_flow', 'terminal_packet', 'flow_consistency', 'packet_consistency', 'consistency_total', 'causal_surprisal_packet_median', 'causal_surprisal_total', 'direction_drift_packet_median', 'pna_packet_median', 'kappa2_speed2norm_packet_median', 'curvature_packet', 'disc_nll_total', 'disc_nll_ch3', 'disc_nll_ch7'] + +def _collect() -> dict[tuple[str, str], dict[int, dict]]: + out: dict[tuple[str, str], dict[int, dict]] = {} + for f in sorted(CROSS_DIR.glob('*.json')): + m = NAME_RE.match(f.stem) + if not m: + continue + key = (m.group('route'), m.group('target')) + out.setdefault(key, {})[int(m.group('seed'))] = json.loads(f.read_text()) + return out + +def _ensemble_sweep(npz_path: Path) -> dict[float, float] | None: + if not npz_path.exists(): + return None + z = np.load(npz_path, allow_pickle=True) + keys = set(z.files) + if 'b_terminal_norm' not in keys or 'b_disc_nll_total' not in keys: + return None + v_tn = z['b_terminal_norm'] + a_tn = z['a_terminal_norm'] + v_dn = z['b_disc_nll_total'] + a_dn = z['a_disc_nll_total'] + + def zsc(v, a): + (mu, sd) = (v.mean(), v.std() + 1e-09) + return ((v - mu) / sd, (a - mu) / sd) + (v_tn_z, a_tn_z) = zsc(v_tn, a_tn) + (v_dn_z, a_dn_z) = zsc(v_dn, a_dn) + out = {} + for alpha in (0.0, 0.5, 0.7, 0.8, 0.9, 1.0): + s_v = alpha * v_tn_z + (1.0 - alpha) * v_dn_z + s_a = alpha * a_tn_z + (1.0 - alpha) * a_dn_z + y = np.r_[np.zeros(len(s_v)), np.ones(len(s_a))] + s = np.r_[s_v, s_a] + out[alpha] = float(roc_auc_score(y, s)) + return out + +def _mean_std(vs: list[float]) -> tuple[float, float]: + arr = np.asarray([v for v in vs if v == v], dtype=np.float64) + if arr.size == 0: + return (float('nan'), float('nan')) + return (float(arr.mean()), float(arr.std())) + +def main() -> None: + data = _collect() + rows: list[str] = [] + rows.append('# Cross-Dataset Eval — CICIoT2023 → {CICIDS2017, CICDDoS2019}') + rows.append('') + rows.append("All models trained on CICIoT2023 (10K benign), evaluated on each target's") + rows.append('10K benign + 10K stratified attack. Source-domain norm stats applied.') + rows.append('3 seeds each. AUROC mean ± std.') + rows.append('') + rows.append('## Primary score: `terminal_norm`') + rows.append('') + header = '| Route | within-CICIoT2023 (ref) | → CICIDS2017 | → CICDDoS2019 |' + rows.append(header) + rows.append('|---|---|---|---|') + within_fallback = {'baseline': (0.9612, 0.0017), 'A: causal': (0.9636, 0.0006), 'B: spectral': (0.9619, 0.0013), 'C: mixed': (0.9625, 0.0028), 'A+C combo': (0.9587, 0.0017)} + within_terminal: dict[str, tuple[float, float]] = {} + for (label, prefix) in ROUTES: + within_seeds = sorted(ROOT.glob(f'{prefix}_seed*/phase1_summary.json')) + vals: list[float] = [] + for f in within_seeds: + try: + s = json.loads(f.read_text()) + v = s.get('overall', {}).get('terminal_norm', {}).get('auroc') + if v is not None: + vals.append(v) + except Exception: + pass + if vals: + within_terminal[label] = _mean_std(vals) + else: + within_terminal[label] = within_fallback.get(label, (float('nan'), float('nan'))) + for (label, prefix) in ROUTES: + cells = [label] + (wm, ws) = within_terminal[label] + cells.append(f'{wm:.4f} ± {ws:.4f}') + for tgt in TARGETS: + seeds = data.get((prefix, tgt), {}) + vals = [s['overall'].get('terminal_norm', {}).get('auroc', float('nan')) for s in seeds.values()] + (m, sd) = _mean_std(vals) + cells.append(f'{m:.4f} ± {sd:.4f}' if m == m else '—') + rows.append('| ' + ' | '.join(cells) + ' |') + rows.append('') + rows.append("## Each route's best score per target") + rows.append('') + for tgt in TARGETS: + rows.append(f'### → {tgt}') + rows.append('') + rows.append("| Route | Best score | AUROC | Δ (vs same-route's terminal_norm) |") + rows.append('|---|---|---|---|') + for (label, prefix) in ROUTES: + seeds = data.get((prefix, tgt), {}) + if not seeds: + rows.append(f'| {label} | — | — | — |') + continue + score_means: dict[str, float] = {} + for s in seeds.values(): + for (k, v) in s.get('overall', {}).items(): + score_means.setdefault(k, []).append(v.get('auroc', float('nan'))) + mean_per_score = {k: _mean_std(v)[0] for (k, v) in score_means.items()} + mean_per_score = {k: v for (k, v) in mean_per_score.items() if v == v} + if not mean_per_score: + rows.append(f'| {label} | — | — | — |') + continue + best = max(mean_per_score, key=mean_per_score.get) + best_v = mean_per_score[best] + best_sd = _mean_std(score_means[best])[1] + tn = mean_per_score.get('terminal_norm', float('nan')) + delta = f'{best_v - tn:+.4f}' if tn == tn else '—' + rows.append(f'| {label} | `{best}` | {best_v:.4f} ± {best_sd:.4f} | {delta} |') + rows.append('') + for tgt in TARGETS: + rows.append(f'## All key scores → {tgt}') + rows.append('') + header = '| Score | ' + ' | '.join((label for (label, _) in ROUTES)) + ' |' + rows.append(header) + rows.append('|---' * (1 + len(ROUTES)) + '|') + for sc in PRIMARY_SCORES: + cells = [f'`{sc}`'] + for (label, prefix) in ROUTES: + seeds = data.get((prefix, tgt), {}) + vals = [s['overall'].get(sc, {}).get('auroc', float('nan')) for s in seeds.values()] + (m, sd) = _mean_std(vals) + cells.append(f'{m:.4f} ± {sd:.4f}' if m == m else '—') + rows.append('| ' + ' | '.join(cells) + ' |') + rows.append('') + for tgt in TARGETS: + rows.append(f'## Route C ensemble (terminal_norm + disc_nll) → {tgt}') + rows.append('') + c_seeds = data.get(('route_c_mixed', tgt), {}) + if c_seeds: + alphas = (0.0, 0.5, 0.7, 0.8, 0.9, 1.0) + rows.append('| α | ' + ' | '.join((f'seed{s}' for s in sorted(c_seeds.keys()))) + ' | mean ± std |') + rows.append('|---' * (2 + len(c_seeds)) + '|') + seed_sweeps = {} + for s in c_seeds: + npz = CROSS_DIR / f'route_c_mixed_seed{s}_to_{tgt}.npz' + seed_sweeps[s] = _ensemble_sweep(npz) or {} + for a in alphas: + cells = [f'{a:.2f}'] + vals = [] + for s in sorted(c_seeds.keys()): + v = seed_sweeps[s].get(a, float('nan')) + cells.append(f'{v:.4f}') + vals.append(v) + (m, sd) = _mean_std(vals) + cells.append(f'**{m:.4f} ± {sd:.4f}**') + rows.append('| ' + ' | '.join(cells) + ' |') + rows.append('') + rows.append('## Run inventory') + rows.append('') + for (label, prefix) in ROUTES: + for tgt in TARGETS: + seeds = sorted(data.get((prefix, tgt), {}).keys()) + rows.append(f"- {label} → {tgt}: seeds = {(seeds if seeds else '(none)')}") + out = ROOT / 'CROSS_RESULTS.md' + out.write_text('\n'.join(rows) + '\n') + print(f'[wrote] {out}') +if __name__ == '__main__': + main() diff --git a/artifacts/route_comparison/aggregate_cross_matrix.py b/artifacts/route_comparison/aggregate_cross_matrix.py new file mode 100644 index 0000000..ded46e7 --- /dev/null +++ b/artifacts/route_comparison/aggregate_cross_matrix.py @@ -0,0 +1,176 @@ +from __future__ import annotations +import json +import re +from pathlib import Path +import numpy as np +from sklearn.covariance import OAS +from sklearn.metrics import roc_auc_score +ROOT = Path(__file__).resolve().parent +CROSS_DIR = ROOT / 'cross' +DATASETS = ['iscxtor2016', 'cicids2017', 'cicddos2019', 'ciciot2023'] +SEEDS = [42, 43, 44] + +def _mahal_eval(npz_path: Path, val_prefix: str, atk_prefix: str) -> float: + if not npz_path.exists(): + return float('nan') + z = np.load(npz_path, allow_pickle=True) + keys = sorted([k.replace(val_prefix, '') for k in z.files if k.startswith(val_prefix) and (not k.endswith('labels'))]) + val_S = np.stack([z[f'{val_prefix}{k}'] for k in keys], axis=1) + atk_S = np.stack([z[f'{atk_prefix}{k}'] for k in keys], axis=1) + val_S = np.nan_to_num(val_S, nan=0.0, posinf=1000000.0, neginf=-1000000.0) + atk_S = np.nan_to_num(atk_S, nan=0.0, posinf=1000000.0, neginf=-1000000.0) + if len(val_S) < 50 or len(atk_S) < 50: + return float('nan') + y = np.r_[np.zeros(len(val_S)), np.ones(len(atk_S))] + K = val_S.shape[1] + try: + oas = OAS().fit(val_S) + inv_cov = np.linalg.inv(oas.covariance_ + 1e-09 * np.eye(K)) + except Exception: + return float('nan') + mu = val_S.mean(0) + + def m(S): + d = S - mu + return np.einsum('ni,ij,nj->n', d, inv_cov, d) + s = np.r_[m(val_S), m(atk_S)] + s = np.nan_to_num(s, nan=0.0, posinf=1000000000000.0, neginf=-1000000000000.0) + try: + return float(roc_auc_score(y, s)) + except ValueError: + return float('nan') + +def _within_mahal(ds: str, seed: int) -> float: + md = ROOT / f'route_ac_combo_{ds}_seed{seed}' + return _mahal_eval(md / 'phase1_scores.npz', 'val_', 'atk_') + +def _within_terminal_norm(ds: str, seed: int) -> float: + f = ROOT / f'route_ac_combo_{ds}_seed{seed}' / 'phase1_summary.json' + if not f.exists(): + return float('nan') + return json.loads(f.read_text())['overall'].get('terminal_norm', {}).get('auroc', float('nan')) + +def _src_aliases(src: str) -> list[str]: + aliases = [src] + if src == 'cicddos2019': + aliases.append('ddos2019') + return aliases + +def _cross_mahal(src: str, tgt: str, seed: int) -> float: + candidates = [] + for alias in _src_aliases(src): + candidates.append(CROSS_DIR / f'route_ac_combo_seed{seed}_{alias}_to_{tgt}.npz') + if src == 'ciciot2023': + candidates.append(CROSS_DIR / f'route_ac_combo_seed{seed}_to_{tgt}.npz') + for c in candidates: + if c.exists(): + return _mahal_eval(c, 'b_', 'a_') + return float('nan') + +def _cross_terminal_norm(src: str, tgt: str, seed: int) -> float: + candidates = [] + for alias in _src_aliases(src): + candidates.append(CROSS_DIR / f'route_ac_combo_seed{seed}_{alias}_to_{tgt}.json') + if src == 'ciciot2023': + candidates.append(CROSS_DIR / f'route_ac_combo_seed{seed}_to_{tgt}.json') + for c in candidates: + if c.exists(): + d = json.loads(c.read_text()) + return d['overall'].get('terminal_norm', {}).get('auroc', float('nan')) + return float('nan') + +def _ms(vals: list[float]) -> str: + arr = np.asarray([v for v in vals if not np.isnan(v)], dtype=np.float64) + if arr.size == 0: + return '—' + if arr.size == 1: + return f'{arr[0]:.4f}' + return f'{arr.mean():.4f}±{arr.std():.4f}' + +def main() -> None: + rows: list[str] = [] + rows.append('# Full 4×4 Cross Matrix — A+C combo + Mahalanobis-OAS') + rows.append('') + rows.append('3-seed mean ± std. Diagonal = within-dataset; off-diagonal = cross.') + rows.append('Aggregator: Mahalanobis-OAS over 10-d A+C combo score vector,') + rows.append('fit on **target-dataset benign val only** (no attack labels).') + rows.append('') + rows.append('## Mahalanobis-OAS AUROC (4×4)') + rows.append('') + rows.append('| Source ↓ \\ Target → | ' + ' | '.join(DATASETS) + ' |') + rows.append('|---' * (1 + len(DATASETS)) + '|') + for src in DATASETS: + cells = [src] + for tgt in DATASETS: + if src == tgt: + vals = [_within_mahal(src, s) for s in SEEDS] + cells.append(f'_{_ms(vals)}_') + else: + vals = [_cross_mahal(src, tgt, s) for s in SEEDS] + cells.append(_ms(vals)) + rows.append('| ' + ' | '.join(cells) + ' |') + rows.append('') + rows.append('(Italic diagonal = within-dataset reference)') + rows.append('') + rows.append('## `terminal_norm` AUROC (4×4) — for comparison (selection-bias-free single fixed score)') + rows.append('') + rows.append('| Source ↓ \\ Target → | ' + ' | '.join(DATASETS) + ' |') + rows.append('|---' * (1 + len(DATASETS)) + '|') + for src in DATASETS: + cells = [src] + for tgt in DATASETS: + if src == tgt: + vals = [_within_terminal_norm(src, s) for s in SEEDS] + cells.append(f'_{_ms(vals)}_') + else: + vals = [_cross_terminal_norm(src, tgt, s) for s in SEEDS] + cells.append(_ms(vals)) + rows.append('| ' + ' | '.join(cells) + ' |') + rows.append('') + rows.append('## Δ Mahalanobis − terminal_norm (where positive, Mahalanobis is better)') + rows.append('') + rows.append('| Source ↓ \\ Target → | ' + ' | '.join(DATASETS) + ' |') + rows.append('|---' * (1 + len(DATASETS)) + '|') + for src in DATASETS: + cells = [src] + for tgt in DATASETS: + if src == tgt: + m = np.mean([v for v in [_within_mahal(src, s) for s in SEEDS] if not np.isnan(v)]) + t = np.mean([v for v in [_within_terminal_norm(src, s) for s in SEEDS] if not np.isnan(v)]) + else: + m = np.mean([v for v in [_cross_mahal(src, tgt, s) for s in SEEDS] if not np.isnan(v)]) + t = np.mean([v for v in [_cross_terminal_norm(src, tgt, s) for s in SEEDS] if not np.isnan(v)]) + if np.isnan(m) or np.isnan(t): + cells.append('—') + else: + d = m - t + if abs(d) < 0.005: + cells.append(f'{d:+.4f}') + elif d > 0: + cells.append(f'**{d:+.4f}**') + else: + cells.append(f'_{d:+.4f}_') + rows.append('| ' + ' | '.join(cells) + ' |') + rows.append('') + rows.append('## Per-source averaged cross-AUROC (Mahalanobis, off-diagonal mean)') + rows.append('') + rows.append('| Source | mean off-diag Mahalanobis | mean off-diag terminal_norm |') + rows.append('|---|---|---|') + for src in DATASETS: + m_offs = [] + t_offs = [] + for tgt in DATASETS: + if src == tgt: + continue + m_vals = [_cross_mahal(src, tgt, s) for s in SEEDS] + t_vals = [_cross_terminal_norm(src, tgt, s) for s in SEEDS] + m_offs.extend([v for v in m_vals if not np.isnan(v)]) + t_offs.extend([v for v in t_vals if not np.isnan(v)]) + m_mean = np.mean(m_offs) if m_offs else float('nan') + t_mean = np.mean(t_offs) if t_offs else float('nan') + rows.append(f'| {src} | {m_mean:.4f} | {t_mean:.4f} |') + out = ROOT / 'CROSS_MATRIX.md' + out.write_text('\n'.join(rows) + '\n') + print(f'[wrote] {out}') +if __name__ == '__main__': + main() diff --git a/artifacts/route_comparison/aggregate_full_sota.py b/artifacts/route_comparison/aggregate_full_sota.py new file mode 100644 index 0000000..2303451 --- /dev/null +++ b/artifacts/route_comparison/aggregate_full_sota.py @@ -0,0 +1,84 @@ +from __future__ import annotations +import json +import re +from pathlib import Path +import numpy as np +ROOT = Path(__file__).resolve().parent +SEED_RE = re.compile('_seed(\\d+)$') +EXISTING_SOTA = {'ISCXTor2016 (NonTor → Tor)': {'shafir_baseline': 0.8731, 'shafir_ref': 'Table VI', 'ours_existing': (0.9945, 0.0011), 'ours_score': 'terminal_norm', 'sigma': 0.1, 'ac_prefix': 'route_ac_combo_iscxtor2016'}, 'CICIDS2017 within (Shafir 10k/10k)': {'shafir_baseline': 0.9303, 'shafir_ref': 'Table VII', 'ours_existing': (0.9858, 0.0021), 'ours_score': 'terminal_norm', 'sigma': 0.6, 'ac_prefix': 'route_ac_combo_cicids2017'}, 'CICDDoS2019 within': {'shafir_baseline': 0.93, 'shafir_ref': 'Table IX, row 1', 'ours_existing': (0.996, 0.001), 'ours_score': 'terminal_norm', 'sigma': 0.1, 'ac_prefix': 'route_ac_combo_cicddos2019'}, 'CICIoT2023 within (multi-seed)': {'shafir_baseline': None, 'shafir_ref': None, 'ours_existing': (0.9612, 0.0017), 'ours_score': 'terminal_norm', 'sigma': 0.1, 'ac_prefix': 'route_ac_combo_ciciot2023'}} + +def _seeds(prefix: str) -> dict[int, Path]: + out = {} + for d in sorted(ROOT.glob(f'{prefix}_seed*')): + m = SEED_RE.search(d.name) + if m and (d / 'phase1_summary.json').exists(): + out[int(m.group(1))] = d + return out + +def _load(d: Path) -> dict: + return json.loads((d / 'phase1_summary.json').read_text()) + +def _mean_std(vs: list[float]) -> tuple[float, float]: + arr = np.asarray([v for v in vs if v == v], dtype=np.float64) + if arr.size == 0: + return (float('nan'), float('nan')) + return (float(arr.mean()), float(arr.std())) + +def main() -> None: + rows: list[str] = [] + rows.append('# SOTA Comparison: A+C combo vs existing UnifiedCFM') + rows.append('') + rows.append('All 4 datasets, 3 seeds each, within-dataset Shafir 10K/10K protocol.') + rows.append('Existing UnifiedCFM uses Phase-2 consistency loss (λ_flow=λ_packet=0.3).') + rows.append('A+C combo uses Mixed_CFM (continuous CFM + DFM) + causal-packet attention,') + rows.append('**no Phase-2 consistency loss**. lambda_disc=1.0, sigma=0.1, use_ot=True.') + rows.append('') + rows.append("## Headline: A+C combo's best score per dataset") + rows.append('') + rows.append('| Dataset | Shafir 2026 | Existing UnifiedCFM (SOTA) | A+C combo `terminal_norm` | A+C combo `terminal_packet` | A+C combo `disc_nll_total` | A+C best | New SOTA? |') + rows.append('|---|---|---|---|---|---|---|---|') + for (label, meta) in EXISTING_SOTA.items(): + seeds = _seeds(meta['ac_prefix']) + shafir_str = f"{meta['shafir_baseline']:.4f}" if meta['shafir_baseline'] else '—' + (existing_m, existing_sd) = meta['ours_existing'] + existing_str = f'{existing_m:.4f} ± {existing_sd:.4f}' + if not seeds: + rows.append(f'| {label} | {shafir_str} | {existing_str} | (running) | — | — | — | — |') + continue + vals_term = [_load(d).get('overall', {}).get('terminal_norm', {}).get('auroc', float('nan')) for d in seeds.values()] + vals_pkt = [_load(d).get('overall', {}).get('terminal_packet', {}).get('auroc', float('nan')) for d in seeds.values()] + vals_disc = [_load(d).get('overall', {}).get('disc_nll_total', {}).get('auroc', float('nan')) for d in seeds.values()] + (m_t, s_t) = _mean_std(vals_term) + (m_p, s_p) = _mean_std(vals_pkt) + (m_d, s_d) = _mean_std(vals_disc) + (best_score, best_m, best_sd) = ('terminal_norm', m_t, s_t) + if m_p > best_m: + (best_score, best_m, best_sd) = ('terminal_packet', m_p, s_p) + if m_d > best_m: + (best_score, best_m, best_sd) = ('disc_nll_total', m_d, s_d) + beats = '✅' if best_m > existing_m else '❌' + rows.append(f'| {label} | {shafir_str} | {existing_str} | {m_t:.4f} ± {s_t:.4f} | {m_p:.4f} ± {s_p:.4f} | {m_d:.4f} ± {s_d:.4f} | `{best_score}` {best_m:.4f} ± {best_sd:.4f} | {beats} {best_m - existing_m:+.4f} |') + rows.append('') + rows.append('## Per-dataset full scoring') + rows.append('') + score_keys = ['terminal_norm', 'terminal_flow', 'terminal_packet', 'disc_nll_total', 'disc_nll_ch3', 'disc_nll_ch4', 'disc_nll_ch5', 'disc_nll_ch7'] + for (label, meta) in EXISTING_SOTA.items(): + rows.append(f'### {label}') + rows.append('') + seeds = _seeds(meta['ac_prefix']) + if not seeds: + rows.append('(not yet completed)\n') + continue + rows.append('| Score | mean ± std | seeds |') + rows.append('|---|---|---|') + for sc in score_keys: + vals = [_load(d).get('overall', {}).get(sc, {}).get('auroc', float('nan')) for d in seeds.values()] + (m, sd) = _mean_std(vals) + if m == m: + rows.append(f'| `{sc}` | {m:.4f} ± {sd:.4f} | {sorted(seeds.keys())} |') + rows.append('') + out = ROOT / 'SOTA_COMPARISON.md' + out.write_text('\n'.join(rows) + '\n') + print(f'[wrote] {out}') +if __name__ == '__main__': + main() diff --git a/artifacts/route_comparison/aggregate_results.py b/artifacts/route_comparison/aggregate_results.py new file mode 100644 index 0000000..400a432 --- /dev/null +++ b/artifacts/route_comparison/aggregate_results.py @@ -0,0 +1,94 @@ +from __future__ import annotations +import json +import re +from collections import defaultdict +from pathlib import Path +import numpy as np +ROOT = Path(__file__).resolve().parent +SEED_RE = re.compile('_seed(\\d+)$') +ROUTES = [('baseline', 'baseline_ciciot2023'), ('A: causal', 'route_a_causal_ciciot2023'), ('B: spectral', 'route_b_spectral_ciciot2023'), ('C: mixed', 'route_c_mixed_ciciot2023')] +PRIMARY_SCORES = ['terminal_norm', 'terminal_flow', 'terminal_packet', 'causal_surprisal_packet_median', 'causal_surprisal_packet_max', 'causal_surprisal_total', 'consistency_total', 'flow_consistency', 'packet_consistency', 'kappa2_speed2norm_packet_median', 'direction_drift_packet_median', 'pna_packet_median', 'disc_nll_total', 'disc_nll_ch2', 'disc_nll_ch3', 'disc_nll_ch4', 'disc_nll_ch5', 'disc_nll_ch6', 'disc_nll_ch7'] + +def _collect(prefix: str) -> dict[int, dict]: + out: dict[int, dict] = {} + for d in sorted(ROOT.glob(f'{prefix}_seed*')): + m = SEED_RE.search(d.name) + if not m: + continue + f = d / 'phase1_summary.json' + if not f.exists(): + continue + out[int(m.group(1))] = json.loads(f.read_text()) + return out + +def _mean_std(values: list[float]) -> tuple[float, float]: + arr = np.asarray([v for v in values if v == v], dtype=np.float64) + if arr.size == 0: + return (float('nan'), float('nan')) + return (float(arr.mean()), float(arr.std())) + +def main() -> None: + routes_data = {label: _collect(prefix) for (label, prefix) in ROUTES} + rows = [] + rows.append('# Route Comparison Results — CICIoT2023') + rows.append('') + rows.append('All routes trained on CICIoT2023 with the protocol locked in `PROTOCOL.md`. ') + rows.append('Numbers are AUROC over benign val (10k cap) vs all attacks (10k cap), ') + rows.append('3 seeds each. ± std across seeds.') + rows.append('') + rows.append('## Overall AUROC by score') + rows.append('') + header = '| Score | ' + ' | '.join((label for (label, _) in ROUTES)) + ' |' + sep = '|---' * (1 + len(ROUTES)) + '|' + rows.append(header) + rows.append(sep) + for score in PRIMARY_SCORES: + cells = [f'`{score}`'] + for (label, _) in ROUTES: + seeds = routes_data[label] + if not seeds: + cells.append('—') + continue + vals = [summary.get('overall', {}).get(score, {}).get('auroc', float('nan')) for summary in seeds.values()] + (mean, std) = _mean_std(vals) + cells.append(f'{mean:.4f} ± {std:.4f}' if mean == mean else '—') + rows.append('| ' + ' | '.join(cells) + ' |') + rows.append('') + rows.append('## Per-attack-class `terminal_norm` AUROC (top 12 by support)') + rows.append('') + seed_dicts = list(routes_data['baseline'].values()) + if seed_dicts: + all_classes: dict[str, float] = {} + for s in seed_dicts: + for (cls, cls_data) in s.get('per_class', {}).items(): + if cls.startswith('_'): + continue + n = cls_data.get('_n', 0.0) + all_classes[cls] = max(all_classes.get(cls, 0.0), n) + ranked = sorted(all_classes.items(), key=lambda kv: -kv[1])[:12] + header = '| Class | n | ' + ' | '.join((label for (label, _) in ROUTES)) + ' |' + sep = '|---' * (2 + len(ROUTES)) + '|' + rows.append(header) + rows.append(sep) + for (cls, n) in ranked: + cells = [cls, f'{int(n)}'] + for (label, _) in ROUTES: + seeds = routes_data[label] + if not seeds: + cells.append('—') + continue + vals = [summary.get('per_class', {}).get(cls, {}).get('terminal_norm', float('nan')) for summary in seeds.values()] + (mean, std) = _mean_std(vals) + cells.append(f'{mean:.3f} ± {std:.3f}' if mean == mean else '—') + rows.append('| ' + ' | '.join(cells) + ' |') + rows.append('') + rows.append('## Run inventory') + rows.append('') + for (label, prefix) in ROUTES: + seeds = sorted(routes_data[label].keys()) + rows.append(f"- **{label}** (`{prefix}_seed*`): seeds = {(seeds if seeds else '(none yet)')}") + out = ROOT / 'RESULTS.md' + out.write_text('\n'.join(rows) + '\n') + print(f'[wrote] {out}') +if __name__ == '__main__': + main() diff --git a/artifacts/route_comparison/aggregate_score_router.py b/artifacts/route_comparison/aggregate_score_router.py new file mode 100644 index 0000000..162c46b --- /dev/null +++ b/artifacts/route_comparison/aggregate_score_router.py @@ -0,0 +1,180 @@ +from __future__ import annotations +import json +from pathlib import Path +import numpy as np +from sklearn.covariance import LedoitWolf, OAS, GraphicalLassoCV +from sklearn.metrics import roc_auc_score +ROOT = Path(__file__).resolve().parent +CROSS_DIR = ROOT / 'cross' +WITHIN_DATASETS = ['iscxtor2016', 'cicids2017', 'cicddos2019', 'ciciot2023'] +CROSS_TARGETS = ['cicids2017', 'cicddos2019'] +SEEDS = [42, 43, 44] + +def _aggregators(val_S: np.ndarray, test_S_list: list[np.ndarray]) -> dict[str, list[np.ndarray]]: + val_S = np.nan_to_num(val_S, nan=0.0, posinf=1000000.0, neginf=-1000000.0) + test_S_list = [np.nan_to_num(t, nan=0.0, posinf=1000000.0, neginf=-1000000.0) for t in test_S_list] + mu = val_S.mean(axis=0) + sigma = val_S.std(axis=0) + 1e-09 + K = val_S.shape[1] + cov_emp = np.cov(val_S, rowvar=False) + inv_cov_plain = np.linalg.inv(cov_emp + 0.001 * np.eye(K)) + lw = LedoitWolf().fit(val_S) + inv_cov_lw = np.linalg.inv(lw.covariance_ + 1e-09 * np.eye(K)) + oas = OAS().fit(val_S) + inv_cov_oas = np.linalg.inv(oas.covariance_ + 1e-09 * np.eye(K)) + + def _max_abs_z(S): + return np.abs((S - mu) / sigma).max(axis=1) + + def _max_pos_z(S): + return ((S - mu) / sigma).max(axis=1) + + def _mahal_factory(inv_cov): + + def f(S): + d = S - mu + return np.einsum('ni,ij,nj->n', d, inv_cov, d) + return f + out: dict[str, list[np.ndarray]] = {} + for (tag, fn) in [('max_abs_z', _max_abs_z), ('max_pos_z', _max_pos_z), ('mahal_plain', _mahal_factory(inv_cov_plain)), ('mahal_lw', _mahal_factory(inv_cov_lw)), ('mahal_oas', _mahal_factory(inv_cov_oas))]: + out[tag] = [fn(t) for t in test_S_list] + return out +SCORE_SUBSETS = {'all': None, 'terminal3': ['terminal_norm', 'terminal_flow', 'terminal_packet'], 'disc7': ['disc_nll_total', 'disc_nll_ch2', 'disc_nll_ch3', 'disc_nll_ch4', 'disc_nll_ch5', 'disc_nll_ch6', 'disc_nll_ch7']} + +def _evaluate(npz: Path, val_prefix: str, atk_prefix: str) -> dict: + z = np.load(npz, allow_pickle=True) + all_keys = sorted([k.replace(val_prefix, '') for k in z.files if k.startswith(val_prefix) and (not k.endswith('labels'))]) + out: dict = {'n_val': None, 'n_atk': None} + for (subset_name, subset_keys) in SCORE_SUBSETS.items(): + if subset_keys is None: + keys = all_keys + else: + keys = [k for k in subset_keys if k in all_keys] + if len(keys) < 2: + continue + val_S = np.stack([z[f'{val_prefix}{k}'] for k in keys], axis=1) + atk_S = np.stack([z[f'{atk_prefix}{k}'] for k in keys], axis=1) + (n_val, n_atk) = (val_S.shape[0], atk_S.shape[0]) + out['n_val'] = n_val + out['n_atk'] = n_atk + y = np.r_[np.zeros(n_val), np.ones(n_atk)] + aggs = _aggregators(val_S, [val_S, atk_S]) + for (tag, (v_agg, a_agg)) in aggs.items(): + s = np.r_[v_agg, a_agg] + s = np.nan_to_num(s, nan=0.0, posinf=1000000000000.0, neginf=-1000000000000.0) + try: + auc = float(roc_auc_score(y, s)) + except ValueError: + auc = float('nan') + out[f'auc_{tag}_{subset_name}'] = auc + out['auc_max_abs_z'] = out.get('auc_max_abs_z_all') + out['auc_max_pos_z'] = out.get('auc_max_pos_z_all') + out['auc_mahal_plain'] = out.get('auc_mahal_plain_all') + out['auc_mahal_lw'] = out.get('auc_mahal_lw_all') + out['auc_mahal_oas'] = out.get('auc_mahal_oas_all') + val_S = np.stack([z[f'{val_prefix}{k}'] for k in all_keys], axis=1) + atk_S = np.stack([z[f'{atk_prefix}{k}'] for k in all_keys], axis=1) + val_S = np.nan_to_num(val_S, nan=0.0, posinf=1000000000000.0, neginf=-1000000000000.0) + atk_S = np.nan_to_num(atk_S, nan=0.0, posinf=1000000000000.0, neginf=-1000000000000.0) + y = np.r_[np.zeros(val_S.shape[0]), np.ones(atk_S.shape[0])] + per_score = {} + for (i, k) in enumerate(all_keys): + s = np.r_[val_S[:, i], atk_S[:, i]] + s = np.nan_to_num(s, nan=0.0, posinf=1000000000000.0, neginf=-1000000000000.0) + a1 = roc_auc_score(y, s) + per_score[k] = max(a1, 1 - a1) + best_score = max(per_score, key=per_score.get) + out['auc_best_fixed'] = per_score[best_score] + out['best_fixed_name'] = best_score + out['auc_term_norm'] = per_score.get('terminal_norm', float('nan')) + out['auc_term_pkt'] = per_score.get('terminal_packet', float('nan')) + out['auc_disc_total'] = per_score.get('disc_nll_total', float('nan')) + return out + +def _mean_std(vs: list[float]) -> tuple[float, float]: + arr = np.asarray([v for v in vs if v == v], dtype=np.float64) + if arr.size == 0: + return (float('nan'), float('nan')) + return (float(arr.mean()), float(arr.std())) + +def main() -> None: + rows: list[str] = [] + rows.append('# Score-vector auto-selection: max-of-|z| / Mahalanobis vs fixed scores') + rows.append('') + rows.append('Aggregators are fit on **benign val only** (no attack labels). All numbers') + rows.append('are 3-seed mean ± std on A+C combo (Mixed_CFM + causal-packet attention).') + rows.append('') + rows.append('Note on fairness: `auc_best_fixed` is selection-biased (picks per-dataset best') + rows.append('score post-hoc on test set). `max_abs_z` and `mahalanobis` are NOT — they only') + rows.append('use benign val to fit aggregator parameters.') + rows.append('') + rows.append("## Within-dataset(A+C combo on each dataset's own benign/attack)") + rows.append('') + rows.append('| Dataset | term_norm | best fixed | max-\\|z\\| (all) | mahal-OAS (all) | **mahal-OAS (term3)** | **mahal-OAS (disc7)** |') + rows.append('|---|---|---|---|---|---|---|') + for ds in WITHIN_DATASETS: + rows_per_seed: list[dict] = [] + for s in SEEDS: + md = ROOT / f'route_ac_combo_{ds}_seed{s}' + npz = md / 'phase1_scores.npz' + if not npz.exists(): + continue + rows_per_seed.append(_evaluate(npz, 'val_', 'atk_')) + if not rows_per_seed: + rows.append(f'| {ds} | (no data) | | | | | |') + continue + + def col(field): + (m, sd) = _mean_std([r[field] for r in rows_per_seed]) + return f'{m:.4f} ± {sd:.4f}' + rows.append(f"| {ds} | {col('auc_term_norm')} | {col('auc_best_fixed')} | {col('auc_max_abs_z_all')} | {col('auc_mahal_oas_all')} | **{col('auc_mahal_oas_terminal3')}** | **{col('auc_mahal_oas_disc7')}** |") + rows.append('') + rows.append('## Cross-dataset(A+C combo trained on CICIoT2023 → eval on target)') + rows.append('') + rows.append('| Target | term_norm | best fixed | max-\\|z\\| (all) | mahal-OAS (all) | **mahal-OAS (term3)** | **mahal-OAS (disc7)** |') + rows.append('|---|---|---|---|---|---|---|') + for tgt in CROSS_TARGETS: + rows_per_seed: list[dict] = [] + for s in SEEDS: + npz = CROSS_DIR / f'route_ac_combo_seed{s}_to_{tgt}.npz' + if not npz.exists(): + continue + rows_per_seed.append(_evaluate(npz, 'b_', 'a_')) + if not rows_per_seed: + rows.append(f'| {tgt} | (no data) | | | | | |') + continue + + def col(field): + (m, sd) = _mean_std([r[field] for r in rows_per_seed]) + return f'{m:.4f} ± {sd:.4f}' + rows.append(f"| {tgt} | {col('auc_term_norm')} | {col('auc_best_fixed')} | {col('auc_max_abs_z_all')} | {col('auc_mahal_oas_all')} | **{col('auc_mahal_oas_terminal3')}** | **{col('auc_mahal_oas_disc7')}** |") + rows.append('') + rows.append('## Best-fixed-score winner per setup') + rows.append('') + rows.append('| Setup | seed42 | seed43 | seed44 |') + rows.append('|---|---|---|---|') + for ds in WITHIN_DATASETS: + cells = [f'within {ds}'] + for s in SEEDS: + npz = ROOT / f'route_ac_combo_{ds}_seed{s}/phase1_scores.npz' + if not npz.exists(): + cells.append('—') + continue + r = _evaluate(npz, 'val_', 'atk_') + cells.append(f"{r['best_fixed_name']} ({r['auc_best_fixed']:.4f})") + rows.append('| ' + ' | '.join(cells) + ' |') + for tgt in CROSS_TARGETS: + cells = [f'cross→{tgt}'] + for s in SEEDS: + npz = CROSS_DIR / f'route_ac_combo_seed{s}_to_{tgt}.npz' + if not npz.exists(): + cells.append('—') + continue + r = _evaluate(npz, 'b_', 'a_') + cells.append(f"{r['best_fixed_name']} ({r['auc_best_fixed']:.4f})") + rows.append('| ' + ' | '.join(cells) + ' |') + out = ROOT / 'SCORE_ROUTER.md' + out.write_text('\n'.join(rows) + '\n') + print(f'[wrote] {out}') +if __name__ == '__main__': + main() diff --git a/artifacts/route_comparison/aggregate_v2.py b/artifacts/route_comparison/aggregate_v2.py new file mode 100644 index 0000000..a1447e5 --- /dev/null +++ b/artifacts/route_comparison/aggregate_v2.py @@ -0,0 +1,182 @@ +from __future__ import annotations +import json +import re +from pathlib import Path +import numpy as np +from sklearn.metrics import roc_auc_score +ROOT = Path(__file__).resolve().parent +SEED_RE = re.compile('_seed(\\d+)$') +ROUTES = [('baseline', 'baseline_ciciot2023'), ('A: causal', 'route_a_causal_ciciot2023'), ('B: spectral', 'route_b_spectral_ciciot2023'), ('C: mixed', 'route_c_mixed_ciciot2023'), ('A+C combo', 'route_ac_combo_ciciot2023')] + +def _seeds(prefix: str) -> dict[int, Path]: + out = {} + for d in sorted(ROOT.glob(f'{prefix}_seed*')): + m = SEED_RE.search(d.name) + if m and (d / 'phase1_summary.json').exists(): + out[int(m.group(1))] = d + return out + +def _load_summary(d: Path) -> dict: + return json.loads((d / 'phase1_summary.json').read_text()) + +def _ensemble_sweep(d: Path) -> dict[float, float] | None: + f = d / 'phase1_scores.npz' + if not f.exists(): + return None + z = np.load(f, allow_pickle=True) + keys = set(z.files) + if 'val_terminal_norm' not in keys or 'val_disc_nll_total' not in keys: + return None + v_tn = z['val_terminal_norm'] + a_tn = z['atk_terminal_norm'] + v_dn = z['val_disc_nll_total'] + a_dn = z['atk_disc_nll_total'] + + def zsc(v, a): + (mu, sd) = (v.mean(), v.std() + 1e-09) + return ((v - mu) / sd, (a - mu) / sd) + (v_tn_z, a_tn_z) = zsc(v_tn, a_tn) + (v_dn_z, a_dn_z) = zsc(v_dn, a_dn) + out: dict[float, float] = {} + for alpha in (0.0, 0.25, 0.5, 0.7, 0.8, 0.9, 1.0): + s_v = alpha * v_tn_z + (1.0 - alpha) * v_dn_z + s_a = alpha * a_tn_z + (1.0 - alpha) * a_dn_z + y = np.r_[np.zeros(len(s_v)), np.ones(len(s_a))] + s = np.r_[s_v, s_a] + out[alpha] = float(roc_auc_score(y, s)) + return out + +def _ensemble_score(d: Path) -> tuple[float, float] | None: + sweep = _ensemble_sweep(d) + if sweep is None: + return None + best_alpha = max(sweep, key=sweep.get) + return (sweep[best_alpha], best_alpha) + +def _mean_std(vals: list[float]) -> tuple[float, float]: + arr = np.asarray([v for v in vals if v == v], dtype=np.float64) + if arr.size == 0: + return (float('nan'), float('nan')) + return (float(arr.mean()), float(arr.std())) + +def main() -> None: + routes_data: dict[str, dict[int, dict]] = {} + routes_dirs: dict[str, dict[int, Path]] = {} + for (label, prefix) in ROUTES: + seeds = _seeds(prefix) + routes_dirs[label] = seeds + routes_data[label] = {s: _load_summary(d) for (s, d) in seeds.items()} + rows: list[str] = [] + rows.append('# Route Comparison Results — CICIoT2023 (multi-seed)') + rows.append('') + rows.append('Phase1 eval: AUROC over benign val (5k cap) vs all attacks (10k cap), 3 seeds each.') + rows.append('') + rows.append("## Each route's best AUROC (overall)") + rows.append('') + rows.append('| Route | Best score | AUROC | Δ vs baseline-best |') + rows.append('|---|---|---|---|') + baseline_best = None + for (label, _) in ROUTES: + seeds = routes_data[label] + if not seeds: + rows.append(f'| {label} | — | — | — |') + continue + all_scores: dict[str, list[float]] = {} + for s in seeds.values(): + for (k, v) in s.get('overall', {}).items(): + all_scores.setdefault(k, []).append(v.get('auroc', float('nan'))) + score_means = {k: _mean_std(v)[0] for (k, v) in all_scores.items()} + score_means = {k: v for (k, v) in score_means.items() if v == v} + if not score_means: + rows.append(f'| {label} | — | — | — |') + continue + best_score = max(score_means, key=score_means.get) + best_val = score_means[best_score] + if label == 'baseline': + baseline_best = best_val + delta_str = '—' + else: + delta_str = f'{best_val - baseline_best:+.4f}' if baseline_best else '—' + std = _mean_std(all_scores[best_score])[1] + rows.append(f'| {label} | `{best_score}` | {best_val:.4f} ± {std:.4f} | {delta_str} |') + rows.append('') + rows.append('## Primary score: `terminal_norm`') + rows.append('') + rows.append('| Route | mean ± std | seeds |') + rows.append('|---|---|---|') + for (label, _) in ROUTES: + seeds = routes_data[label] + if not seeds: + rows.append(f'| {label} | — | — |') + continue + vals = [s['overall'].get('terminal_norm', {}).get('auroc', float('nan')) for s in seeds.values()] + (m, sd) = _mean_std(vals) + rows.append(f'| {label} | {m:.4f} ± {sd:.4f} | {sorted(seeds.keys())} |') + rows.append('') + rows.append('## Route-specific signature scores (mean ± std, 3 seeds)') + rows.append('') + score_groups = [('Route A signature (consistency family)', ['flow_consistency', 'packet_consistency', 'consistency_total', 'causal_surprisal_total', 'causal_surprisal_packet_median']), ('Route B signature (curvature/dynamics)', ['kappa2_speed2norm_packet_median', 'direction_drift_packet_median', 'pna_packet_median', 'curvature_packet']), ('Route C signature (discrete NLL)', ['disc_nll_total', 'disc_nll_ch3', 'disc_nll_ch4', 'disc_nll_ch5', 'disc_nll_ch7'])] + for (grp_name, scores) in score_groups: + rows.append(f'### {grp_name}') + rows.append('') + rows.append('| Score | ' + ' | '.join((label for (label, _) in ROUTES)) + ' |') + rows.append('|---' * (1 + len(ROUTES)) + '|') + for sc in scores: + cells = [f'`{sc}`'] + for (label, _) in ROUTES: + seeds = routes_data[label] + vals = [s['overall'].get(sc, {}).get('auroc', float('nan')) for s in seeds.values()] + (m, sd) = _mean_std(vals) + cells.append(f'{m:.4f} ± {sd:.4f}' if m == m else '—') + rows.append('| ' + ' | '.join(cells) + ' |') + rows.append('') + rows.append('## Route C ensemble: α·terminal_norm + (1−α)·disc_nll_total (z-scored)') + rows.append('') + c_dirs = routes_dirs.get('C: mixed', {}) + if c_dirs: + alphas = (0.0, 0.25, 0.5, 0.7, 0.8, 0.9, 1.0) + rows.append('| α | ' + ' | '.join((f'seed{s}' for s in sorted(c_dirs.keys()))) + ' | mean ± std |') + rows.append('|---' * (2 + len(c_dirs)) + '|') + per_alpha: dict[float, list[float]] = {a: [] for a in alphas} + per_seed_sweeps = {s: _ensemble_sweep(d) or {} for (s, d) in c_dirs.items()} + for a in alphas: + cells = [f'{a:.2f}'] + vals = [] + for s in sorted(c_dirs.keys()): + v = per_seed_sweeps[s].get(a, float('nan')) + cells.append(f'{v:.4f}') + vals.append(v) + (m, sd) = _mean_std(vals) + cells.append(f'**{m:.4f} ± {sd:.4f}**') + rows.append('| ' + ' | '.join(cells) + ' |') + rows.append('') + rows.append('(α=1.0 = terminal_norm only; α=0.0 = disc_nll only.)') + rows.append('') + rows.append('## Per-attack-class AUROC (top 12, terminal_norm)') + rows.append('') + if routes_data['baseline']: + any_summary = next(iter(routes_data['baseline'].values())) + classes = sorted([(c, d.get('_n', 0)) for (c, d) in any_summary.get('per_class', {}).items() if not c.startswith('_')], key=lambda kv: -kv[1])[:12] + header = '| Class | n | ' + ' | '.join((label for (label, _) in ROUTES)) + ' |' + sep = '|---' * (2 + len(ROUTES)) + '|' + rows.append(header) + rows.append(sep) + for (cls, n) in classes: + cells = [cls, f'{int(n)}'] + for (label, _) in ROUTES: + seeds = routes_data[label] + vals = [s.get('per_class', {}).get(cls, {}).get('terminal_norm', float('nan')) for s in seeds.values()] + (m, sd) = _mean_std(vals) + cells.append(f'{m:.3f}±{sd:.3f}' if m == m else '—') + rows.append('| ' + ' | '.join(cells) + ' |') + rows.append('') + rows.append('## Run inventory') + rows.append('') + for (label, prefix) in ROUTES: + seeds = sorted(routes_data[label].keys()) + rows.append(f"- **{label}** (`{prefix}_seed*`): seeds = {(seeds if seeds else '(none yet)')}") + out = ROOT / 'RESULTS.md' + out.write_text('\n'.join(rows) + '\n') + print(f'[wrote] {out}') +if __name__ == '__main__': + main() diff --git a/artifacts/route_comparison/baseline_seed42.log b/artifacts/route_comparison/baseline_seed42.log new file mode 100644 index 0000000..5dfc2cb --- /dev/null +++ b/artifacts/route_comparison/baseline_seed42.log @@ -0,0 +1,61 @@ +Device: cuda +[seed] model=42 data=42 +[data] flows=/home/chy/mambafortrafficmodeling/datasets/ciciot2023/processed/full_store/flows.parquet packets_source=/home/chy/mambafortrafficmodeling/datasets/ciciot2023/processed/full_store +[data] using external flow features D=20 +[data] rows total=8,193,621 keep len>=2: 3,797,530 +[data] benign=97,045 attack=20,000 -> train=77,636 val=10,000 +[data] T=64 packet_D=9 flow_D=20 train=77,636 val=10,000 attack=20,000 +[data] using 10,000 benign training flows +[model] params=1,226,261 token_dim=21 seq_len=65 sigma=0.1 use_ot=True reference_mode=None +[loss] λ_flow=0.3 λ_packet=0.3 packet_mask_ratio=0.5 +[epoch 1/50 ] (2.8s) loss=2.2385 aux_flow=2.6910 aux_pkt=1.0210 +[epoch 2/50 ] (2.1s) loss=1.8185 aux_flow=2.2098 aux_pkt=0.9856 +[epoch 3/50 ] (2.2s) loss=1.5694 aux_flow=1.9865 aux_pkt=0.9600 +[epoch 4/50 ] (2.9s) loss=1.4088 aux_flow=1.8584 aux_pkt=0.9427 +[epoch 5/50 ] (6.0s) loss=1.2803 aux_flow=1.7153 aux_pkt=0.9398 +[epoch 6/50 ] (6.0s) loss=1.1939 aux_flow=1.6313 aux_pkt=0.9344 +[epoch 7/50 ] (6.0s) loss=1.1530 aux_flow=1.5987 aux_pkt=0.9302 +[epoch 8/50 ] (6.0s) loss=1.1123 aux_flow=1.5272 aux_pkt=0.9287 +[epoch 9/50 ] (6.1s) loss=1.0785 aux_flow=1.4726 aux_pkt=0.9311 +[epoch 10/50 ] (71.6s) loss=1.0583 auroc_terminal=0.953 aux_flow=1.4576 aux_pkt=0.9233 +[epoch 11/50 ] (3.1s) loss=1.0317 aux_flow=1.4195 aux_pkt=0.9233 +[epoch 12/50 ] (3.0s) loss=1.0145 aux_flow=1.3876 aux_pkt=0.9228 +[epoch 13/50 ] (3.4s) loss=1.0529 aux_flow=1.4878 aux_pkt=0.9247 +[epoch 14/50 ] (5.9s) loss=0.9897 aux_flow=1.3594 aux_pkt=0.9167 +[epoch 15/50 ] (6.0s) loss=0.9755 aux_flow=1.3333 aux_pkt=0.9196 +[epoch 16/50 ] (6.1s) loss=0.9706 aux_flow=1.3205 aux_pkt=0.9151 +[epoch 17/50 ] (5.9s) loss=0.9741 aux_flow=1.3408 aux_pkt=0.9169 +[epoch 18/50 ] (6.0s) loss=0.9865 aux_flow=1.3802 aux_pkt=0.9176 +[epoch 19/50 ] (6.0s) loss=0.9678 aux_flow=1.3466 aux_pkt=0.9225 +[epoch 20/50 ] (68.4s) loss=0.9453 auroc_terminal=0.960 aux_flow=1.2853 aux_pkt=0.9216 +[epoch 21/50 ] (3.1s) loss=0.9450 aux_flow=1.3088 aux_pkt=0.9112 +[epoch 22/50 ] (3.0s) loss=0.9600 aux_flow=1.3598 aux_pkt=0.9128 +[epoch 23/50 ] (4.7s) loss=0.9320 aux_flow=1.2747 aux_pkt=0.9135 +[epoch 24/50 ] (6.0s) loss=0.9258 aux_flow=1.2705 aux_pkt=0.9177 +[epoch 25/50 ] (6.0s) loss=0.9202 aux_flow=1.2642 aux_pkt=0.9153 +[epoch 26/50 ] (6.0s) loss=0.9248 aux_flow=1.2816 aux_pkt=0.9132 +[epoch 27/50 ] (6.0s) loss=0.9080 aux_flow=1.2399 aux_pkt=0.9179 +[epoch 28/50 ] (6.1s) loss=0.9162 aux_flow=1.2700 aux_pkt=0.9129 +[epoch 29/50 ] (5.9s) loss=0.9037 aux_flow=1.2479 aux_pkt=0.9110 +[epoch 30/50 ] (67.0s) loss=0.9134 auroc_terminal=0.959 aux_flow=1.2686 aux_pkt=0.9155 +[epoch 31/50 ] (3.0s) loss=0.9049 aux_flow=1.2512 aux_pkt=0.9138 +[epoch 32/50 ] (3.8s) loss=0.9110 aux_flow=1.2720 aux_pkt=0.9133 +[epoch 33/50 ] (4.6s) loss=0.9011 aux_flow=1.2387 aux_pkt=0.9169 +[epoch 34/50 ] (6.0s) loss=0.9061 aux_flow=1.2695 aux_pkt=0.9149 +[epoch 35/50 ] (6.1s) loss=0.8893 aux_flow=1.2278 aux_pkt=0.9084 +[epoch 36/50 ] (6.0s) loss=0.8844 aux_flow=1.2182 aux_pkt=0.9060 +[epoch 37/50 ] (6.0s) loss=0.8820 aux_flow=1.2183 aux_pkt=0.9076 +[epoch 38/50 ] (6.0s) loss=0.8884 aux_flow=1.2248 aux_pkt=0.9118 +[epoch 39/50 ] (6.0s) loss=0.8901 aux_flow=1.2342 aux_pkt=0.9173 +[epoch 40/50 ] (66.1s) loss=0.8866 auroc_terminal=0.963 aux_flow=1.2305 aux_pkt=0.9071 +[epoch 41/50 ] (3.2s) loss=0.8884 aux_flow=1.2274 aux_pkt=0.9106 +[epoch 42/50 ] (4.3s) loss=0.8857 aux_flow=1.2260 aux_pkt=0.9116 +[epoch 43/50 ] (4.5s) loss=0.8710 aux_flow=1.1892 aux_pkt=0.9070 +[epoch 44/50 ] (6.0s) loss=0.8824 aux_flow=1.2141 aux_pkt=0.9124 +[epoch 45/50 ] (6.0s) loss=0.8768 aux_flow=1.2025 aux_pkt=0.9081 +[epoch 46/50 ] (6.0s) loss=0.8775 aux_flow=1.1961 aux_pkt=0.9122 +[epoch 47/50 ] (6.0s) loss=0.8779 aux_flow=1.2098 aux_pkt=0.9116 +[epoch 48/50 ] (6.0s) loss=0.8735 aux_flow=1.1931 aux_pkt=0.9098 +[epoch 49/50 ] (6.0s) loss=0.8781 aux_flow=1.2054 aux_pkt=0.9092 +[epoch 50/50 ] (65.3s) loss=0.8739 auroc_terminal=0.964 aux_flow=1.1932 aux_pkt=0.9092 +[saved] /home/chy/mambafortrafficmodeling/artifacts/route_comparison/baseline_ciciot2023_seed42/model.pt diff --git a/artifacts/route_comparison/baseline_seed43.log b/artifacts/route_comparison/baseline_seed43.log new file mode 100644 index 0000000..644b22f --- /dev/null +++ b/artifacts/route_comparison/baseline_seed43.log @@ -0,0 +1,61 @@ +Device: cuda +[seed] model=43 data=43 +[data] flows=/home/chy/mambafortrafficmodeling/datasets/ciciot2023/processed/full_store/flows.parquet packets_source=/home/chy/mambafortrafficmodeling/datasets/ciciot2023/processed/full_store +[data] using external flow features D=20 +[data] rows total=8,193,621 keep len>=2: 3,797,530 +[data] benign=97,045 attack=20,000 -> train=77,636 val=10,000 +[data] T=64 packet_D=9 flow_D=20 train=77,636 val=10,000 attack=20,000 +[data] using 10,000 benign training flows +[model] params=1,226,261 token_dim=21 seq_len=65 sigma=0.1 use_ot=True reference_mode=None +[loss] λ_flow=0.3 λ_packet=0.3 packet_mask_ratio=0.5 +[epoch 1/50 ] (6.5s) loss=2.2950 aux_flow=2.8636 aux_pkt=1.0202 +[epoch 2/50 ] (6.0s) loss=1.8686 aux_flow=2.3480 aux_pkt=0.9926 +[epoch 3/50 ] (6.0s) loss=1.5990 aux_flow=2.0891 aux_pkt=0.9587 +[epoch 4/50 ] (5.9s) loss=1.4314 aux_flow=1.9244 aux_pkt=0.9541 +[epoch 5/50 ] (6.0s) loss=1.3124 aux_flow=1.7769 aux_pkt=0.9525 +[epoch 6/50 ] (6.0s) loss=1.2334 aux_flow=1.7219 aux_pkt=0.9403 +[epoch 7/50 ] (4.9s) loss=1.1754 aux_flow=1.6386 aux_pkt=0.9388 +[epoch 8/50 ] (4.3s) loss=1.1245 aux_flow=1.5572 aux_pkt=0.9406 +[epoch 9/50 ] (4.3s) loss=1.0910 aux_flow=1.5038 aux_pkt=0.9323 +[epoch 10/50 ] (61.6s) loss=1.0686 auroc_terminal=0.943 aux_flow=1.4825 aux_pkt=0.9287 +[epoch 11/50 ] (5.5s) loss=1.0531 aux_flow=1.4595 aux_pkt=0.9286 +[epoch 12/50 ] (6.0s) loss=1.0392 aux_flow=1.4418 aux_pkt=0.9294 +[epoch 13/50 ] (5.9s) loss=1.0203 aux_flow=1.4019 aux_pkt=0.9300 +[epoch 14/50 ] (6.0s) loss=1.0114 aux_flow=1.3792 aux_pkt=0.9340 +[epoch 15/50 ] (6.0s) loss=1.0096 aux_flow=1.3923 aux_pkt=0.9287 +[epoch 16/50 ] (6.0s) loss=1.0078 aux_flow=1.4262 aux_pkt=0.9241 +[epoch 17/50 ] (6.0s) loss=0.9922 aux_flow=1.3857 aux_pkt=0.9235 +[epoch 18/50 ] (4.8s) loss=0.9932 aux_flow=1.3899 aux_pkt=0.9210 +[epoch 19/50 ] (4.5s) loss=0.9798 aux_flow=1.3516 aux_pkt=0.9195 +[epoch 20/50 ] (61.0s) loss=0.9674 auroc_terminal=0.953 aux_flow=1.3313 aux_pkt=0.9260 +[epoch 21/50 ] (4.7s) loss=0.9683 aux_flow=1.3487 aux_pkt=0.9208 +[epoch 22/50 ] (6.0s) loss=0.9630 aux_flow=1.3441 aux_pkt=0.9214 +[epoch 23/50 ] (6.0s) loss=0.9523 aux_flow=1.3144 aux_pkt=0.9227 +[epoch 24/50 ] (6.0s) loss=0.9441 aux_flow=1.2960 aux_pkt=0.9210 +[epoch 25/50 ] (6.0s) loss=0.9389 aux_flow=1.2984 aux_pkt=0.9188 +[epoch 26/50 ] (6.0s) loss=0.9517 aux_flow=1.3492 aux_pkt=0.9192 +[epoch 27/50 ] (5.9s) loss=0.9351 aux_flow=1.2965 aux_pkt=0.9173 +[epoch 28/50 ] (6.0s) loss=0.9299 aux_flow=1.2959 aux_pkt=0.9192 +[epoch 29/50 ] (4.4s) loss=0.9205 aux_flow=1.2822 aux_pkt=0.9168 +[epoch 30/50 ] (61.0s) loss=0.9184 auroc_terminal=0.954 aux_flow=1.2632 aux_pkt=0.9191 +[epoch 31/50 ] (4.3s) loss=0.9260 aux_flow=1.2890 aux_pkt=0.9184 +[epoch 32/50 ] (5.3s) loss=0.9211 aux_flow=1.2825 aux_pkt=0.9203 +[epoch 33/50 ] (5.9s) loss=0.9169 aux_flow=1.2665 aux_pkt=0.9199 +[epoch 34/50 ] (6.0s) loss=0.9252 aux_flow=1.2949 aux_pkt=0.9254 +[epoch 35/50 ] (6.0s) loss=0.9108 aux_flow=1.2644 aux_pkt=0.9169 +[epoch 36/50 ] (5.9s) loss=0.9040 aux_flow=1.2475 aux_pkt=0.9174 +[epoch 37/50 ] (6.0s) loss=0.9060 aux_flow=1.2480 aux_pkt=0.9182 +[epoch 38/50 ] (6.0s) loss=0.9034 aux_flow=1.2471 aux_pkt=0.9143 +[epoch 39/50 ] (5.4s) loss=0.9016 aux_flow=1.2337 aux_pkt=0.9205 +[epoch 40/50 ] (61.0s) loss=0.8977 auroc_terminal=0.958 aux_flow=1.2398 aux_pkt=0.9147 +[epoch 41/50 ] (4.3s) loss=0.8944 aux_flow=1.2394 aux_pkt=0.9147 +[epoch 42/50 ] (4.3s) loss=0.8916 aux_flow=1.2291 aux_pkt=0.9174 +[epoch 43/50 ] (5.7s) loss=0.8987 aux_flow=1.2367 aux_pkt=0.9171 +[epoch 44/50 ] (6.0s) loss=0.8854 aux_flow=1.2158 aux_pkt=0.9101 +[epoch 45/50 ] (6.0s) loss=0.8927 aux_flow=1.2282 aux_pkt=0.9127 +[epoch 46/50 ] (6.0s) loss=0.8910 aux_flow=1.2225 aux_pkt=0.9180 +[epoch 47/50 ] (6.0s) loss=0.8925 aux_flow=1.2352 aux_pkt=0.9123 +[epoch 48/50 ] (5.9s) loss=0.8876 aux_flow=1.2166 aux_pkt=0.9132 +[epoch 49/50 ] (6.0s) loss=0.8876 aux_flow=1.2258 aux_pkt=0.9081 +[epoch 50/50 ] (61.1s) loss=0.8882 auroc_terminal=0.960 aux_flow=1.2200 aux_pkt=0.9186 +[saved] /home/chy/mambafortrafficmodeling/artifacts/route_comparison/baseline_ciciot2023_seed43/model.pt diff --git a/artifacts/route_comparison/baseline_seed44.log b/artifacts/route_comparison/baseline_seed44.log new file mode 100644 index 0000000..3b47a23 --- /dev/null +++ b/artifacts/route_comparison/baseline_seed44.log @@ -0,0 +1,61 @@ +Device: cuda +[seed] model=44 data=44 +[data] flows=/home/chy/mambafortrafficmodeling/datasets/ciciot2023/processed/full_store/flows.parquet packets_source=/home/chy/mambafortrafficmodeling/datasets/ciciot2023/processed/full_store +[data] using external flow features D=20 +[data] rows total=8,193,621 keep len>=2: 3,797,530 +[data] benign=97,045 attack=20,000 -> train=77,636 val=10,000 +[data] T=64 packet_D=9 flow_D=20 train=77,636 val=10,000 attack=20,000 +[data] using 10,000 benign training flows +[model] params=1,226,261 token_dim=21 seq_len=65 sigma=0.1 use_ot=True reference_mode=None +[loss] λ_flow=0.3 λ_packet=0.3 packet_mask_ratio=0.5 +[epoch 1/50 ] (4.6s) loss=2.3754 aux_flow=3.0671 aux_pkt=1.0371 +[epoch 2/50 ] (3.9s) loss=1.9514 aux_flow=2.5465 aux_pkt=0.9975 +[epoch 3/50 ] (4.1s) loss=1.6782 aux_flow=2.2742 aux_pkt=0.9602 +[epoch 4/50 ] (4.0s) loss=1.5021 aux_flow=2.0847 aux_pkt=0.9582 +[epoch 5/50 ] (4.1s) loss=1.3728 aux_flow=1.9244 aux_pkt=0.9510 +[epoch 6/50 ] (4.0s) loss=1.2696 aux_flow=1.8004 aux_pkt=0.9442 +[epoch 7/50 ] (4.0s) loss=1.2203 aux_flow=1.7529 aux_pkt=0.9420 +[epoch 8/50 ] (3.9s) loss=1.1492 aux_flow=1.6252 aux_pkt=0.9396 +[epoch 9/50 ] (4.0s) loss=1.1424 aux_flow=1.6307 aux_pkt=0.9396 +[epoch 10/50 ] (39.7s) loss=1.0963 auroc_terminal=0.952 aux_flow=1.5450 aux_pkt=0.9282 +[epoch 11/50 ] (3.8s) loss=1.0864 aux_flow=1.5387 aux_pkt=0.9311 +[epoch 12/50 ] (3.9s) loss=1.0739 aux_flow=1.5157 aux_pkt=0.9392 +[epoch 13/50 ] (4.0s) loss=1.0486 aux_flow=1.4622 aux_pkt=0.9310 +[epoch 14/50 ] (3.9s) loss=1.0427 aux_flow=1.4598 aux_pkt=0.9299 +[epoch 15/50 ] (3.9s) loss=1.0284 aux_flow=1.4427 aux_pkt=0.9324 +[epoch 16/50 ] (3.9s) loss=1.0063 aux_flow=1.3971 aux_pkt=0.9304 +[epoch 17/50 ] (3.9s) loss=1.0274 aux_flow=1.4575 aux_pkt=0.9361 +[epoch 18/50 ] (4.0s) loss=0.9907 aux_flow=1.3769 aux_pkt=0.9310 +[epoch 19/50 ] (3.9s) loss=0.9896 aux_flow=1.3886 aux_pkt=0.9214 +[epoch 20/50 ] (39.5s) loss=0.9752 auroc_terminal=0.950 aux_flow=1.3546 aux_pkt=0.9228 +[epoch 21/50 ] (3.9s) loss=0.9695 aux_flow=1.3517 aux_pkt=0.9208 +[epoch 22/50 ] (4.1s) loss=0.9612 aux_flow=1.3437 aux_pkt=0.9179 +[epoch 23/50 ] (3.9s) loss=0.9662 aux_flow=1.3626 aux_pkt=0.9198 +[epoch 24/50 ] (3.9s) loss=0.9572 aux_flow=1.3308 aux_pkt=0.9202 +[epoch 25/50 ] (3.9s) loss=0.9416 aux_flow=1.3112 aux_pkt=0.9232 +[epoch 26/50 ] (3.9s) loss=0.9377 aux_flow=1.2940 aux_pkt=0.9218 +[epoch 27/50 ] (3.9s) loss=0.9386 aux_flow=1.2995 aux_pkt=0.9210 +[epoch 28/50 ] (3.9s) loss=0.9421 aux_flow=1.3222 aux_pkt=0.9225 +[epoch 29/50 ] (3.9s) loss=0.9332 aux_flow=1.2946 aux_pkt=0.9210 +[epoch 30/50 ] (39.5s) loss=0.9247 auroc_terminal=0.955 aux_flow=1.2880 aux_pkt=0.9146 +[epoch 31/50 ] (3.9s) loss=0.9301 aux_flow=1.2971 aux_pkt=0.9214 +[epoch 32/50 ] (4.0s) loss=0.9165 aux_flow=1.2809 aux_pkt=0.9139 +[epoch 33/50 ] (4.0s) loss=0.9202 aux_flow=1.2862 aux_pkt=0.9185 +[epoch 34/50 ] (4.1s) loss=0.9154 aux_flow=1.2710 aux_pkt=0.9152 +[epoch 35/50 ] (4.0s) loss=0.9058 aux_flow=1.2471 aux_pkt=0.9188 +[epoch 36/50 ] (4.0s) loss=0.9158 aux_flow=1.2780 aux_pkt=0.9210 +[epoch 37/50 ] (4.1s) loss=0.9052 aux_flow=1.2524 aux_pkt=0.9231 +[epoch 38/50 ] (4.0s) loss=0.9068 aux_flow=1.2526 aux_pkt=0.9238 +[epoch 39/50 ] (4.0s) loss=0.9058 aux_flow=1.2551 aux_pkt=0.9143 +[epoch 40/50 ] (39.8s) loss=0.9015 auroc_terminal=0.956 aux_flow=1.2396 aux_pkt=0.9243 +[epoch 41/50 ] (3.4s) loss=0.8986 aux_flow=1.2317 aux_pkt=0.9171 +[epoch 42/50 ] (4.0s) loss=0.8967 aux_flow=1.2348 aux_pkt=0.9194 +[epoch 43/50 ] (3.9s) loss=0.8985 aux_flow=1.2380 aux_pkt=0.9183 +[epoch 44/50 ] (3.9s) loss=0.8979 aux_flow=1.2323 aux_pkt=0.9215 +[epoch 45/50 ] (4.0s) loss=0.9030 aux_flow=1.2545 aux_pkt=0.9187 +[epoch 46/50 ] (3.9s) loss=0.9023 aux_flow=1.2471 aux_pkt=0.9242 +[epoch 47/50 ] (3.9s) loss=0.8925 aux_flow=1.2208 aux_pkt=0.9262 +[epoch 48/50 ] (4.0s) loss=0.8903 aux_flow=1.2234 aux_pkt=0.9141 +[epoch 49/50 ] (4.0s) loss=0.8955 aux_flow=1.2256 aux_pkt=0.9165 +[epoch 50/50 ] (40.0s) loss=0.9002 auroc_terminal=0.960 aux_flow=1.2373 aux_pkt=0.9226 +[saved] /home/chy/mambafortrafficmodeling/artifacts/route_comparison/baseline_ciciot2023_seed44/model.pt diff --git a/artifacts/route_comparison/route_a_causal_seed42.log b/artifacts/route_comparison/route_a_causal_seed42.log new file mode 100644 index 0000000..7140646 --- /dev/null +++ b/artifacts/route_comparison/route_a_causal_seed42.log @@ -0,0 +1,61 @@ +Device: cuda +[seed] model=42 data=42 +[data] flows=/home/chy/mambafortrafficmodeling/datasets/ciciot2023/processed/full_store/flows.parquet packets_source=/home/chy/mambafortrafficmodeling/datasets/ciciot2023/processed/full_store +[data] using external flow features D=20 +[data] rows total=8,193,621 keep len>=2: 3,797,530 +[data] benign=97,045 attack=20,000 -> train=77,636 val=10,000 +[data] T=64 packet_D=9 flow_D=20 train=77,636 val=10,000 attack=20,000 +[data] using 10,000 benign training flows +[model] params=1,226,261 token_dim=21 seq_len=65 sigma=0.1 use_ot=True reference_mode=causal_packets +[loss] λ_flow=0.3 λ_packet=0.3 packet_mask_ratio=0.5 +[epoch 1/50 ] (2.6s) loss=2.2380 aux_flow=2.6923 aux_pkt=1.0203 +[epoch 2/50 ] (2.1s) loss=1.8172 aux_flow=2.2086 aux_pkt=0.9828 +[epoch 3/50 ] (2.2s) loss=1.5662 aux_flow=1.9835 aux_pkt=0.9600 +[epoch 4/50 ] (2.2s) loss=1.4073 aux_flow=1.8576 aux_pkt=0.9430 +[epoch 5/50 ] (2.2s) loss=1.2753 aux_flow=1.7117 aux_pkt=0.9369 +[epoch 6/50 ] (2.2s) loss=1.1937 aux_flow=1.6339 aux_pkt=0.9321 +[epoch 7/50 ] (2.1s) loss=1.1450 aux_flow=1.5880 aux_pkt=0.9273 +[epoch 8/50 ] (2.2s) loss=1.1089 aux_flow=1.5219 aux_pkt=0.9279 +[epoch 9/50 ] (2.1s) loss=1.0736 aux_flow=1.4709 aux_pkt=0.9299 +[epoch 10/50 ] (16.5s) loss=1.0578 auroc_terminal=0.955 aux_flow=1.4602 aux_pkt=0.9224 +[epoch 11/50 ] (2.1s) loss=1.0278 aux_flow=1.4116 aux_pkt=0.9229 +[epoch 12/50 ] (2.1s) loss=1.0097 aux_flow=1.3753 aux_pkt=0.9225 +[epoch 13/50 ] (2.1s) loss=1.0283 aux_flow=1.4326 aux_pkt=0.9229 +[epoch 14/50 ] (2.1s) loss=0.9791 aux_flow=1.3302 aux_pkt=0.9169 +[epoch 15/50 ] (2.1s) loss=0.9650 aux_flow=1.3061 aux_pkt=0.9191 +[epoch 16/50 ] (2.1s) loss=0.9617 aux_flow=1.3056 aux_pkt=0.9150 +[epoch 17/50 ] (2.1s) loss=0.9696 aux_flow=1.3364 aux_pkt=0.9165 +[epoch 18/50 ] (2.1s) loss=0.9715 aux_flow=1.3450 aux_pkt=0.9175 +[epoch 19/50 ] (2.1s) loss=0.9673 aux_flow=1.3524 aux_pkt=0.9220 +[epoch 20/50 ] (16.3s) loss=0.9409 auroc_terminal=0.959 aux_flow=1.2745 aux_pkt=0.9217 +[epoch 21/50 ] (2.1s) loss=0.9365 aux_flow=1.2878 aux_pkt=0.9114 +[epoch 22/50 ] (2.1s) loss=0.9301 aux_flow=1.2805 aux_pkt=0.9120 +[epoch 23/50 ] (2.1s) loss=0.9262 aux_flow=1.2736 aux_pkt=0.9136 +[epoch 24/50 ] (2.1s) loss=0.9245 aux_flow=1.2689 aux_pkt=0.9180 +[epoch 25/50 ] (2.1s) loss=0.9169 aux_flow=1.2569 aux_pkt=0.9155 +[epoch 26/50 ] (2.1s) loss=0.9217 aux_flow=1.2750 aux_pkt=0.9138 +[epoch 27/50 ] (2.1s) loss=0.9034 aux_flow=1.2295 aux_pkt=0.9182 +[epoch 28/50 ] (2.1s) loss=0.9068 aux_flow=1.2465 aux_pkt=0.9133 +[epoch 29/50 ] (2.1s) loss=0.9019 aux_flow=1.2457 aux_pkt=0.9116 +[epoch 30/50 ] (16.3s) loss=0.9012 auroc_terminal=0.951 aux_flow=1.2319 aux_pkt=0.9162 +[epoch 31/50 ] (2.1s) loss=0.9013 aux_flow=1.2417 aux_pkt=0.9145 +[epoch 32/50 ] (2.1s) loss=0.9059 aux_flow=1.2610 aux_pkt=0.9132 +[epoch 33/50 ] (2.1s) loss=0.8978 aux_flow=1.2296 aux_pkt=0.9174 +[epoch 34/50 ] (2.1s) loss=0.8993 aux_flow=1.2505 aux_pkt=0.9153 +[epoch 35/50 ] (2.1s) loss=0.8850 aux_flow=1.2160 aux_pkt=0.9091 +[epoch 36/50 ] (2.1s) loss=0.8795 aux_flow=1.2042 aux_pkt=0.9063 +[epoch 37/50 ] (2.1s) loss=0.8763 aux_flow=1.2019 aux_pkt=0.9084 +[epoch 38/50 ] (2.1s) loss=0.8860 aux_flow=1.2183 aux_pkt=0.9124 +[epoch 39/50 ] (2.1s) loss=0.8856 aux_flow=1.2206 aux_pkt=0.9178 +[epoch 40/50 ] (16.4s) loss=0.8829 auroc_terminal=0.966 aux_flow=1.2216 aux_pkt=0.9077 +[epoch 41/50 ] (2.2s) loss=0.8864 aux_flow=1.2224 aux_pkt=0.9110 +[epoch 42/50 ] (2.2s) loss=0.8806 aux_flow=1.2121 aux_pkt=0.9121 +[epoch 43/50 ] (2.2s) loss=0.8681 aux_flow=1.1815 aux_pkt=0.9076 +[epoch 44/50 ] (2.2s) loss=0.8800 aux_flow=1.2063 aux_pkt=0.9135 +[epoch 45/50 ] (2.2s) loss=0.8749 aux_flow=1.1974 aux_pkt=0.9087 +[epoch 46/50 ] (2.2s) loss=0.8747 aux_flow=1.1876 aux_pkt=0.9129 +[epoch 47/50 ] (2.1s) loss=0.8759 aux_flow=1.2051 aux_pkt=0.9121 +[epoch 48/50 ] (2.1s) loss=0.8707 aux_flow=1.1847 aux_pkt=0.9106 +[epoch 49/50 ] (2.1s) loss=0.8762 aux_flow=1.1999 aux_pkt=0.9097 +[epoch 50/50 ] (16.3s) loss=0.8713 auroc_terminal=0.965 aux_flow=1.1869 aux_pkt=0.9094 +[saved] /home/chy/mambafortrafficmodeling/artifacts/route_comparison/route_a_causal_ciciot2023_seed42/model.pt diff --git a/artifacts/route_comparison/route_a_causal_seed43.log b/artifacts/route_comparison/route_a_causal_seed43.log new file mode 100644 index 0000000..514f2bd --- /dev/null +++ b/artifacts/route_comparison/route_a_causal_seed43.log @@ -0,0 +1,61 @@ +Device: cuda +[seed] model=43 data=43 +[data] flows=/home/chy/mambafortrafficmodeling/datasets/ciciot2023/processed/full_store/flows.parquet packets_source=/home/chy/mambafortrafficmodeling/datasets/ciciot2023/processed/full_store +[data] using external flow features D=20 +[data] rows total=8,193,621 keep len>=2: 3,797,530 +[data] benign=97,045 attack=20,000 -> train=77,636 val=10,000 +[data] T=64 packet_D=9 flow_D=20 train=77,636 val=10,000 attack=20,000 +[data] using 10,000 benign training flows +[model] params=1,226,261 token_dim=21 seq_len=65 sigma=0.1 use_ot=True reference_mode=causal_packets +[loss] λ_flow=0.3 λ_packet=0.3 packet_mask_ratio=0.5 +[epoch 1/50 ] (6.6s) loss=2.2959 aux_flow=2.8675 aux_pkt=1.0198 +[epoch 2/50 ] (6.0s) loss=1.8685 aux_flow=2.3522 aux_pkt=0.9891 +[epoch 3/50 ] (6.2s) loss=1.5978 aux_flow=2.0900 aux_pkt=0.9588 +[epoch 4/50 ] (6.1s) loss=1.4301 aux_flow=1.9213 aux_pkt=0.9547 +[epoch 5/50 ] (6.1s) loss=1.3076 aux_flow=1.7716 aux_pkt=0.9504 +[epoch 6/50 ] (6.0s) loss=1.2276 aux_flow=1.7145 aux_pkt=0.9371 +[epoch 7/50 ] (4.7s) loss=1.1703 aux_flow=1.6318 aux_pkt=0.9364 +[epoch 8/50 ] (4.5s) loss=1.1194 aux_flow=1.5476 aux_pkt=0.9386 +[epoch 9/50 ] (4.5s) loss=1.0877 aux_flow=1.4994 aux_pkt=0.9305 +[epoch 10/50 ] (62.0s) loss=1.0637 auroc_terminal=0.947 aux_flow=1.4755 aux_pkt=0.9272 +[epoch 11/50 ] (6.2s) loss=1.0444 aux_flow=1.4389 aux_pkt=0.9275 +[epoch 12/50 ] (6.1s) loss=1.0405 aux_flow=1.4481 aux_pkt=0.9283 +[epoch 13/50 ] (6.0s) loss=1.0182 aux_flow=1.3953 aux_pkt=0.9292 +[epoch 14/50 ] (6.1s) loss=1.0272 aux_flow=1.4086 aux_pkt=0.9345 +[epoch 15/50 ] (6.0s) loss=1.0115 aux_flow=1.3953 aux_pkt=0.9279 +[epoch 16/50 ] (6.0s) loss=0.9934 aux_flow=1.3773 aux_pkt=0.9232 +[epoch 17/50 ] (5.9s) loss=0.9976 aux_flow=1.3996 aux_pkt=0.9226 +[epoch 18/50 ] (4.4s) loss=0.9984 aux_flow=1.4081 aux_pkt=0.9210 +[epoch 19/50 ] (4.4s) loss=0.9801 aux_flow=1.3475 aux_pkt=0.9192 +[epoch 20/50 ] (62.5s) loss=0.9614 auroc_terminal=0.958 aux_flow=1.3169 aux_pkt=0.9251 +[epoch 21/50 ] (6.0s) loss=0.9617 aux_flow=1.3308 aux_pkt=0.9201 +[epoch 22/50 ] (6.2s) loss=0.9594 aux_flow=1.3368 aux_pkt=0.9207 +[epoch 23/50 ] (6.2s) loss=0.9529 aux_flow=1.3144 aux_pkt=0.9228 +[epoch 24/50 ] (6.1s) loss=0.9419 aux_flow=1.2857 aux_pkt=0.9203 +[epoch 25/50 ] (6.0s) loss=0.9423 aux_flow=1.3042 aux_pkt=0.9192 +[epoch 26/50 ] (6.1s) loss=0.9366 aux_flow=1.3046 aux_pkt=0.9189 +[epoch 27/50 ] (6.2s) loss=0.9281 aux_flow=1.2809 aux_pkt=0.9168 +[epoch 28/50 ] (4.8s) loss=0.9284 aux_flow=1.2921 aux_pkt=0.9189 +[epoch 29/50 ] (4.4s) loss=0.9183 aux_flow=1.2753 aux_pkt=0.9168 +[epoch 30/50 ] (63.0s) loss=0.9140 auroc_terminal=0.949 aux_flow=1.2496 aux_pkt=0.9194 +[epoch 31/50 ] (6.2s) loss=0.9256 aux_flow=1.2875 aux_pkt=0.9186 +[epoch 32/50 ] (6.0s) loss=0.9190 aux_flow=1.2749 aux_pkt=0.9204 +[epoch 33/50 ] (6.0s) loss=0.9111 aux_flow=1.2474 aux_pkt=0.9200 +[epoch 34/50 ] (6.0s) loss=0.9224 aux_flow=1.2890 aux_pkt=0.9254 +[epoch 35/50 ] (6.0s) loss=0.9126 aux_flow=1.2701 aux_pkt=0.9168 +[epoch 36/50 ] (6.0s) loss=0.9046 aux_flow=1.2480 aux_pkt=0.9178 +[epoch 37/50 ] (6.0s) loss=0.9037 aux_flow=1.2394 aux_pkt=0.9186 +[epoch 38/50 ] (4.6s) loss=0.9031 aux_flow=1.2446 aux_pkt=0.9147 +[epoch 39/50 ] (3.7s) loss=0.9007 aux_flow=1.2302 aux_pkt=0.9203 +[epoch 40/50 ] (64.2s) loss=0.8960 auroc_terminal=0.963 aux_flow=1.2332 aux_pkt=0.9150 +[epoch 41/50 ] (6.1s) loss=0.8942 aux_flow=1.2376 aux_pkt=0.9147 +[epoch 42/50 ] (6.0s) loss=0.8919 aux_flow=1.2276 aux_pkt=0.9181 +[epoch 43/50 ] (6.0s) loss=0.8982 aux_flow=1.2339 aux_pkt=0.9173 +[epoch 44/50 ] (6.0s) loss=0.8850 aux_flow=1.2133 aux_pkt=0.9105 +[epoch 45/50 ] (6.0s) loss=0.8926 aux_flow=1.2262 aux_pkt=0.9129 +[epoch 46/50 ] (6.1s) loss=0.8909 aux_flow=1.2210 aux_pkt=0.9181 +[epoch 47/50 ] (6.0s) loss=0.8919 aux_flow=1.2334 aux_pkt=0.9124 +[epoch 48/50 ] (4.3s) loss=0.8868 aux_flow=1.2138 aux_pkt=0.9134 +[epoch 49/50 ] (3.0s) loss=0.8867 aux_flow=1.2226 aux_pkt=0.9084 +[epoch 50/50 ] (53.7s) loss=0.8878 auroc_terminal=0.963 aux_flow=1.2178 aux_pkt=0.9188 +[saved] /home/chy/mambafortrafficmodeling/artifacts/route_comparison/route_a_causal_ciciot2023_seed43/model.pt diff --git a/artifacts/route_comparison/route_a_causal_seed44.log b/artifacts/route_comparison/route_a_causal_seed44.log new file mode 100644 index 0000000..dd85a2d --- /dev/null +++ b/artifacts/route_comparison/route_a_causal_seed44.log @@ -0,0 +1,61 @@ +Device: cuda +[seed] model=44 data=44 +[data] flows=/home/chy/mambafortrafficmodeling/datasets/ciciot2023/processed/full_store/flows.parquet packets_source=/home/chy/mambafortrafficmodeling/datasets/ciciot2023/processed/full_store +[data] using external flow features D=20 +[data] rows total=8,193,621 keep len>=2: 3,797,530 +[data] benign=97,045 attack=20,000 -> train=77,636 val=10,000 +[data] T=64 packet_D=9 flow_D=20 train=77,636 val=10,000 attack=20,000 +[data] using 10,000 benign training flows +[model] params=1,226,261 token_dim=21 seq_len=65 sigma=0.1 use_ot=True reference_mode=causal_packets +[loss] λ_flow=0.3 λ_packet=0.3 packet_mask_ratio=0.5 +[epoch 1/50 ] (4.7s) loss=2.3750 aux_flow=3.0676 aux_pkt=1.0370 +[epoch 2/50 ] (4.0s) loss=1.9489 aux_flow=2.5473 aux_pkt=0.9941 +[epoch 3/50 ] (4.2s) loss=1.6761 aux_flow=2.2727 aux_pkt=0.9608 +[epoch 4/50 ] (4.2s) loss=1.5044 aux_flow=2.0865 aux_pkt=0.9600 +[epoch 5/50 ] (4.0s) loss=1.3762 aux_flow=1.9236 aux_pkt=0.9518 +[epoch 6/50 ] (4.2s) loss=1.2737 aux_flow=1.8012 aux_pkt=0.9448 +[epoch 7/50 ] (4.1s) loss=1.2216 aux_flow=1.7500 aux_pkt=0.9405 +[epoch 8/50 ] (4.1s) loss=1.1493 aux_flow=1.6228 aux_pkt=0.9387 +[epoch 9/50 ] (4.1s) loss=1.1219 aux_flow=1.5798 aux_pkt=0.9383 +[epoch 10/50 ] (39.1s) loss=1.0879 auroc_terminal=0.947 aux_flow=1.5309 aux_pkt=0.9272 +[epoch 11/50 ] (4.0s) loss=1.0890 aux_flow=1.5261 aux_pkt=0.9321 +[epoch 12/50 ] (4.0s) loss=1.0708 aux_flow=1.5040 aux_pkt=0.9397 +[epoch 13/50 ] (4.0s) loss=1.0345 aux_flow=1.4267 aux_pkt=0.9298 +[epoch 14/50 ] (3.9s) loss=1.0338 aux_flow=1.4521 aux_pkt=0.9289 +[epoch 15/50 ] (3.9s) loss=1.0246 aux_flow=1.4387 aux_pkt=0.9316 +[epoch 16/50 ] (3.9s) loss=1.0059 aux_flow=1.3998 aux_pkt=0.9301 +[epoch 17/50 ] (4.0s) loss=1.0186 aux_flow=1.4382 aux_pkt=0.9363 +[epoch 18/50 ] (4.1s) loss=0.9842 aux_flow=1.3565 aux_pkt=0.9313 +[epoch 19/50 ] (4.0s) loss=0.9716 aux_flow=1.3373 aux_pkt=0.9212 +[epoch 20/50 ] (38.9s) loss=0.9684 auroc_terminal=0.954 aux_flow=1.3399 aux_pkt=0.9230 +[epoch 21/50 ] (4.0s) loss=0.9674 aux_flow=1.3455 aux_pkt=0.9214 +[epoch 22/50 ] (4.2s) loss=0.9620 aux_flow=1.3472 aux_pkt=0.9187 +[epoch 23/50 ] (4.0s) loss=0.9594 aux_flow=1.3501 aux_pkt=0.9199 +[epoch 24/50 ] (3.9s) loss=0.9626 aux_flow=1.3495 aux_pkt=0.9213 +[epoch 25/50 ] (3.9s) loss=0.9356 aux_flow=1.2959 aux_pkt=0.9235 +[epoch 26/50 ] (4.0s) loss=0.9334 aux_flow=1.2857 aux_pkt=0.9222 +[epoch 27/50 ] (4.0s) loss=0.9385 aux_flow=1.2991 aux_pkt=0.9215 +[epoch 28/50 ] (4.0s) loss=0.9381 aux_flow=1.3131 aux_pkt=0.9231 +[epoch 29/50 ] (3.9s) loss=0.9329 aux_flow=1.2921 aux_pkt=0.9215 +[epoch 30/50 ] (38.9s) loss=0.9211 auroc_terminal=0.960 aux_flow=1.2794 aux_pkt=0.9148 +[epoch 31/50 ] (4.0s) loss=0.9249 aux_flow=1.2804 aux_pkt=0.9218 +[epoch 32/50 ] (4.2s) loss=0.9136 aux_flow=1.2724 aux_pkt=0.9143 +[epoch 33/50 ] (4.1s) loss=0.9095 aux_flow=1.2530 aux_pkt=0.9194 +[epoch 34/50 ] (4.1s) loss=0.9063 aux_flow=1.2465 aux_pkt=0.9158 +[epoch 35/50 ] (4.1s) loss=0.9018 aux_flow=1.2373 aux_pkt=0.9193 +[epoch 36/50 ] (4.2s) loss=0.9110 aux_flow=1.2656 aux_pkt=0.9214 +[epoch 37/50 ] (4.2s) loss=0.9033 aux_flow=1.2478 aux_pkt=0.9238 +[epoch 38/50 ] (4.1s) loss=0.9051 aux_flow=1.2477 aux_pkt=0.9244 +[epoch 39/50 ] (4.1s) loss=0.9041 aux_flow=1.2505 aux_pkt=0.9149 +[epoch 40/50 ] (39.4s) loss=0.8997 auroc_terminal=0.961 aux_flow=1.2348 aux_pkt=0.9246 +[epoch 41/50 ] (4.0s) loss=0.8969 aux_flow=1.2279 aux_pkt=0.9180 +[epoch 42/50 ] (3.9s) loss=0.8951 aux_flow=1.2318 aux_pkt=0.9196 +[epoch 43/50 ] (3.9s) loss=0.8981 aux_flow=1.2364 aux_pkt=0.9190 +[epoch 44/50 ] (4.1s) loss=0.8958 aux_flow=1.2263 aux_pkt=0.9220 +[epoch 45/50 ] (3.9s) loss=0.9015 aux_flow=1.2515 aux_pkt=0.9192 +[epoch 46/50 ] (3.9s) loss=0.9003 aux_flow=1.2421 aux_pkt=0.9244 +[epoch 47/50 ] (4.0s) loss=0.8903 aux_flow=1.2153 aux_pkt=0.9264 +[epoch 48/50 ] (4.1s) loss=0.8877 aux_flow=1.2175 aux_pkt=0.9142 +[epoch 49/50 ] (4.2s) loss=0.8928 aux_flow=1.2186 aux_pkt=0.9169 +[epoch 50/50 ] (38.3s) loss=0.8974 auroc_terminal=0.964 aux_flow=1.2308 aux_pkt=0.9225 +[saved] /home/chy/mambafortrafficmodeling/artifacts/route_comparison/route_a_causal_ciciot2023_seed44/model.pt diff --git a/artifacts/route_comparison/route_ac_combo_cicddos2019_seed42.log b/artifacts/route_comparison/route_ac_combo_cicddos2019_seed42.log new file mode 100644 index 0000000..064e3d6 --- /dev/null +++ b/artifacts/route_comparison/route_ac_combo_cicddos2019_seed42.log @@ -0,0 +1,58 @@ +Device: cuda seed=model:42/data:42 +[data] flows=/home/chy/mambafortrafficmodeling/datasets/cicddos2019/processed/flows.parquet packets=/home/chy/mambafortrafficmodeling/datasets/cicddos2019/processed/full_store +[data] kept 8,986,875 of 8,993,376 (min_len=2) +[data] train=74,565 val=18,642 attack=20,000 +[data] T=64 cont=3 disc=6 flow=20 train=74,565 val=18,642 attack=20,000 +[data] training on 10,000 flows +[model] params=1,227,809 token_dim=21 sigma=0.1 use_ot=True lambda_disc=1.0 +[epoch 1/50 ] (2.2s) loss=1.2867 L_disc=0.4776 +[epoch 2/50 ] (1.6s) loss=0.9260 L_disc=0.3648 +[epoch 3/50 ] (1.6s) loss=0.7255 L_disc=0.3391 +[epoch 4/50 ] (1.6s) loss=0.6166 L_disc=0.3174 +[epoch 5/50 ] (1.6s) loss=0.5367 L_disc=0.2993 +[epoch 6/50 ] (1.4s) loss=0.4895 L_disc=0.2824 +[epoch 7/50 ] (1.5s) loss=0.4529 L_disc=0.2657 +[epoch 8/50 ] (1.7s) loss=0.4244 L_disc=0.2501 +[epoch 9/50 ] (1.6s) loss=0.3960 L_disc=0.2316 +[epoch 10/50 ] (5.3s) loss=0.3785 auroc_term=0.989 auroc_disc=0.615 L_disc=0.2210 +[epoch 11/50 ] (1.7s) loss=0.3633 L_disc=0.2144 +[epoch 12/50 ] (1.7s) loss=0.3543 L_disc=0.2076 +[epoch 13/50 ] (1.6s) loss=0.3473 L_disc=0.2045 +[epoch 14/50 ] (1.7s) loss=0.3343 L_disc=0.1972 +[epoch 15/50 ] (1.5s) loss=0.3288 L_disc=0.1944 +[epoch 16/50 ] (1.4s) loss=0.3252 L_disc=0.1947 +[epoch 17/50 ] (1.5s) loss=0.3161 L_disc=0.1869 +[epoch 18/50 ] (1.7s) loss=0.3169 L_disc=0.1886 +[epoch 19/50 ] (1.7s) loss=0.3122 L_disc=0.1870 +[epoch 20/50 ] (5.2s) loss=0.3064 auroc_term=0.995 auroc_disc=0.537 L_disc=0.1839 +[epoch 21/50 ] (1.7s) loss=0.3052 L_disc=0.1816 +[epoch 22/50 ] (1.6s) loss=0.3031 L_disc=0.1823 +[epoch 23/50 ] (1.6s) loss=0.2957 L_disc=0.1774 +[epoch 24/50 ] (1.6s) loss=0.2939 L_disc=0.1758 +[epoch 25/50 ] (1.5s) loss=0.2907 L_disc=0.1753 +[epoch 26/50 ] (1.5s) loss=0.2924 L_disc=0.1752 +[epoch 27/50 ] (1.7s) loss=0.2846 L_disc=0.1712 +[epoch 28/50 ] (1.6s) loss=0.2877 L_disc=0.1729 +[epoch 29/50 ] (1.7s) loss=0.2821 L_disc=0.1711 +[epoch 30/50 ] (5.3s) loss=0.2816 auroc_term=0.996 auroc_disc=0.530 L_disc=0.1708 +[epoch 31/50 ] (1.6s) loss=0.2805 L_disc=0.1691 +[epoch 32/50 ] (1.7s) loss=0.2784 L_disc=0.1689 +[epoch 33/50 ] (1.6s) loss=0.2756 L_disc=0.1673 +[epoch 34/50 ] (1.4s) loss=0.2718 L_disc=0.1650 +[epoch 35/50 ] (1.5s) loss=0.2772 L_disc=0.1674 +[epoch 36/50 ] (1.6s) loss=0.2718 L_disc=0.1652 +[epoch 37/50 ] (1.7s) loss=0.2726 L_disc=0.1653 +[epoch 38/50 ] (1.6s) loss=0.2738 L_disc=0.1654 +[epoch 39/50 ] (1.6s) loss=0.2707 L_disc=0.1646 +[epoch 40/50 ] (5.2s) loss=0.2712 auroc_term=0.996 auroc_disc=0.531 L_disc=0.1644 +[epoch 41/50 ] (1.6s) loss=0.2676 L_disc=0.1622 +[epoch 42/50 ] (1.5s) loss=0.2703 L_disc=0.1658 +[epoch 43/50 ] (1.5s) loss=0.2672 L_disc=0.1621 +[epoch 44/50 ] (1.5s) loss=0.2665 L_disc=0.1633 +[epoch 45/50 ] (1.4s) loss=0.2674 L_disc=0.1640 +[epoch 46/50 ] (1.4s) loss=0.2681 L_disc=0.1633 +[epoch 47/50 ] (1.3s) loss=0.2658 L_disc=0.1613 +[epoch 48/50 ] (1.4s) loss=0.2658 L_disc=0.1619 +[epoch 49/50 ] (1.4s) loss=0.2669 L_disc=0.1626 +[epoch 50/50 ] (5.0s) loss=0.2660 auroc_term=0.997 auroc_disc=0.527 L_disc=0.1611 +[saved] /home/chy/mambafortrafficmodeling/artifacts/route_comparison/route_ac_combo_cicddos2019_seed42/model.pt diff --git a/artifacts/route_comparison/route_ac_combo_cicddos2019_seed43.log b/artifacts/route_comparison/route_ac_combo_cicddos2019_seed43.log new file mode 100644 index 0000000..6a083d5 --- /dev/null +++ b/artifacts/route_comparison/route_ac_combo_cicddos2019_seed43.log @@ -0,0 +1,58 @@ +Device: cuda seed=model:43/data:43 +[data] flows=/home/chy/mambafortrafficmodeling/datasets/cicddos2019/processed/flows.parquet packets=/home/chy/mambafortrafficmodeling/datasets/cicddos2019/processed/full_store +[data] kept 8,986,875 of 8,993,376 (min_len=2) +[data] train=74,565 val=18,642 attack=20,000 +[data] T=64 cont=3 disc=6 flow=20 train=74,565 val=18,642 attack=20,000 +[data] training on 10,000 flows +[model] params=1,227,809 token_dim=21 sigma=0.1 use_ot=True lambda_disc=1.0 +[epoch 1/50 ] (2.2s) loss=1.2920 L_disc=0.4804 +[epoch 2/50 ] (1.6s) loss=0.9200 L_disc=0.3650 +[epoch 3/50 ] (1.6s) loss=0.7199 L_disc=0.3403 +[epoch 4/50 ] (1.6s) loss=0.6052 L_disc=0.3151 +[epoch 5/50 ] (1.6s) loss=0.5306 L_disc=0.2963 +[epoch 6/50 ] (1.6s) loss=0.4815 L_disc=0.2798 +[epoch 7/50 ] (1.6s) loss=0.4433 L_disc=0.2602 +[epoch 8/50 ] (1.7s) loss=0.4084 L_disc=0.2369 +[epoch 9/50 ] (1.6s) loss=0.3860 L_disc=0.2224 +[epoch 10/50 ] (5.1s) loss=0.3686 auroc_term=0.995 auroc_disc=0.758 L_disc=0.2134 +[epoch 11/50 ] (1.5s) loss=0.3569 L_disc=0.2050 +[epoch 12/50 ] (1.6s) loss=0.3445 L_disc=0.1977 +[epoch 13/50 ] (1.5s) loss=0.3348 L_disc=0.1928 +[epoch 14/50 ] (1.5s) loss=0.3227 L_disc=0.1892 +[epoch 15/50 ] (1.6s) loss=0.3224 L_disc=0.1885 +[epoch 16/50 ] (1.5s) loss=0.3217 L_disc=0.1881 +[epoch 17/50 ] (1.7s) loss=0.3131 L_disc=0.1827 +[epoch 18/50 ] (1.6s) loss=0.3080 L_disc=0.1806 +[epoch 19/50 ] (1.6s) loss=0.3051 L_disc=0.1799 +[epoch 20/50 ] (5.1s) loss=0.3001 auroc_term=0.996 auroc_disc=0.658 L_disc=0.1755 +[epoch 21/50 ] (1.5s) loss=0.2930 L_disc=0.1721 +[epoch 22/50 ] (1.7s) loss=0.2942 L_disc=0.1731 +[epoch 23/50 ] (1.7s) loss=0.2915 L_disc=0.1727 +[epoch 24/50 ] (1.6s) loss=0.2849 L_disc=0.1684 +[epoch 25/50 ] (1.6s) loss=0.2844 L_disc=0.1689 +[epoch 26/50 ] (1.6s) loss=0.2818 L_disc=0.1664 +[epoch 27/50 ] (1.7s) loss=0.2816 L_disc=0.1683 +[epoch 28/50 ] (1.6s) loss=0.2796 L_disc=0.1669 +[epoch 29/50 ] (1.5s) loss=0.2764 L_disc=0.1649 +[epoch 30/50 ] (5.1s) loss=0.2758 auroc_term=0.997 auroc_disc=0.643 L_disc=0.1656 +[epoch 31/50 ] (1.5s) loss=0.2749 L_disc=0.1639 +[epoch 32/50 ] (1.6s) loss=0.2750 L_disc=0.1658 +[epoch 33/50 ] (1.5s) loss=0.2738 L_disc=0.1632 +[epoch 34/50 ] (1.6s) loss=0.2719 L_disc=0.1620 +[epoch 35/50 ] (1.6s) loss=0.2716 L_disc=0.1621 +[epoch 36/50 ] (1.6s) loss=0.2685 L_disc=0.1619 +[epoch 37/50 ] (1.5s) loss=0.2675 L_disc=0.1607 +[epoch 38/50 ] (1.6s) loss=0.2636 L_disc=0.1583 +[epoch 39/50 ] (1.5s) loss=0.2641 L_disc=0.1592 +[epoch 40/50 ] (5.1s) loss=0.2653 auroc_term=0.998 auroc_disc=0.609 L_disc=0.1611 +[epoch 41/50 ] (1.4s) loss=0.2640 L_disc=0.1588 +[epoch 42/50 ] (1.4s) loss=0.2637 L_disc=0.1590 +[epoch 43/50 ] (1.4s) loss=0.2632 L_disc=0.1592 +[epoch 44/50 ] (1.4s) loss=0.2650 L_disc=0.1604 +[epoch 45/50 ] (1.5s) loss=0.2628 L_disc=0.1589 +[epoch 46/50 ] (1.6s) loss=0.2620 L_disc=0.1583 +[epoch 47/50 ] (1.6s) loss=0.2628 L_disc=0.1601 +[epoch 48/50 ] (1.7s) loss=0.2652 L_disc=0.1603 +[epoch 49/50 ] (1.6s) loss=0.2632 L_disc=0.1587 +[epoch 50/50 ] (5.2s) loss=0.2614 auroc_term=0.998 auroc_disc=0.620 L_disc=0.1572 +[saved] /home/chy/mambafortrafficmodeling/artifacts/route_comparison/route_ac_combo_cicddos2019_seed43/model.pt diff --git a/artifacts/route_comparison/route_ac_combo_cicddos2019_seed44.log b/artifacts/route_comparison/route_ac_combo_cicddos2019_seed44.log new file mode 100644 index 0000000..dc08a87 --- /dev/null +++ b/artifacts/route_comparison/route_ac_combo_cicddos2019_seed44.log @@ -0,0 +1,58 @@ +Device: cuda seed=model:44/data:44 +[data] flows=/home/chy/mambafortrafficmodeling/datasets/cicddos2019/processed/flows.parquet packets=/home/chy/mambafortrafficmodeling/datasets/cicddos2019/processed/full_store +[data] kept 8,986,875 of 8,993,376 (min_len=2) +[data] train=74,565 val=18,642 attack=20,000 +[data] T=64 cont=3 disc=6 flow=20 train=74,565 val=18,642 attack=20,000 +[data] training on 10,000 flows +[model] params=1,227,809 token_dim=21 sigma=0.1 use_ot=True lambda_disc=1.0 +[epoch 1/50 ] (2.3s) loss=1.2951 L_disc=0.4836 +[epoch 2/50 ] (1.6s) loss=0.9292 L_disc=0.3686 +[epoch 3/50 ] (1.6s) loss=0.7342 L_disc=0.3448 +[epoch 4/50 ] (1.5s) loss=0.6189 L_disc=0.3213 +[epoch 5/50 ] (1.4s) loss=0.5469 L_disc=0.3075 +[epoch 6/50 ] (1.6s) loss=0.5009 L_disc=0.2914 +[epoch 7/50 ] (1.6s) loss=0.4686 L_disc=0.2754 +[epoch 8/50 ] (1.5s) loss=0.4422 L_disc=0.2644 +[epoch 9/50 ] (1.6s) loss=0.4251 L_disc=0.2514 +[epoch 10/50 ] (5.3s) loss=0.4005 auroc_term=0.983 auroc_disc=0.368 L_disc=0.2362 +[epoch 11/50 ] (1.6s) loss=0.3802 L_disc=0.2219 +[epoch 12/50 ] (1.6s) loss=0.3649 L_disc=0.2145 +[epoch 13/50 ] (1.5s) loss=0.3584 L_disc=0.2113 +[epoch 14/50 ] (1.4s) loss=0.3491 L_disc=0.2053 +[epoch 15/50 ] (1.6s) loss=0.3367 L_disc=0.2004 +[epoch 16/50 ] (1.6s) loss=0.3318 L_disc=0.1953 +[epoch 17/50 ] (1.6s) loss=0.3295 L_disc=0.1955 +[epoch 18/50 ] (1.6s) loss=0.3238 L_disc=0.1926 +[epoch 19/50 ] (1.5s) loss=0.3208 L_disc=0.1914 +[epoch 20/50 ] (5.3s) loss=0.3097 auroc_term=0.988 auroc_disc=0.518 L_disc=0.1835 +[epoch 21/50 ] (1.6s) loss=0.3120 L_disc=0.1854 +[epoch 22/50 ] (1.7s) loss=0.3069 L_disc=0.1825 +[epoch 23/50 ] (1.5s) loss=0.3065 L_disc=0.1829 +[epoch 24/50 ] (1.6s) loss=0.2998 L_disc=0.1775 +[epoch 25/50 ] (1.8s) loss=0.2991 L_disc=0.1790 +[epoch 26/50 ] (1.8s) loss=0.2959 L_disc=0.1784 +[epoch 27/50 ] (1.8s) loss=0.2926 L_disc=0.1751 +[epoch 28/50 ] (1.6s) loss=0.2944 L_disc=0.1762 +[epoch 29/50 ] (1.6s) loss=0.2895 L_disc=0.1742 +[epoch 30/50 ] (5.3s) loss=0.2874 auroc_term=0.994 auroc_disc=0.553 L_disc=0.1723 +[epoch 31/50 ] (1.6s) loss=0.2861 L_disc=0.1711 +[epoch 32/50 ] (1.5s) loss=0.2854 L_disc=0.1717 +[epoch 33/50 ] (1.4s) loss=0.2822 L_disc=0.1699 +[epoch 34/50 ] (1.4s) loss=0.2818 L_disc=0.1710 +[epoch 35/50 ] (1.4s) loss=0.2818 L_disc=0.1710 +[epoch 36/50 ] (1.4s) loss=0.2824 L_disc=0.1708 +[epoch 37/50 ] (1.4s) loss=0.2779 L_disc=0.1684 +[epoch 38/50 ] (1.4s) loss=0.2777 L_disc=0.1681 +[epoch 39/50 ] (1.4s) loss=0.2749 L_disc=0.1653 +[epoch 40/50 ] (4.9s) loss=0.2787 auroc_term=0.995 auroc_disc=0.515 L_disc=0.1683 +[epoch 41/50 ] (1.4s) loss=0.2740 L_disc=0.1655 +[epoch 42/50 ] (1.4s) loss=0.2731 L_disc=0.1675 +[epoch 43/50 ] (1.4s) loss=0.2747 L_disc=0.1665 +[epoch 44/50 ] (1.4s) loss=0.2724 L_disc=0.1646 +[epoch 45/50 ] (1.3s) loss=0.2724 L_disc=0.1652 +[epoch 46/50 ] (1.4s) loss=0.2739 L_disc=0.1663 +[epoch 47/50 ] (1.3s) loss=0.2764 L_disc=0.1668 +[epoch 48/50 ] (1.4s) loss=0.2719 L_disc=0.1652 +[epoch 49/50 ] (1.4s) loss=0.2739 L_disc=0.1667 +[epoch 50/50 ] (5.0s) loss=0.2721 auroc_term=0.996 auroc_disc=0.523 L_disc=0.1643 +[saved] /home/chy/mambafortrafficmodeling/artifacts/route_comparison/route_ac_combo_cicddos2019_seed44/model.pt diff --git a/artifacts/route_comparison/route_ac_combo_cicids2017_seed42.log b/artifacts/route_comparison/route_ac_combo_cicids2017_seed42.log new file mode 100644 index 0000000..b398ca3 --- /dev/null +++ b/artifacts/route_comparison/route_ac_combo_cicids2017_seed42.log @@ -0,0 +1,58 @@ +Device: cuda seed=model:42/data:42 +[data] flows=/home/chy/mambafortrafficmodeling/datasets/cicids2017/processed/flows.parquet packets=/home/chy/mambafortrafficmodeling/datasets/cicids2017/processed/packets.npz +[data] kept 2,017,180 of 2,025,564 (min_len=2) +[data] train=1,210,760 val=302,690 attack=503,730 +[data] T=64 cont=3 disc=6 flow=20 train=1,210,760 val=302,690 attack=503,730 +[data] training on 10,000 flows +[model] params=1,227,809 token_dim=21 sigma=0.1 use_ot=True lambda_disc=1.0 +[epoch 1/50 ] (2.1s) loss=1.2840 L_disc=0.4660 +[epoch 2/50 ] (1.4s) loss=0.9156 L_disc=0.3417 +[epoch 3/50 ] (1.4s) loss=0.7043 L_disc=0.3158 +[epoch 4/50 ] (1.3s) loss=0.5913 L_disc=0.2964 +[epoch 5/50 ] (1.0s) loss=0.5248 L_disc=0.2786 +[epoch 6/50 ] (1.1s) loss=0.4805 L_disc=0.2646 +[epoch 7/50 ] (1.0s) loss=0.4503 L_disc=0.2524 +[epoch 8/50 ] (1.2s) loss=0.4252 L_disc=0.2396 +[epoch 9/50 ] (1.3s) loss=0.4064 L_disc=0.2290 +[epoch 10/50 ] (8.5s) loss=0.3940 auroc_term=0.966 auroc_disc=0.963 L_disc=0.2189 +[epoch 11/50 ] (1.4s) loss=0.3775 L_disc=0.2118 +[epoch 12/50 ] (1.3s) loss=0.3669 L_disc=0.2051 +[epoch 13/50 ] (1.3s) loss=0.3600 L_disc=0.2020 +[epoch 14/50 ] (1.3s) loss=0.3533 L_disc=0.1978 +[epoch 15/50 ] (1.4s) loss=0.3465 L_disc=0.1963 +[epoch 16/50 ] (1.3s) loss=0.3442 L_disc=0.1956 +[epoch 17/50 ] (1.4s) loss=0.3384 L_disc=0.1910 +[epoch 18/50 ] (1.4s) loss=0.3324 L_disc=0.1894 +[epoch 19/50 ] (1.4s) loss=0.3266 L_disc=0.1868 +[epoch 20/50 ] (8.5s) loss=0.3227 auroc_term=0.970 auroc_disc=0.982 L_disc=0.1839 +[epoch 21/50 ] (1.4s) loss=0.3232 L_disc=0.1853 +[epoch 22/50 ] (1.4s) loss=0.3175 L_disc=0.1824 +[epoch 23/50 ] (1.4s) loss=0.3149 L_disc=0.1809 +[epoch 24/50 ] (1.4s) loss=0.3087 L_disc=0.1761 +[epoch 25/50 ] (1.4s) loss=0.3071 L_disc=0.1761 +[epoch 26/50 ] (1.3s) loss=0.3058 L_disc=0.1736 +[epoch 27/50 ] (1.3s) loss=0.2993 L_disc=0.1708 +[epoch 28/50 ] (1.3s) loss=0.3011 L_disc=0.1725 +[epoch 29/50 ] (1.3s) loss=0.3013 L_disc=0.1726 +[epoch 30/50 ] (8.4s) loss=0.2981 auroc_term=0.983 auroc_disc=0.983 L_disc=0.1702 +[epoch 31/50 ] (1.4s) loss=0.2978 L_disc=0.1703 +[epoch 32/50 ] (1.4s) loss=0.2943 L_disc=0.1691 +[epoch 33/50 ] (1.4s) loss=0.2940 L_disc=0.1691 +[epoch 34/50 ] (1.4s) loss=0.2892 L_disc=0.1659 +[epoch 35/50 ] (1.4s) loss=0.2921 L_disc=0.1694 +[epoch 36/50 ] (1.4s) loss=0.2902 L_disc=0.1677 +[epoch 37/50 ] (1.4s) loss=0.2890 L_disc=0.1669 +[epoch 38/50 ] (1.4s) loss=0.2916 L_disc=0.1669 +[epoch 39/50 ] (1.4s) loss=0.2838 L_disc=0.1629 +[epoch 40/50 ] (8.5s) loss=0.2891 auroc_term=0.984 auroc_disc=0.983 L_disc=0.1666 +[epoch 41/50 ] (1.4s) loss=0.2848 L_disc=0.1637 +[epoch 42/50 ] (1.3s) loss=0.2846 L_disc=0.1646 +[epoch 43/50 ] (1.4s) loss=0.2851 L_disc=0.1645 +[epoch 44/50 ] (1.4s) loss=0.2827 L_disc=0.1632 +[epoch 45/50 ] (1.4s) loss=0.2839 L_disc=0.1646 +[epoch 46/50 ] (1.4s) loss=0.2827 L_disc=0.1629 +[epoch 47/50 ] (1.4s) loss=0.2830 L_disc=0.1629 +[epoch 48/50 ] (1.3s) loss=0.2850 L_disc=0.1645 +[epoch 49/50 ] (1.3s) loss=0.2828 L_disc=0.1631 +[epoch 50/50 ] (8.5s) loss=0.2831 auroc_term=0.986 auroc_disc=0.983 L_disc=0.1633 +[saved] /home/chy/mambafortrafficmodeling/artifacts/route_comparison/route_ac_combo_cicids2017_seed42/model.pt diff --git a/artifacts/route_comparison/route_ac_combo_cicids2017_seed43.log b/artifacts/route_comparison/route_ac_combo_cicids2017_seed43.log new file mode 100644 index 0000000..032b414 --- /dev/null +++ b/artifacts/route_comparison/route_ac_combo_cicids2017_seed43.log @@ -0,0 +1,58 @@ +Device: cuda seed=model:43/data:43 +[data] flows=/home/chy/mambafortrafficmodeling/datasets/cicids2017/processed/flows.parquet packets=/home/chy/mambafortrafficmodeling/datasets/cicids2017/processed/packets.npz +[data] kept 2,017,180 of 2,025,564 (min_len=2) +[data] train=1,210,760 val=302,690 attack=503,730 +[data] T=64 cont=3 disc=6 flow=20 train=1,210,760 val=302,690 attack=503,730 +[data] training on 10,000 flows +[model] params=1,227,809 token_dim=21 sigma=0.1 use_ot=True lambda_disc=1.0 +[epoch 1/50 ] (2.1s) loss=1.2874 L_disc=0.4639 +[epoch 2/50 ] (1.5s) loss=0.9137 L_disc=0.3415 +[epoch 3/50 ] (1.5s) loss=0.7024 L_disc=0.3152 +[epoch 4/50 ] (1.4s) loss=0.5849 L_disc=0.2928 +[epoch 5/50 ] (1.4s) loss=0.5184 L_disc=0.2772 +[epoch 6/50 ] (1.4s) loss=0.4747 L_disc=0.2611 +[epoch 7/50 ] (1.4s) loss=0.4455 L_disc=0.2492 +[epoch 8/50 ] (1.4s) loss=0.4191 L_disc=0.2335 +[epoch 9/50 ] (1.4s) loss=0.3992 L_disc=0.2227 +[epoch 10/50 ] (8.4s) loss=0.3849 auroc_term=0.981 auroc_disc=0.953 L_disc=0.2147 +[epoch 11/50 ] (1.4s) loss=0.3738 L_disc=0.2083 +[epoch 12/50 ] (1.4s) loss=0.3642 L_disc=0.2025 +[epoch 13/50 ] (1.4s) loss=0.3566 L_disc=0.1983 +[epoch 14/50 ] (1.4s) loss=0.3498 L_disc=0.1971 +[epoch 15/50 ] (1.4s) loss=0.3385 L_disc=0.1900 +[epoch 16/50 ] (1.4s) loss=0.3364 L_disc=0.1894 +[epoch 17/50 ] (1.4s) loss=0.3330 L_disc=0.1871 +[epoch 18/50 ] (1.4s) loss=0.3278 L_disc=0.1855 +[epoch 19/50 ] (1.4s) loss=0.3268 L_disc=0.1841 +[epoch 20/50 ] (8.5s) loss=0.3200 auroc_term=0.979 auroc_disc=0.982 L_disc=0.1817 +[epoch 21/50 ] (1.4s) loss=0.3171 L_disc=0.1797 +[epoch 22/50 ] (1.4s) loss=0.3147 L_disc=0.1788 +[epoch 23/50 ] (1.4s) loss=0.3111 L_disc=0.1772 +[epoch 24/50 ] (1.4s) loss=0.3071 L_disc=0.1755 +[epoch 25/50 ] (1.4s) loss=0.3070 L_disc=0.1756 +[epoch 26/50 ] (1.3s) loss=0.3058 L_disc=0.1732 +[epoch 27/50 ] (1.4s) loss=0.3013 L_disc=0.1725 +[epoch 28/50 ] (1.4s) loss=0.3001 L_disc=0.1713 +[epoch 29/50 ] (1.4s) loss=0.2986 L_disc=0.1706 +[epoch 30/50 ] (8.5s) loss=0.2953 auroc_term=0.976 auroc_disc=0.981 L_disc=0.1693 +[epoch 31/50 ] (1.4s) loss=0.2936 L_disc=0.1672 +[epoch 32/50 ] (1.4s) loss=0.2931 L_disc=0.1686 +[epoch 33/50 ] (1.4s) loss=0.2927 L_disc=0.1674 +[epoch 34/50 ] (1.4s) loss=0.2896 L_disc=0.1642 +[epoch 35/50 ] (1.4s) loss=0.2885 L_disc=0.1650 +[epoch 36/50 ] (1.4s) loss=0.2869 L_disc=0.1653 +[epoch 37/50 ] (1.4s) loss=0.2869 L_disc=0.1650 +[epoch 38/50 ] (1.4s) loss=0.2849 L_disc=0.1626 +[epoch 39/50 ] (1.4s) loss=0.2895 L_disc=0.1678 +[epoch 40/50 ] (8.5s) loss=0.2858 auroc_term=0.984 auroc_disc=0.983 L_disc=0.1661 +[epoch 41/50 ] (1.4s) loss=0.2848 L_disc=0.1628 +[epoch 42/50 ] (1.4s) loss=0.2844 L_disc=0.1637 +[epoch 43/50 ] (1.4s) loss=0.2844 L_disc=0.1627 +[epoch 44/50 ] (1.4s) loss=0.2824 L_disc=0.1630 +[epoch 45/50 ] (1.4s) loss=0.2812 L_disc=0.1615 +[epoch 46/50 ] (1.4s) loss=0.2827 L_disc=0.1634 +[epoch 47/50 ] (1.4s) loss=0.2805 L_disc=0.1621 +[epoch 48/50 ] (1.4s) loss=0.2835 L_disc=0.1639 +[epoch 49/50 ] (1.4s) loss=0.2845 L_disc=0.1642 +[epoch 50/50 ] (8.6s) loss=0.2812 auroc_term=0.986 auroc_disc=0.983 L_disc=0.1620 +[saved] /home/chy/mambafortrafficmodeling/artifacts/route_comparison/route_ac_combo_cicids2017_seed43/model.pt diff --git a/artifacts/route_comparison/route_ac_combo_cicids2017_seed44.log b/artifacts/route_comparison/route_ac_combo_cicids2017_seed44.log new file mode 100644 index 0000000..cc29e18 --- /dev/null +++ b/artifacts/route_comparison/route_ac_combo_cicids2017_seed44.log @@ -0,0 +1,58 @@ +Device: cuda seed=model:44/data:44 +[data] flows=/home/chy/mambafortrafficmodeling/datasets/cicids2017/processed/flows.parquet packets=/home/chy/mambafortrafficmodeling/datasets/cicids2017/processed/packets.npz +[data] kept 2,017,180 of 2,025,564 (min_len=2) +[data] train=1,210,760 val=302,690 attack=503,730 +[data] T=64 cont=3 disc=6 flow=20 train=1,210,760 val=302,690 attack=503,730 +[data] training on 10,000 flows +[model] params=1,227,809 token_dim=21 sigma=0.1 use_ot=True lambda_disc=1.0 +[epoch 1/50 ] (2.0s) loss=1.2839 L_disc=0.4620 +[epoch 2/50 ] (1.5s) loss=0.9119 L_disc=0.3367 +[epoch 3/50 ] (1.4s) loss=0.7023 L_disc=0.3103 +[epoch 4/50 ] (1.4s) loss=0.5846 L_disc=0.2888 +[epoch 5/50 ] (1.4s) loss=0.5216 L_disc=0.2747 +[epoch 6/50 ] (1.4s) loss=0.4807 L_disc=0.2620 +[epoch 7/50 ] (1.4s) loss=0.4525 L_disc=0.2490 +[epoch 8/50 ] (1.4s) loss=0.4253 L_disc=0.2373 +[epoch 9/50 ] (1.4s) loss=0.4102 L_disc=0.2275 +[epoch 10/50 ] (8.5s) loss=0.3861 auroc_term=0.898 auroc_disc=0.955 L_disc=0.2143 +[epoch 11/50 ] (1.4s) loss=0.3707 L_disc=0.2056 +[epoch 12/50 ] (1.4s) loss=0.3645 L_disc=0.2043 +[epoch 13/50 ] (1.4s) loss=0.3519 L_disc=0.1962 +[epoch 14/50 ] (1.4s) loss=0.3469 L_disc=0.1932 +[epoch 15/50 ] (1.3s) loss=0.3366 L_disc=0.1879 +[epoch 16/50 ] (1.3s) loss=0.3355 L_disc=0.1874 +[epoch 17/50 ] (1.4s) loss=0.3300 L_disc=0.1841 +[epoch 18/50 ] (1.4s) loss=0.3254 L_disc=0.1831 +[epoch 19/50 ] (1.4s) loss=0.3216 L_disc=0.1812 +[epoch 20/50 ] (8.5s) loss=0.3170 auroc_term=0.969 auroc_disc=0.976 L_disc=0.1795 +[epoch 21/50 ] (1.4s) loss=0.3158 L_disc=0.1778 +[epoch 22/50 ] (1.4s) loss=0.3138 L_disc=0.1785 +[epoch 23/50 ] (1.4s) loss=0.3140 L_disc=0.1780 +[epoch 24/50 ] (1.4s) loss=0.3077 L_disc=0.1732 +[epoch 25/50 ] (1.4s) loss=0.3016 L_disc=0.1702 +[epoch 26/50 ] (1.4s) loss=0.3023 L_disc=0.1712 +[epoch 27/50 ] (1.4s) loss=0.2995 L_disc=0.1695 +[epoch 28/50 ] (1.4s) loss=0.2985 L_disc=0.1686 +[epoch 29/50 ] (1.4s) loss=0.2936 L_disc=0.1662 +[epoch 30/50 ] (8.4s) loss=0.2926 auroc_term=0.981 auroc_disc=0.987 L_disc=0.1666 +[epoch 31/50 ] (1.4s) loss=0.2948 L_disc=0.1666 +[epoch 32/50 ] (1.4s) loss=0.2898 L_disc=0.1646 +[epoch 33/50 ] (1.3s) loss=0.2892 L_disc=0.1650 +[epoch 34/50 ] (1.4s) loss=0.2872 L_disc=0.1636 +[epoch 35/50 ] (1.4s) loss=0.2839 L_disc=0.1617 +[epoch 36/50 ] (1.4s) loss=0.2871 L_disc=0.1628 +[epoch 37/50 ] (1.4s) loss=0.2843 L_disc=0.1626 +[epoch 38/50 ] (1.4s) loss=0.2840 L_disc=0.1627 +[epoch 39/50 ] (1.4s) loss=0.2833 L_disc=0.1615 +[epoch 40/50 ] (8.5s) loss=0.2839 auroc_term=0.988 auroc_disc=0.987 L_disc=0.1618 +[epoch 41/50 ] (1.4s) loss=0.2826 L_disc=0.1624 +[epoch 42/50 ] (1.4s) loss=0.2779 L_disc=0.1589 +[epoch 43/50 ] (1.4s) loss=0.2800 L_disc=0.1590 +[epoch 44/50 ] (1.4s) loss=0.2821 L_disc=0.1622 +[epoch 45/50 ] (1.4s) loss=0.2813 L_disc=0.1600 +[epoch 46/50 ] (1.4s) loss=0.2798 L_disc=0.1596 +[epoch 47/50 ] (1.4s) loss=0.2821 L_disc=0.1610 +[epoch 48/50 ] (1.4s) loss=0.2813 L_disc=0.1614 +[epoch 49/50 ] (1.4s) loss=0.2805 L_disc=0.1596 +[epoch 50/50 ] (8.5s) loss=0.2803 auroc_term=0.989 auroc_disc=0.985 L_disc=0.1597 +[saved] /home/chy/mambafortrafficmodeling/artifacts/route_comparison/route_ac_combo_cicids2017_seed44/model.pt diff --git a/artifacts/route_comparison/route_ac_combo_iscxtor2016_seed42.log b/artifacts/route_comparison/route_ac_combo_iscxtor2016_seed42.log new file mode 100644 index 0000000..2c001bc --- /dev/null +++ b/artifacts/route_comparison/route_ac_combo_iscxtor2016_seed42.log @@ -0,0 +1,58 @@ +Device: cuda seed=model:42/data:42 +[data] flows=/home/chy/mambafortrafficmodeling/datasets/iscxtor2016/processed/flows.parquet packets=/home/chy/mambafortrafficmodeling/datasets/iscxtor2016/processed/packets.npz +[data] kept 66,189 of 103,079 (min_len=2) +[data] train=51,901 val=12,976 attack=1,312 +[data] T=64 cont=3 disc=6 flow=20 train=51,901 val=12,976 attack=1,312 +[data] training on 10,000 flows +[model] params=1,227,809 token_dim=21 sigma=0.1 use_ot=True lambda_disc=1.0 +[epoch 1/50 ] (2.0s) loss=1.3093 L_disc=0.4591 +[epoch 2/50 ] (1.4s) loss=0.9363 L_disc=0.3326 +[epoch 3/50 ] (1.4s) loss=0.7199 L_disc=0.3112 +[epoch 4/50 ] (1.4s) loss=0.6218 L_disc=0.2963 +[epoch 5/50 ] (1.4s) loss=0.5451 L_disc=0.2822 +[epoch 6/50 ] (1.5s) loss=0.4874 L_disc=0.2658 +[epoch 7/50 ] (1.6s) loss=0.4532 L_disc=0.2530 +[epoch 8/50 ] (1.7s) loss=0.4271 L_disc=0.2401 +[epoch 9/50 ] (1.7s) loss=0.3999 L_disc=0.2297 +[epoch 10/50 ] (4.3s) loss=0.3917 auroc_term=0.943 auroc_disc=0.574 L_disc=0.2261 +[epoch 11/50 ] (1.6s) loss=0.3770 L_disc=0.2223 +[epoch 12/50 ] (1.7s) loss=0.3680 L_disc=0.2170 +[epoch 13/50 ] (1.7s) loss=0.3591 L_disc=0.2115 +[epoch 14/50 ] (1.5s) loss=0.3536 L_disc=0.2094 +[epoch 15/50 ] (1.5s) loss=0.3421 L_disc=0.2028 +[epoch 16/50 ] (1.4s) loss=0.3306 L_disc=0.1964 +[epoch 17/50 ] (1.8s) loss=0.3257 L_disc=0.1906 +[epoch 18/50 ] (1.7s) loss=0.3177 L_disc=0.1893 +[epoch 19/50 ] (1.6s) loss=0.3141 L_disc=0.1869 +[epoch 20/50 ] (4.3s) loss=0.3126 auroc_term=0.989 auroc_disc=0.554 L_disc=0.1860 +[epoch 21/50 ] (1.7s) loss=0.3086 L_disc=0.1840 +[epoch 22/50 ] (1.7s) loss=0.3031 L_disc=0.1802 +[epoch 23/50 ] (1.6s) loss=0.3005 L_disc=0.1805 +[epoch 24/50 ] (1.7s) loss=0.3003 L_disc=0.1793 +[epoch 25/50 ] (1.4s) loss=0.2926 L_disc=0.1753 +[epoch 26/50 ] (1.4s) loss=0.2918 L_disc=0.1738 +[epoch 27/50 ] (1.6s) loss=0.2914 L_disc=0.1754 +[epoch 28/50 ] (1.6s) loss=0.2879 L_disc=0.1721 +[epoch 29/50 ] (1.6s) loss=0.2866 L_disc=0.1733 +[epoch 30/50 ] (4.2s) loss=0.2815 auroc_term=0.994 auroc_disc=0.675 L_disc=0.1697 +[epoch 31/50 ] (1.6s) loss=0.2784 L_disc=0.1665 +[epoch 32/50 ] (1.6s) loss=0.2781 L_disc=0.1669 +[epoch 33/50 ] (1.5s) loss=0.2776 L_disc=0.1677 +[epoch 34/50 ] (1.6s) loss=0.2750 L_disc=0.1662 +[epoch 35/50 ] (1.5s) loss=0.2752 L_disc=0.1665 +[epoch 36/50 ] (1.4s) loss=0.2715 L_disc=0.1645 +[epoch 37/50 ] (1.4s) loss=0.2727 L_disc=0.1652 +[epoch 38/50 ] (1.6s) loss=0.2721 L_disc=0.1634 +[epoch 39/50 ] (1.6s) loss=0.2712 L_disc=0.1646 +[epoch 40/50 ] (4.3s) loss=0.2721 auroc_term=0.994 auroc_disc=0.636 L_disc=0.1640 +[epoch 41/50 ] (1.5s) loss=0.2694 L_disc=0.1625 +[epoch 42/50 ] (1.7s) loss=0.2693 L_disc=0.1642 +[epoch 43/50 ] (1.6s) loss=0.2700 L_disc=0.1642 +[epoch 44/50 ] (1.7s) loss=0.2657 L_disc=0.1610 +[epoch 45/50 ] (1.6s) loss=0.2632 L_disc=0.1597 +[epoch 46/50 ] (1.5s) loss=0.2672 L_disc=0.1624 +[epoch 47/50 ] (1.5s) loss=0.2695 L_disc=0.1639 +[epoch 48/50 ] (1.5s) loss=0.2683 L_disc=0.1628 +[epoch 49/50 ] (1.5s) loss=0.2660 L_disc=0.1610 +[epoch 50/50 ] (4.3s) loss=0.2662 auroc_term=0.995 auroc_disc=0.680 L_disc=0.1615 +[saved] /home/chy/mambafortrafficmodeling/artifacts/route_comparison/route_ac_combo_iscxtor2016_seed42/model.pt diff --git a/artifacts/route_comparison/route_ac_combo_iscxtor2016_seed43.log b/artifacts/route_comparison/route_ac_combo_iscxtor2016_seed43.log new file mode 100644 index 0000000..18c5164 --- /dev/null +++ b/artifacts/route_comparison/route_ac_combo_iscxtor2016_seed43.log @@ -0,0 +1,58 @@ +Device: cuda seed=model:43/data:43 +[data] flows=/home/chy/mambafortrafficmodeling/datasets/iscxtor2016/processed/flows.parquet packets=/home/chy/mambafortrafficmodeling/datasets/iscxtor2016/processed/packets.npz +[data] kept 66,189 of 103,079 (min_len=2) +[data] train=51,901 val=12,976 attack=1,312 +[data] T=64 cont=3 disc=6 flow=20 train=51,901 val=12,976 attack=1,312 +[data] training on 10,000 flows +[model] params=1,227,809 token_dim=21 sigma=0.1 use_ot=True lambda_disc=1.0 +[epoch 1/50 ] (2.1s) loss=1.3076 L_disc=0.4549 +[epoch 2/50 ] (1.4s) loss=0.9314 L_disc=0.3308 +[epoch 3/50 ] (1.4s) loss=0.7183 L_disc=0.3095 +[epoch 4/50 ] (1.4s) loss=0.6044 L_disc=0.2891 +[epoch 5/50 ] (1.4s) loss=0.5339 L_disc=0.2786 +[epoch 6/50 ] (1.4s) loss=0.4813 L_disc=0.2643 +[epoch 7/50 ] (1.3s) loss=0.4508 L_disc=0.2532 +[epoch 8/50 ] (1.3s) loss=0.4191 L_disc=0.2387 +[epoch 9/50 ] (1.3s) loss=0.4026 L_disc=0.2304 +[epoch 10/50 ] (3.9s) loss=0.3872 auroc_term=0.981 auroc_disc=0.724 L_disc=0.2259 +[epoch 11/50 ] (1.6s) loss=0.3806 L_disc=0.2222 +[epoch 12/50 ] (1.7s) loss=0.3655 L_disc=0.2160 +[epoch 13/50 ] (1.6s) loss=0.3576 L_disc=0.2128 +[epoch 14/50 ] (1.6s) loss=0.3509 L_disc=0.2083 +[epoch 15/50 ] (1.6s) loss=0.3391 L_disc=0.2016 +[epoch 16/50 ] (1.7s) loss=0.3316 L_disc=0.1961 +[epoch 17/50 ] (1.5s) loss=0.3235 L_disc=0.1911 +[epoch 18/50 ] (1.6s) loss=0.3195 L_disc=0.1887 +[epoch 19/50 ] (1.5s) loss=0.3225 L_disc=0.1894 +[epoch 20/50 ] (4.0s) loss=0.3111 auroc_term=0.977 auroc_disc=0.657 L_disc=0.1855 +[epoch 21/50 ] (1.4s) loss=0.3063 L_disc=0.1822 +[epoch 22/50 ] (1.6s) loss=0.3012 L_disc=0.1783 +[epoch 23/50 ] (1.5s) loss=0.2992 L_disc=0.1790 +[epoch 24/50 ] (1.6s) loss=0.2934 L_disc=0.1749 +[epoch 25/50 ] (1.5s) loss=0.2903 L_disc=0.1734 +[epoch 26/50 ] (1.6s) loss=0.2881 L_disc=0.1700 +[epoch 27/50 ] (1.6s) loss=0.2846 L_disc=0.1699 +[epoch 28/50 ] (1.7s) loss=0.2823 L_disc=0.1686 +[epoch 29/50 ] (1.6s) loss=0.2834 L_disc=0.1701 +[epoch 30/50 ] (4.2s) loss=0.2815 auroc_term=0.993 auroc_disc=0.672 L_disc=0.1688 +[epoch 31/50 ] (1.4s) loss=0.2765 L_disc=0.1652 +[epoch 32/50 ] (1.5s) loss=0.2752 L_disc=0.1656 +[epoch 33/50 ] (1.6s) loss=0.2800 L_disc=0.1677 +[epoch 34/50 ] (1.7s) loss=0.2748 L_disc=0.1647 +[epoch 35/50 ] (1.5s) loss=0.2779 L_disc=0.1664 +[epoch 36/50 ] (1.6s) loss=0.2712 L_disc=0.1634 +[epoch 37/50 ] (1.7s) loss=0.2705 L_disc=0.1630 +[epoch 38/50 ] (1.7s) loss=0.2712 L_disc=0.1638 +[epoch 39/50 ] (1.6s) loss=0.2708 L_disc=0.1648 +[epoch 40/50 ] (4.3s) loss=0.2663 auroc_term=0.995 auroc_disc=0.704 L_disc=0.1610 +[epoch 41/50 ] (1.5s) loss=0.2672 L_disc=0.1605 +[epoch 42/50 ] (1.4s) loss=0.2668 L_disc=0.1612 +[epoch 43/50 ] (1.6s) loss=0.2668 L_disc=0.1614 +[epoch 44/50 ] (1.6s) loss=0.2647 L_disc=0.1609 +[epoch 45/50 ] (1.5s) loss=0.2656 L_disc=0.1599 +[epoch 46/50 ] (1.6s) loss=0.2675 L_disc=0.1617 +[epoch 47/50 ] (1.6s) loss=0.2650 L_disc=0.1609 +[epoch 48/50 ] (1.6s) loss=0.2685 L_disc=0.1620 +[epoch 49/50 ] (1.7s) loss=0.2645 L_disc=0.1595 +[epoch 50/50 ] (4.3s) loss=0.2637 auroc_term=0.995 auroc_disc=0.711 L_disc=0.1591 +[saved] /home/chy/mambafortrafficmodeling/artifacts/route_comparison/route_ac_combo_iscxtor2016_seed43/model.pt diff --git a/artifacts/route_comparison/route_ac_combo_iscxtor2016_seed44.log b/artifacts/route_comparison/route_ac_combo_iscxtor2016_seed44.log new file mode 100644 index 0000000..1fb50ca --- /dev/null +++ b/artifacts/route_comparison/route_ac_combo_iscxtor2016_seed44.log @@ -0,0 +1,58 @@ +Device: cuda seed=model:44/data:44 +[data] flows=/home/chy/mambafortrafficmodeling/datasets/iscxtor2016/processed/flows.parquet packets=/home/chy/mambafortrafficmodeling/datasets/iscxtor2016/processed/packets.npz +[data] kept 66,189 of 103,079 (min_len=2) +[data] train=51,901 val=12,976 attack=1,312 +[data] T=64 cont=3 disc=6 flow=20 train=51,901 val=12,976 attack=1,312 +[data] training on 10,000 flows +[model] params=1,227,809 token_dim=21 sigma=0.1 use_ot=True lambda_disc=1.0 +[epoch 1/50 ] (2.3s) loss=1.3142 L_disc=0.4635 +[epoch 2/50 ] (1.8s) loss=0.9383 L_disc=0.3372 +[epoch 3/50 ] (1.6s) loss=0.7307 L_disc=0.3152 +[epoch 4/50 ] (1.7s) loss=0.6097 L_disc=0.2950 +[epoch 5/50 ] (1.6s) loss=0.5325 L_disc=0.2807 +[epoch 6/50 ] (1.5s) loss=0.4878 L_disc=0.2681 +[epoch 7/50 ] (1.4s) loss=0.4522 L_disc=0.2530 +[epoch 8/50 ] (1.4s) loss=0.4242 L_disc=0.2416 +[epoch 9/50 ] (1.4s) loss=0.4055 L_disc=0.2331 +[epoch 10/50 ] (4.0s) loss=0.3890 auroc_term=0.971 auroc_disc=0.707 L_disc=0.2272 +[epoch 11/50 ] (1.4s) loss=0.3812 L_disc=0.2257 +[epoch 12/50 ] (1.4s) loss=0.3717 L_disc=0.2220 +[epoch 13/50 ] (1.3s) loss=0.3673 L_disc=0.2204 +[epoch 14/50 ] (1.4s) loss=0.3583 L_disc=0.2168 +[epoch 15/50 ] (1.3s) loss=0.3469 L_disc=0.2117 +[epoch 16/50 ] (1.4s) loss=0.3403 L_disc=0.2052 +[epoch 17/50 ] (1.4s) loss=0.3404 L_disc=0.2045 +[epoch 18/50 ] (1.6s) loss=0.3251 L_disc=0.1952 +[epoch 19/50 ] (1.5s) loss=0.3150 L_disc=0.1887 +[epoch 20/50 ] (4.2s) loss=0.3099 auroc_term=0.989 auroc_disc=0.632 L_disc=0.1861 +[epoch 21/50 ] (1.6s) loss=0.3074 L_disc=0.1849 +[epoch 22/50 ] (1.7s) loss=0.3036 L_disc=0.1830 +[epoch 23/50 ] (1.7s) loss=0.3009 L_disc=0.1808 +[epoch 24/50 ] (1.6s) loss=0.2952 L_disc=0.1772 +[epoch 25/50 ] (1.7s) loss=0.2957 L_disc=0.1780 +[epoch 26/50 ] (1.4s) loss=0.2919 L_disc=0.1757 +[epoch 27/50 ] (1.4s) loss=0.2887 L_disc=0.1746 +[epoch 28/50 ] (1.6s) loss=0.2880 L_disc=0.1734 +[epoch 29/50 ] (1.6s) loss=0.2872 L_disc=0.1740 +[epoch 30/50 ] (4.2s) loss=0.2805 auroc_term=0.995 auroc_disc=0.657 L_disc=0.1691 +[epoch 31/50 ] (1.6s) loss=0.2818 L_disc=0.1691 +[epoch 32/50 ] (1.6s) loss=0.2783 L_disc=0.1678 +[epoch 33/50 ] (1.6s) loss=0.2778 L_disc=0.1675 +[epoch 34/50 ] (1.6s) loss=0.2763 L_disc=0.1666 +[epoch 35/50 ] (1.6s) loss=0.2775 L_disc=0.1688 +[epoch 36/50 ] (1.6s) loss=0.2731 L_disc=0.1653 +[epoch 37/50 ] (1.4s) loss=0.2723 L_disc=0.1641 +[epoch 38/50 ] (1.5s) loss=0.2693 L_disc=0.1628 +[epoch 39/50 ] (1.7s) loss=0.2719 L_disc=0.1646 +[epoch 40/50 ] (4.4s) loss=0.2703 auroc_term=0.995 auroc_disc=0.719 L_disc=0.1626 +[epoch 41/50 ] (1.8s) loss=0.2685 L_disc=0.1628 +[epoch 42/50 ] (1.9s) loss=0.2651 L_disc=0.1618 +[epoch 43/50 ] (1.7s) loss=0.2697 L_disc=0.1637 +[epoch 44/50 ] (1.6s) loss=0.2664 L_disc=0.1609 +[epoch 45/50 ] (1.5s) loss=0.2664 L_disc=0.1615 +[epoch 46/50 ] (1.7s) loss=0.2684 L_disc=0.1628 +[epoch 47/50 ] (1.5s) loss=0.2679 L_disc=0.1615 +[epoch 48/50 ] (1.4s) loss=0.2685 L_disc=0.1628 +[epoch 49/50 ] (1.5s) loss=0.2653 L_disc=0.1602 +[epoch 50/50 ] (4.2s) loss=0.2685 auroc_term=0.996 auroc_disc=0.728 L_disc=0.1622 +[saved] /home/chy/mambafortrafficmodeling/artifacts/route_comparison/route_ac_combo_iscxtor2016_seed44/model.pt diff --git a/artifacts/route_comparison/route_ac_combo_seed42.log b/artifacts/route_comparison/route_ac_combo_seed42.log new file mode 100644 index 0000000..e7409e1 --- /dev/null +++ b/artifacts/route_comparison/route_ac_combo_seed42.log @@ -0,0 +1,58 @@ +Device: cuda seed=model:42/data:42 +[data] flows=/home/chy/mambafortrafficmodeling/datasets/ciciot2023/processed/full_store/flows.parquet packets=/home/chy/mambafortrafficmodeling/datasets/ciciot2023/processed/full_store +[data] kept 3,797,530 of 8,193,621 (min_len=2) +[data] train=77,636 val=10,000 attack=20,000 +[data] T=64 cont=3 disc=6 flow=20 train=77,636 val=10,000 attack=20,000 +[data] training on 10,000 flows +[model] params=1,227,809 token_dim=21 sigma=0.1 use_ot=True lambda_disc=1.0 +[epoch 1/50 ] (2.0s) loss=1.3451 L_disc=0.5037 +[epoch 2/50 ] (1.7s) loss=1.0190 L_disc=0.4033 +[epoch 3/50 ] (2.3s) loss=0.8192 L_disc=0.3748 +[epoch 4/50 ] (2.5s) loss=0.6682 L_disc=0.3369 +[epoch 5/50 ] (2.5s) loss=0.5787 L_disc=0.3063 +[epoch 6/50 ] (2.5s) loss=0.5261 L_disc=0.2864 +[epoch 7/50 ] (2.4s) loss=0.4914 L_disc=0.2750 +[epoch 8/50 ] (2.8s) loss=0.4579 L_disc=0.2578 +[epoch 9/50 ] (2.6s) loss=0.4391 L_disc=0.2489 +[epoch 10/50 ] (13.2s) loss=0.4235 auroc_term=0.938 auroc_disc=0.811 L_disc=0.2427 +[epoch 11/50 ] (2.6s) loss=0.4009 L_disc=0.2303 +[epoch 12/50 ] (2.8s) loss=0.3990 L_disc=0.2305 +[epoch 13/50 ] (2.5s) loss=0.3869 L_disc=0.2205 +[epoch 14/50 ] (2.4s) loss=0.3739 L_disc=0.2157 +[epoch 15/50 ] (2.5s) loss=0.3571 L_disc=0.2069 +[epoch 16/50 ] (2.5s) loss=0.3455 L_disc=0.1988 +[epoch 17/50 ] (2.3s) loss=0.3432 L_disc=0.1970 +[epoch 18/50 ] (2.2s) loss=0.3434 L_disc=0.1999 +[epoch 19/50 ] (2.3s) loss=0.3390 L_disc=0.2005 +[epoch 20/50 ] (13.2s) loss=0.3335 auroc_term=0.950 auroc_disc=0.876 L_disc=0.1945 +[epoch 21/50 ] (2.6s) loss=0.3291 L_disc=0.1914 +[epoch 22/50 ] (2.4s) loss=0.3173 L_disc=0.1845 +[epoch 23/50 ] (2.0s) loss=0.3131 L_disc=0.1830 +[epoch 24/50 ] (2.3s) loss=0.3120 L_disc=0.1823 +[epoch 25/50 ] (2.6s) loss=0.3085 L_disc=0.1809 +[epoch 26/50 ] (2.6s) loss=0.3031 L_disc=0.1764 +[epoch 27/50 ] (2.6s) loss=0.3081 L_disc=0.1815 +[epoch 28/50 ] (2.5s) loss=0.3035 L_disc=0.1770 +[epoch 29/50 ] (2.5s) loss=0.2966 L_disc=0.1741 +[epoch 30/50 ] (13.5s) loss=0.2969 auroc_term=0.957 auroc_disc=0.908 L_disc=0.1747 +[epoch 31/50 ] (2.5s) loss=0.2938 L_disc=0.1712 +[epoch 32/50 ] (2.5s) loss=0.2877 L_disc=0.1672 +[epoch 33/50 ] (2.3s) loss=0.2951 L_disc=0.1749 +[epoch 34/50 ] (2.3s) loss=0.2890 L_disc=0.1693 +[epoch 35/50 ] (2.4s) loss=0.2911 L_disc=0.1720 +[epoch 36/50 ] (2.4s) loss=0.2823 L_disc=0.1660 +[epoch 37/50 ] (2.4s) loss=0.2804 L_disc=0.1642 +[epoch 38/50 ] (2.4s) loss=0.2808 L_disc=0.1623 +[epoch 39/50 ] (2.3s) loss=0.2834 L_disc=0.1685 +[epoch 40/50 ] (13.3s) loss=0.2775 auroc_term=0.961 auroc_disc=0.912 L_disc=0.1621 +[epoch 41/50 ] (2.1s) loss=0.2806 L_disc=0.1651 +[epoch 42/50 ] (2.1s) loss=0.2768 L_disc=0.1640 +[epoch 43/50 ] (2.1s) loss=0.2789 L_disc=0.1655 +[epoch 44/50 ] (2.0s) loss=0.2731 L_disc=0.1607 +[epoch 45/50 ] (2.0s) loss=0.2733 L_disc=0.1604 +[epoch 46/50 ] (2.1s) loss=0.2753 L_disc=0.1624 +[epoch 47/50 ] (2.0s) loss=0.2779 L_disc=0.1657 +[epoch 48/50 ] (2.2s) loss=0.2782 L_disc=0.1654 +[epoch 49/50 ] (2.1s) loss=0.2804 L_disc=0.1686 +[epoch 50/50 ] (13.3s) loss=0.2731 auroc_term=0.961 auroc_disc=0.914 L_disc=0.1614 +[saved] /home/chy/mambafortrafficmodeling/artifacts/route_comparison/route_ac_combo_ciciot2023_seed42/model.pt diff --git a/artifacts/route_comparison/route_ac_combo_seed43.log b/artifacts/route_comparison/route_ac_combo_seed43.log new file mode 100644 index 0000000..8a49ae5 --- /dev/null +++ b/artifacts/route_comparison/route_ac_combo_seed43.log @@ -0,0 +1,58 @@ +Device: cuda seed=model:43/data:43 +[data] flows=/home/chy/mambafortrafficmodeling/datasets/ciciot2023/processed/full_store/flows.parquet packets=/home/chy/mambafortrafficmodeling/datasets/ciciot2023/processed/full_store +[data] kept 3,797,530 of 8,193,621 (min_len=2) +[data] train=77,636 val=10,000 attack=20,000 +[data] T=64 cont=3 disc=6 flow=20 train=77,636 val=10,000 attack=20,000 +[data] training on 10,000 flows +[model] params=1,227,809 token_dim=21 sigma=0.1 use_ot=True lambda_disc=1.0 +[epoch 1/50 ] (2.8s) loss=1.3526 L_disc=0.5016 +[epoch 2/50 ] (2.5s) loss=1.0229 L_disc=0.4022 +[epoch 3/50 ] (2.6s) loss=0.8147 L_disc=0.3781 +[epoch 4/50 ] (2.4s) loss=0.6833 L_disc=0.3436 +[epoch 5/50 ] (2.3s) loss=0.5983 L_disc=0.3159 +[epoch 6/50 ] (2.6s) loss=0.5383 L_disc=0.2944 +[epoch 7/50 ] (2.4s) loss=0.5056 L_disc=0.2836 +[epoch 8/50 ] (2.7s) loss=0.4754 L_disc=0.2685 +[epoch 9/50 ] (2.5s) loss=0.4531 L_disc=0.2576 +[epoch 10/50 ] (12.9s) loss=0.4327 auroc_term=0.947 auroc_disc=0.861 L_disc=0.2499 +[epoch 11/50 ] (2.7s) loss=0.4261 L_disc=0.2436 +[epoch 12/50 ] (2.7s) loss=0.3999 L_disc=0.2284 +[epoch 13/50 ] (2.3s) loss=0.3958 L_disc=0.2303 +[epoch 14/50 ] (2.6s) loss=0.3744 L_disc=0.2158 +[epoch 15/50 ] (2.4s) loss=0.3720 L_disc=0.2130 +[epoch 16/50 ] (2.4s) loss=0.3664 L_disc=0.2117 +[epoch 17/50 ] (2.1s) loss=0.3570 L_disc=0.2073 +[epoch 18/50 ] (2.3s) loss=0.3501 L_disc=0.2032 +[epoch 19/50 ] (2.4s) loss=0.3436 L_disc=0.2000 +[epoch 20/50 ] (13.3s) loss=0.3378 auroc_term=0.956 auroc_disc=0.875 L_disc=0.1949 +[epoch 21/50 ] (2.4s) loss=0.3301 L_disc=0.1905 +[epoch 22/50 ] (2.3s) loss=0.3305 L_disc=0.1924 +[epoch 23/50 ] (2.2s) loss=0.3250 L_disc=0.1896 +[epoch 24/50 ] (2.6s) loss=0.3157 L_disc=0.1827 +[epoch 25/50 ] (2.4s) loss=0.3155 L_disc=0.1833 +[epoch 26/50 ] (2.6s) loss=0.3173 L_disc=0.1854 +[epoch 27/50 ] (2.4s) loss=0.3134 L_disc=0.1846 +[epoch 28/50 ] (2.4s) loss=0.3018 L_disc=0.1744 +[epoch 29/50 ] (2.6s) loss=0.3007 L_disc=0.1744 +[epoch 30/50 ] (13.3s) loss=0.2973 auroc_term=0.953 auroc_disc=0.891 L_disc=0.1725 +[epoch 31/50 ] (2.5s) loss=0.2938 L_disc=0.1690 +[epoch 32/50 ] (2.3s) loss=0.2986 L_disc=0.1741 +[epoch 33/50 ] (2.3s) loss=0.2964 L_disc=0.1737 +[epoch 34/50 ] (2.4s) loss=0.2916 L_disc=0.1690 +[epoch 35/50 ] (2.5s) loss=0.2920 L_disc=0.1695 +[epoch 36/50 ] (2.6s) loss=0.2917 L_disc=0.1725 +[epoch 37/50 ] (2.5s) loss=0.2889 L_disc=0.1704 +[epoch 38/50 ] (2.3s) loss=0.2858 L_disc=0.1666 +[epoch 39/50 ] (2.2s) loss=0.2864 L_disc=0.1684 +[epoch 40/50 ] (13.2s) loss=0.2826 auroc_term=0.957 auroc_disc=0.902 L_disc=0.1667 +[epoch 41/50 ] (2.1s) loss=0.2873 L_disc=0.1675 +[epoch 42/50 ] (2.1s) loss=0.2897 L_disc=0.1713 +[epoch 43/50 ] (2.0s) loss=0.2848 L_disc=0.1680 +[epoch 44/50 ] (2.2s) loss=0.2852 L_disc=0.1693 +[epoch 45/50 ] (2.0s) loss=0.2839 L_disc=0.1664 +[epoch 46/50 ] (2.0s) loss=0.2832 L_disc=0.1668 +[epoch 47/50 ] (2.2s) loss=0.2778 L_disc=0.1644 +[epoch 48/50 ] (2.1s) loss=0.2858 L_disc=0.1681 +[epoch 49/50 ] (2.2s) loss=0.2841 L_disc=0.1665 +[epoch 50/50 ] (12.2s) loss=0.2803 auroc_term=0.958 auroc_disc=0.896 L_disc=0.1645 +[saved] /home/chy/mambafortrafficmodeling/artifacts/route_comparison/route_ac_combo_ciciot2023_seed43/model.pt diff --git a/artifacts/route_comparison/route_ac_combo_seed44.log b/artifacts/route_comparison/route_ac_combo_seed44.log new file mode 100644 index 0000000..06eaf0d --- /dev/null +++ b/artifacts/route_comparison/route_ac_combo_seed44.log @@ -0,0 +1,58 @@ +Device: cuda seed=model:44/data:44 +[data] flows=/home/chy/mambafortrafficmodeling/datasets/ciciot2023/processed/full_store/flows.parquet packets=/home/chy/mambafortrafficmodeling/datasets/ciciot2023/processed/full_store +[data] kept 3,797,530 of 8,193,621 (min_len=2) +[data] train=77,636 val=10,000 attack=20,000 +[data] T=64 cont=3 disc=6 flow=20 train=77,636 val=10,000 attack=20,000 +[data] training on 10,000 flows +[model] params=1,227,809 token_dim=21 sigma=0.1 use_ot=True lambda_disc=1.0 +[epoch 1/50 ] (2.8s) loss=1.3564 L_disc=0.5045 +[epoch 2/50 ] (2.3s) loss=1.0204 L_disc=0.4016 +[epoch 3/50 ] (2.3s) loss=0.8221 L_disc=0.3784 +[epoch 4/50 ] (2.2s) loss=0.6901 L_disc=0.3475 +[epoch 5/50 ] (2.2s) loss=0.5952 L_disc=0.3093 +[epoch 6/50 ] (2.3s) loss=0.5358 L_disc=0.2867 +[epoch 7/50 ] (2.2s) loss=0.5014 L_disc=0.2729 +[epoch 8/50 ] (2.1s) loss=0.4773 L_disc=0.2672 +[epoch 9/50 ] (1.9s) loss=0.4561 L_disc=0.2530 +[epoch 10/50 ] (7.1s) loss=0.4294 auroc_term=0.942 auroc_disc=0.847 L_disc=0.2410 +[epoch 11/50 ] (1.5s) loss=0.4165 L_disc=0.2326 +[epoch 12/50 ] (1.6s) loss=0.3969 L_disc=0.2230 +[epoch 13/50 ] (1.7s) loss=0.3970 L_disc=0.2259 +[epoch 14/50 ] (2.0s) loss=0.3888 L_disc=0.2208 +[epoch 15/50 ] (2.1s) loss=0.3712 L_disc=0.2116 +[epoch 16/50 ] (2.2s) loss=0.3681 L_disc=0.2095 +[epoch 17/50 ] (2.2s) loss=0.3562 L_disc=0.2044 +[epoch 18/50 ] (2.2s) loss=0.3501 L_disc=0.2004 +[epoch 19/50 ] (2.2s) loss=0.3467 L_disc=0.1997 +[epoch 20/50 ] (7.8s) loss=0.3345 auroc_term=0.952 auroc_disc=0.879 L_disc=0.1913 +[epoch 21/50 ] (1.9s) loss=0.3390 L_disc=0.1943 +[epoch 22/50 ] (1.7s) loss=0.3296 L_disc=0.1878 +[epoch 23/50 ] (1.6s) loss=0.3245 L_disc=0.1855 +[epoch 24/50 ] (1.4s) loss=0.3262 L_disc=0.1877 +[epoch 25/50 ] (1.5s) loss=0.3189 L_disc=0.1840 +[epoch 26/50 ] (1.5s) loss=0.3114 L_disc=0.1780 +[epoch 27/50 ] (1.5s) loss=0.3097 L_disc=0.1778 +[epoch 28/50 ] (1.6s) loss=0.3124 L_disc=0.1790 +[epoch 29/50 ] (1.7s) loss=0.2988 L_disc=0.1711 +[epoch 30/50 ] (7.7s) loss=0.3018 auroc_term=0.956 auroc_disc=0.873 L_disc=0.1736 +[epoch 31/50 ] (2.0s) loss=0.3028 L_disc=0.1730 +[epoch 32/50 ] (2.2s) loss=0.3024 L_disc=0.1756 +[epoch 33/50 ] (2.2s) loss=0.2964 L_disc=0.1710 +[epoch 34/50 ] (2.2s) loss=0.2962 L_disc=0.1715 +[epoch 35/50 ] (2.1s) loss=0.2889 L_disc=0.1662 +[epoch 36/50 ] (2.1s) loss=0.2888 L_disc=0.1664 +[epoch 37/50 ] (2.1s) loss=0.2921 L_disc=0.1696 +[epoch 38/50 ] (1.8s) loss=0.2934 L_disc=0.1721 +[epoch 39/50 ] (1.6s) loss=0.2890 L_disc=0.1659 +[epoch 40/50 ] (6.9s) loss=0.2872 auroc_term=0.956 auroc_disc=0.887 L_disc=0.1651 +[epoch 41/50 ] (1.6s) loss=0.2825 L_disc=0.1634 +[epoch 42/50 ] (1.8s) loss=0.2820 L_disc=0.1642 +[epoch 43/50 ] (2.1s) loss=0.2806 L_disc=0.1628 +[epoch 44/50 ] (2.2s) loss=0.2877 L_disc=0.1677 +[epoch 45/50 ] (2.3s) loss=0.2879 L_disc=0.1668 +[epoch 46/50 ] (2.2s) loss=0.2792 L_disc=0.1614 +[epoch 47/50 ] (2.2s) loss=0.2815 L_disc=0.1619 +[epoch 48/50 ] (2.3s) loss=0.2847 L_disc=0.1660 +[epoch 49/50 ] (2.2s) loss=0.2855 L_disc=0.1661 +[epoch 50/50 ] (7.8s) loss=0.2830 auroc_term=0.957 auroc_disc=0.892 L_disc=0.1640 +[saved] /home/chy/mambafortrafficmodeling/artifacts/route_comparison/route_ac_combo_ciciot2023_seed44/model.pt diff --git a/artifacts/route_comparison/route_b_spectral_seed42.log b/artifacts/route_comparison/route_b_spectral_seed42.log new file mode 100644 index 0000000..a68abc9 --- /dev/null +++ b/artifacts/route_comparison/route_b_spectral_seed42.log @@ -0,0 +1,61 @@ +Device: cuda +[seed] model=42 data=42 +[data] flows=/home/chy/mambafortrafficmodeling/datasets/ciciot2023/processed/full_store/flows.parquet packets_source=/home/chy/mambafortrafficmodeling/datasets/ciciot2023/processed/full_store +[data] using external flow features D=52 +[data] rows total=8,193,621 keep len>=2: 3,797,530 +[data] benign=97,045 attack=20,000 -> train=77,636 val=10,000 +[data] T=64 packet_D=9 flow_D=52 train=77,636 val=10,000 attack=20,000 +[data] using 10,000 benign training flows +[model] params=1,234,485 token_dim=53 seq_len=65 sigma=0.1 use_ot=True reference_mode=None +[loss] λ_flow=0.3 λ_packet=0.3 packet_mask_ratio=0.5 +[epoch 1/50 ] (2.8s) loss=2.3544 aux_flow=3.1786 aux_pkt=0.9661 +[epoch 2/50 ] (4.0s) loss=2.0830 aux_flow=2.7865 aux_pkt=0.9686 +[epoch 3/50 ] (4.0s) loss=1.8203 aux_flow=2.4385 aux_pkt=0.9612 +[epoch 4/50 ] (4.0s) loss=1.6441 aux_flow=2.2780 aux_pkt=0.9528 +[epoch 5/50 ] (4.0s) loss=1.5232 aux_flow=2.1756 aux_pkt=0.9528 +[epoch 6/50 ] (4.0s) loss=1.4129 aux_flow=2.0367 aux_pkt=0.9481 +[epoch 7/50 ] (4.0s) loss=1.3444 aux_flow=1.9742 aux_pkt=0.9498 +[epoch 8/50 ] (4.0s) loss=1.2819 aux_flow=1.8964 aux_pkt=0.9474 +[epoch 9/50 ] (3.9s) loss=1.2370 aux_flow=1.8439 aux_pkt=0.9488 +[epoch 10/50 ] (40.6s) loss=1.2030 auroc_terminal=0.933 aux_flow=1.8201 aux_pkt=0.9431 +[epoch 11/50 ] (3.0s) loss=1.1606 aux_flow=1.7593 aux_pkt=0.9459 +[epoch 12/50 ] (4.0s) loss=1.1252 aux_flow=1.6987 aux_pkt=0.9423 +[epoch 13/50 ] (4.0s) loss=1.1246 aux_flow=1.7348 aux_pkt=0.9390 +[epoch 14/50 ] (4.0s) loss=1.0960 aux_flow=1.6805 aux_pkt=0.9436 +[epoch 15/50 ] (4.0s) loss=1.0787 aux_flow=1.6671 aux_pkt=0.9424 +[epoch 16/50 ] (4.1s) loss=1.0653 aux_flow=1.6495 aux_pkt=0.9405 +[epoch 17/50 ] (4.0s) loss=1.0593 aux_flow=1.6453 aux_pkt=0.9375 +[epoch 18/50 ] (4.0s) loss=1.0483 aux_flow=1.6232 aux_pkt=0.9380 +[epoch 19/50 ] (3.9s) loss=1.0290 aux_flow=1.6002 aux_pkt=0.9406 +[epoch 20/50 ] (40.5s) loss=1.0214 auroc_terminal=0.954 aux_flow=1.5838 aux_pkt=0.9412 +[epoch 21/50 ] (3.3s) loss=1.0239 aux_flow=1.6022 aux_pkt=0.9398 +[epoch 22/50 ] (4.0s) loss=1.0107 aux_flow=1.5874 aux_pkt=0.9362 +[epoch 23/50 ] (4.0s) loss=0.9994 aux_flow=1.5565 aux_pkt=0.9413 +[epoch 24/50 ] (4.0s) loss=0.9897 aux_flow=1.5394 aux_pkt=0.9374 +[epoch 25/50 ] (4.0s) loss=0.9888 aux_flow=1.5502 aux_pkt=0.9348 +[epoch 26/50 ] (3.9s) loss=0.9864 aux_flow=1.5475 aux_pkt=0.9373 +[epoch 27/50 ] (4.0s) loss=0.9821 aux_flow=1.5463 aux_pkt=0.9396 +[epoch 28/50 ] (3.9s) loss=0.9691 aux_flow=1.5212 aux_pkt=0.9353 +[epoch 29/50 ] (4.0s) loss=0.9669 aux_flow=1.5096 aux_pkt=0.9401 +[epoch 30/50 ] (40.2s) loss=0.9735 auroc_terminal=0.963 aux_flow=1.5328 aux_pkt=0.9407 +[epoch 31/50 ] (3.3s) loss=0.9612 aux_flow=1.5108 aux_pkt=0.9326 +[epoch 32/50 ] (4.0s) loss=0.9627 aux_flow=1.5228 aux_pkt=0.9332 +[epoch 33/50 ] (4.0s) loss=0.9654 aux_flow=1.5278 aux_pkt=0.9370 +[epoch 34/50 ] (3.9s) loss=0.9489 aux_flow=1.4877 aux_pkt=0.9357 +[epoch 35/50 ] (4.0s) loss=0.9475 aux_flow=1.4857 aux_pkt=0.9359 +[epoch 36/50 ] (3.9s) loss=0.9579 aux_flow=1.5192 aux_pkt=0.9345 +[epoch 37/50 ] (4.0s) loss=0.9397 aux_flow=1.4714 aux_pkt=0.9379 +[epoch 38/50 ] (4.1s) loss=0.9407 aux_flow=1.4776 aux_pkt=0.9323 +[epoch 39/50 ] (3.9s) loss=0.9388 aux_flow=1.4773 aux_pkt=0.9347 +[epoch 40/50 ] (40.1s) loss=0.9401 auroc_terminal=0.965 aux_flow=1.4798 aux_pkt=0.9325 +[epoch 41/50 ] (3.7s) loss=0.9425 aux_flow=1.4853 aux_pkt=0.9376 +[epoch 42/50 ] (3.9s) loss=0.9361 aux_flow=1.4683 aux_pkt=0.9348 +[epoch 43/50 ] (4.0s) loss=0.9382 aux_flow=1.4798 aux_pkt=0.9327 +[epoch 44/50 ] (3.9s) loss=0.9292 aux_flow=1.4476 aux_pkt=0.9336 +[epoch 45/50 ] (3.9s) loss=0.9373 aux_flow=1.4664 aux_pkt=0.9363 +[epoch 46/50 ] (3.9s) loss=0.9421 aux_flow=1.4845 aux_pkt=0.9368 +[epoch 47/50 ] (3.9s) loss=0.9305 aux_flow=1.4641 aux_pkt=0.9332 +[epoch 48/50 ] (3.9s) loss=0.9369 aux_flow=1.4747 aux_pkt=0.9344 +[epoch 49/50 ] (4.0s) loss=0.9330 aux_flow=1.4635 aux_pkt=0.9330 +[epoch 50/50 ] (40.0s) loss=0.9413 auroc_terminal=0.965 aux_flow=1.4829 aux_pkt=0.9368 +[saved] /home/chy/mambafortrafficmodeling/artifacts/route_comparison/route_b_spectral_ciciot2023_seed42/model.pt diff --git a/artifacts/route_comparison/route_b_spectral_seed43.log b/artifacts/route_comparison/route_b_spectral_seed43.log new file mode 100644 index 0000000..2c905e6 --- /dev/null +++ b/artifacts/route_comparison/route_b_spectral_seed43.log @@ -0,0 +1,61 @@ +Device: cuda +[seed] model=43 data=43 +[data] flows=/home/chy/mambafortrafficmodeling/datasets/ciciot2023/processed/full_store/flows.parquet packets_source=/home/chy/mambafortrafficmodeling/datasets/ciciot2023/processed/full_store +[data] using external flow features D=52 +[data] rows total=8,193,621 keep len>=2: 3,797,530 +[data] benign=97,045 attack=20,000 -> train=77,636 val=10,000 +[data] T=64 packet_D=9 flow_D=52 train=77,636 val=10,000 attack=20,000 +[data] using 10,000 benign training flows +[model] params=1,234,485 token_dim=53 seq_len=65 sigma=0.1 use_ot=True reference_mode=None +[loss] λ_flow=0.3 λ_packet=0.3 packet_mask_ratio=0.5 +[epoch 1/50 ] (4.5s) loss=2.4201 aux_flow=3.3744 aux_pkt=0.9734 +[epoch 2/50 ] (4.0s) loss=2.0997 aux_flow=2.8514 aux_pkt=0.9646 +[epoch 3/50 ] (4.0s) loss=1.8899 aux_flow=2.6628 aux_pkt=0.9609 +[epoch 4/50 ] (4.0s) loss=1.6781 aux_flow=2.3786 aux_pkt=0.9600 +[epoch 5/50 ] (4.0s) loss=1.5686 aux_flow=2.2909 aux_pkt=0.9661 +[epoch 6/50 ] (4.0s) loss=1.4405 aux_flow=2.1185 aux_pkt=0.9573 +[epoch 7/50 ] (4.0s) loss=1.3880 aux_flow=2.0996 aux_pkt=0.9595 +[epoch 8/50 ] (3.9s) loss=1.3128 aux_flow=1.9850 aux_pkt=0.9541 +[epoch 9/50 ] (3.9s) loss=1.2755 aux_flow=1.9430 aux_pkt=0.9513 +[epoch 10/50 ] (39.1s) loss=1.2268 auroc_terminal=0.920 aux_flow=1.8673 aux_pkt=0.9498 +[epoch 11/50 ] (4.0s) loss=1.2071 aux_flow=1.8713 aux_pkt=0.9489 +[epoch 12/50 ] (4.0s) loss=1.1613 aux_flow=1.7944 aux_pkt=0.9456 +[epoch 13/50 ] (4.0s) loss=1.1536 aux_flow=1.7997 aux_pkt=0.9485 +[epoch 14/50 ] (4.0s) loss=1.1315 aux_flow=1.7690 aux_pkt=0.9453 +[epoch 15/50 ] (4.1s) loss=1.1274 aux_flow=1.7830 aux_pkt=0.9437 +[epoch 16/50 ] (4.0s) loss=1.1027 aux_flow=1.7480 aux_pkt=0.9458 +[epoch 17/50 ] (3.9s) loss=1.0860 aux_flow=1.7196 aux_pkt=0.9463 +[epoch 18/50 ] (3.9s) loss=1.0883 aux_flow=1.7458 aux_pkt=0.9437 +[epoch 19/50 ] (3.9s) loss=1.0673 aux_flow=1.6918 aux_pkt=0.9388 +[epoch 20/50 ] (38.8s) loss=1.0633 auroc_terminal=0.952 aux_flow=1.6942 aux_pkt=0.9414 +[epoch 21/50 ] (4.0s) loss=1.0518 aux_flow=1.6818 aux_pkt=0.9417 +[epoch 22/50 ] (4.0s) loss=1.0546 aux_flow=1.7053 aux_pkt=0.9434 +[epoch 23/50 ] (4.1s) loss=1.0507 aux_flow=1.6968 aux_pkt=0.9414 +[epoch 24/50 ] (4.0s) loss=1.0281 aux_flow=1.6344 aux_pkt=0.9364 +[epoch 25/50 ] (4.0s) loss=1.0245 aux_flow=1.6365 aux_pkt=0.9423 +[epoch 26/50 ] (4.0s) loss=1.0075 aux_flow=1.6021 aux_pkt=0.9414 +[epoch 27/50 ] (3.9s) loss=1.0144 aux_flow=1.6296 aux_pkt=0.9389 +[epoch 28/50 ] (3.9s) loss=1.0041 aux_flow=1.5959 aux_pkt=0.9443 +[epoch 29/50 ] (4.0s) loss=1.0105 aux_flow=1.6301 aux_pkt=0.9431 +[epoch 30/50 ] (39.3s) loss=0.9935 auroc_terminal=0.958 aux_flow=1.5859 aux_pkt=0.9375 +[epoch 31/50 ] (4.0s) loss=1.0036 aux_flow=1.6218 aux_pkt=0.9375 +[epoch 32/50 ] (4.0s) loss=0.9917 aux_flow=1.5974 aux_pkt=0.9335 +[epoch 33/50 ] (3.9s) loss=0.9823 aux_flow=1.5637 aux_pkt=0.9417 +[epoch 34/50 ] (4.0s) loss=0.9800 aux_flow=1.5719 aux_pkt=0.9344 +[epoch 35/50 ] (3.9s) loss=0.9890 aux_flow=1.6022 aux_pkt=0.9372 +[epoch 36/50 ] (3.9s) loss=0.9704 aux_flow=1.5508 aux_pkt=0.9359 +[epoch 37/50 ] (4.0s) loss=0.9831 aux_flow=1.5802 aux_pkt=0.9383 +[epoch 38/50 ] (4.0s) loss=0.9879 aux_flow=1.6067 aux_pkt=0.9354 +[epoch 39/50 ] (3.9s) loss=0.9685 aux_flow=1.5418 aux_pkt=0.9410 +[epoch 40/50 ] (38.8s) loss=0.9719 auroc_terminal=0.962 aux_flow=1.5721 aux_pkt=0.9330 +[epoch 41/50 ] (3.9s) loss=0.9679 aux_flow=1.5551 aux_pkt=0.9360 +[epoch 42/50 ] (3.9s) loss=0.9539 aux_flow=1.5209 aux_pkt=0.9356 +[epoch 43/50 ] (4.0s) loss=0.9706 aux_flow=1.5551 aux_pkt=0.9389 +[epoch 44/50 ] (3.9s) loss=0.9644 aux_flow=1.5438 aux_pkt=0.9385 +[epoch 45/50 ] (3.9s) loss=0.9691 aux_flow=1.5552 aux_pkt=0.9378 +[epoch 46/50 ] (3.9s) loss=0.9614 aux_flow=1.5372 aux_pkt=0.9367 +[epoch 47/50 ] (4.0s) loss=0.9625 aux_flow=1.5373 aux_pkt=0.9377 +[epoch 48/50 ] (3.9s) loss=0.9547 aux_flow=1.5161 aux_pkt=0.9365 +[epoch 49/50 ] (3.9s) loss=0.9616 aux_flow=1.5415 aux_pkt=0.9370 +[epoch 50/50 ] (39.3s) loss=0.9600 auroc_terminal=0.963 aux_flow=1.5274 aux_pkt=0.9456 +[saved] /home/chy/mambafortrafficmodeling/artifacts/route_comparison/route_b_spectral_ciciot2023_seed43/model.pt diff --git a/artifacts/route_comparison/route_b_spectral_seed44.log b/artifacts/route_comparison/route_b_spectral_seed44.log new file mode 100644 index 0000000..dbd153e --- /dev/null +++ b/artifacts/route_comparison/route_b_spectral_seed44.log @@ -0,0 +1,61 @@ +Device: cuda +[seed] model=44 data=44 +[data] flows=/home/chy/mambafortrafficmodeling/datasets/ciciot2023/processed/full_store/flows.parquet packets_source=/home/chy/mambafortrafficmodeling/datasets/ciciot2023/processed/full_store +[data] using external flow features D=52 +[data] rows total=8,193,621 keep len>=2: 3,797,530 +[data] benign=97,045 attack=20,000 -> train=77,636 val=10,000 +[data] T=64 packet_D=9 flow_D=52 train=77,636 val=10,000 attack=20,000 +[data] using 10,000 benign training flows +[model] params=1,234,485 token_dim=53 seq_len=65 sigma=0.1 use_ot=True reference_mode=None +[loss] λ_flow=0.3 λ_packet=0.3 packet_mask_ratio=0.5 +[epoch 1/50 ] (2.8s) loss=2.4962 aux_flow=3.6093 aux_pkt=0.9787 +[epoch 2/50 ] (2.3s) loss=2.1768 aux_flow=3.0675 aux_pkt=0.9691 +[epoch 3/50 ] (2.3s) loss=1.9341 aux_flow=2.7710 aux_pkt=0.9656 +[epoch 4/50 ] (2.2s) loss=1.7438 aux_flow=2.5494 aux_pkt=0.9627 +[epoch 5/50 ] (2.3s) loss=1.5846 aux_flow=2.3368 aux_pkt=0.9598 +[epoch 6/50 ] (2.3s) loss=1.5026 aux_flow=2.2796 aux_pkt=0.9618 +[epoch 7/50 ] (2.3s) loss=1.3985 aux_flow=2.1222 aux_pkt=0.9540 +[epoch 8/50 ] (2.3s) loss=1.3477 aux_flow=2.0648 aux_pkt=0.9608 +[epoch 9/50 ] (2.3s) loss=1.3081 aux_flow=2.0368 aux_pkt=0.9555 +[epoch 10/50 ] (17.0s) loss=1.2861 auroc_terminal=0.922 aux_flow=2.0402 aux_pkt=0.9597 +[epoch 11/50 ] (2.2s) loss=1.2449 aux_flow=1.9686 aux_pkt=0.9484 +[epoch 12/50 ] (2.2s) loss=1.2067 aux_flow=1.8993 aux_pkt=0.9454 +[epoch 13/50 ] (2.2s) loss=1.1773 aux_flow=1.8377 aux_pkt=0.9521 +[epoch 14/50 ] (2.2s) loss=1.1562 aux_flow=1.8204 aux_pkt=0.9454 +[epoch 15/50 ] (2.2s) loss=1.1414 aux_flow=1.8006 aux_pkt=0.9505 +[epoch 16/50 ] (2.2s) loss=1.1194 aux_flow=1.7702 aux_pkt=0.9474 +[epoch 17/50 ] (2.2s) loss=1.1146 aux_flow=1.7713 aux_pkt=0.9532 +[epoch 18/50 ] (2.1s) loss=1.1101 aux_flow=1.7801 aux_pkt=0.9501 +[epoch 19/50 ] (2.2s) loss=1.0890 aux_flow=1.7346 aux_pkt=0.9440 +[epoch 20/50 ] (16.4s) loss=1.0941 auroc_terminal=0.953 aux_flow=1.7565 aux_pkt=0.9523 +[epoch 21/50 ] (2.2s) loss=1.0880 aux_flow=1.7606 aux_pkt=0.9453 +[epoch 22/50 ] (2.3s) loss=1.0633 aux_flow=1.7103 aux_pkt=0.9435 +[epoch 23/50 ] (2.3s) loss=1.0621 aux_flow=1.7152 aux_pkt=0.9426 +[epoch 24/50 ] (2.2s) loss=1.0594 aux_flow=1.7000 aux_pkt=0.9493 +[epoch 25/50 ] (2.2s) loss=1.0447 aux_flow=1.6813 aux_pkt=0.9492 +[epoch 26/50 ] (2.2s) loss=1.0395 aux_flow=1.6704 aux_pkt=0.9471 +[epoch 27/50 ] (2.3s) loss=1.0303 aux_flow=1.6538 aux_pkt=0.9418 +[epoch 28/50 ] (2.3s) loss=1.0136 aux_flow=1.6232 aux_pkt=0.9427 +[epoch 29/50 ] (2.3s) loss=1.0252 aux_flow=1.6551 aux_pkt=0.9450 +[epoch 30/50 ] (17.0s) loss=1.0092 auroc_terminal=0.955 aux_flow=1.6198 aux_pkt=0.9407 +[epoch 31/50 ] (2.2s) loss=1.0127 aux_flow=1.6250 aux_pkt=0.9485 +[epoch 32/50 ] (2.1s) loss=0.9991 aux_flow=1.6026 aux_pkt=0.9422 +[epoch 33/50 ] (2.2s) loss=0.9913 aux_flow=1.5832 aux_pkt=0.9401 +[epoch 34/50 ] (2.2s) loss=1.0053 aux_flow=1.6317 aux_pkt=0.9361 +[epoch 35/50 ] (2.2s) loss=0.9960 aux_flow=1.6005 aux_pkt=0.9456 +[epoch 36/50 ] (2.2s) loss=0.9959 aux_flow=1.6043 aux_pkt=0.9472 +[epoch 37/50 ] (2.2s) loss=0.9913 aux_flow=1.6011 aux_pkt=0.9417 +[epoch 38/50 ] (2.2s) loss=0.9909 aux_flow=1.6016 aux_pkt=0.9438 +[epoch 39/50 ] (2.2s) loss=0.9861 aux_flow=1.5866 aux_pkt=0.9386 +[epoch 40/50 ] (16.3s) loss=0.9789 auroc_terminal=0.961 aux_flow=1.5679 aux_pkt=0.9428 +[epoch 41/50 ] (2.3s) loss=0.9755 aux_flow=1.5589 aux_pkt=0.9385 +[epoch 42/50 ] (2.3s) loss=0.9816 aux_flow=1.5740 aux_pkt=0.9446 +[epoch 43/50 ] (2.3s) loss=0.9773 aux_flow=1.5663 aux_pkt=0.9411 +[epoch 44/50 ] (2.2s) loss=0.9759 aux_flow=1.5609 aux_pkt=0.9435 +[epoch 45/50 ] (2.3s) loss=0.9732 aux_flow=1.5489 aux_pkt=0.9431 +[epoch 46/50 ] (2.3s) loss=0.9797 aux_flow=1.5711 aux_pkt=0.9430 +[epoch 47/50 ] (2.3s) loss=0.9768 aux_flow=1.5666 aux_pkt=0.9455 +[epoch 48/50 ] (2.3s) loss=0.9764 aux_flow=1.5646 aux_pkt=0.9406 +[epoch 49/50 ] (2.3s) loss=0.9781 aux_flow=1.5619 aux_pkt=0.9415 +[epoch 50/50 ] (16.9s) loss=0.9714 auroc_terminal=0.961 aux_flow=1.5477 aux_pkt=0.9438 +[saved] /home/chy/mambafortrafficmodeling/artifacts/route_comparison/route_b_spectral_ciciot2023_seed44/model.pt diff --git a/artifacts/route_comparison/route_c_mixed_seed42.log b/artifacts/route_comparison/route_c_mixed_seed42.log new file mode 100644 index 0000000..a630a1f --- /dev/null +++ b/artifacts/route_comparison/route_c_mixed_seed42.log @@ -0,0 +1,58 @@ +Device: cuda seed=model:42/data:42 +[data] flows=/home/chy/mambafortrafficmodeling/datasets/ciciot2023/processed/full_store/flows.parquet packets=/home/chy/mambafortrafficmodeling/datasets/ciciot2023/processed/full_store +[data] kept 3,797,530 of 8,193,621 (min_len=2) +[data] train=77,636 val=10,000 attack=20,000 +[data] T=64 cont=3 disc=6 flow=20 train=77,636 val=10,000 attack=20,000 +[data] training on 10,000 flows +[model] params=1,227,809 token_dim=21 sigma=0.1 use_ot=True lambda_disc=1.0 +[epoch 1/50 ] (1.9s) loss=1.3454 L_disc=0.5034 +[epoch 2/50 ] (1.3s) loss=1.0186 L_disc=0.4025 +[epoch 3/50 ] (1.4s) loss=0.8178 L_disc=0.3743 +[epoch 4/50 ] (1.4s) loss=0.6716 L_disc=0.3415 +[epoch 5/50 ] (1.4s) loss=0.5847 L_disc=0.3137 +[epoch 6/50 ] (1.4s) loss=0.5295 L_disc=0.2908 +[epoch 7/50 ] (1.3s) loss=0.4960 L_disc=0.2801 +[epoch 8/50 ] (1.4s) loss=0.4646 L_disc=0.2629 +[epoch 9/50 ] (1.3s) loss=0.4429 L_disc=0.2522 +[epoch 10/50 ] (6.5s) loss=0.4298 auroc_term=0.952 auroc_disc=0.811 L_disc=0.2487 +[epoch 11/50 ] (1.4s) loss=0.4070 L_disc=0.2370 +[epoch 12/50 ] (1.4s) loss=0.4080 L_disc=0.2383 +[epoch 13/50 ] (1.4s) loss=0.3905 L_disc=0.2235 +[epoch 14/50 ] (1.3s) loss=0.3782 L_disc=0.2195 +[epoch 15/50 ] (1.4s) loss=0.3606 L_disc=0.2103 +[epoch 16/50 ] (1.4s) loss=0.3479 L_disc=0.2016 +[epoch 17/50 ] (1.4s) loss=0.3460 L_disc=0.1987 +[epoch 18/50 ] (1.4s) loss=0.3456 L_disc=0.2029 +[epoch 19/50 ] (1.4s) loss=0.3400 L_disc=0.2020 +[epoch 20/50 ] (6.5s) loss=0.3351 auroc_term=0.960 auroc_disc=0.851 L_disc=0.1967 +[epoch 21/50 ] (1.4s) loss=0.3316 L_disc=0.1935 +[epoch 22/50 ] (1.4s) loss=0.3199 L_disc=0.1871 +[epoch 23/50 ] (1.4s) loss=0.3147 L_disc=0.1848 +[epoch 24/50 ] (1.4s) loss=0.3142 L_disc=0.1848 +[epoch 25/50 ] (1.3s) loss=0.3097 L_disc=0.1821 +[epoch 26/50 ] (1.3s) loss=0.3056 L_disc=0.1787 +[epoch 27/50 ] (1.3s) loss=0.3059 L_disc=0.1800 +[epoch 28/50 ] (1.4s) loss=0.3039 L_disc=0.1772 +[epoch 29/50 ] (1.4s) loss=0.2965 L_disc=0.1738 +[epoch 30/50 ] (6.7s) loss=0.2975 auroc_term=0.960 auroc_disc=0.888 L_disc=0.1754 +[epoch 31/50 ] (1.4s) loss=0.2940 L_disc=0.1715 +[epoch 32/50 ] (1.4s) loss=0.2899 L_disc=0.1690 +[epoch 33/50 ] (1.4s) loss=0.2944 L_disc=0.1745 +[epoch 34/50 ] (1.4s) loss=0.2886 L_disc=0.1690 +[epoch 35/50 ] (1.7s) loss=0.2932 L_disc=0.1739 +[epoch 36/50 ] (1.9s) loss=0.2839 L_disc=0.1674 +[epoch 37/50 ] (2.1s) loss=0.2814 L_disc=0.1650 +[epoch 38/50 ] (2.3s) loss=0.2816 L_disc=0.1629 +[epoch 39/50 ] (2.2s) loss=0.2848 L_disc=0.1696 +[epoch 40/50 ] (7.8s) loss=0.2791 auroc_term=0.963 auroc_disc=0.895 L_disc=0.1636 +[epoch 41/50 ] (2.2s) loss=0.2798 L_disc=0.1644 +[epoch 42/50 ] (2.3s) loss=0.2775 L_disc=0.1644 +[epoch 43/50 ] (2.4s) loss=0.2810 L_disc=0.1671 +[epoch 44/50 ] (1.9s) loss=0.2745 L_disc=0.1616 +[epoch 45/50 ] (1.7s) loss=0.2741 L_disc=0.1610 +[epoch 46/50 ] (1.5s) loss=0.2773 L_disc=0.1642 +[epoch 47/50 ] (1.5s) loss=0.2790 L_disc=0.1667 +[epoch 48/50 ] (1.5s) loss=0.2795 L_disc=0.1664 +[epoch 49/50 ] (1.5s) loss=0.2816 L_disc=0.1698 +[epoch 50/50 ] (7.0s) loss=0.2739 auroc_term=0.964 auroc_disc=0.893 L_disc=0.1620 +[saved] /home/chy/mambafortrafficmodeling/artifacts/route_comparison/route_c_mixed_ciciot2023_seed42/model.pt diff --git a/artifacts/route_comparison/route_c_mixed_seed43.log b/artifacts/route_comparison/route_c_mixed_seed43.log new file mode 100644 index 0000000..850cb5f --- /dev/null +++ b/artifacts/route_comparison/route_c_mixed_seed43.log @@ -0,0 +1,58 @@ +Device: cuda seed=model:43/data:43 +[data] flows=/home/chy/mambafortrafficmodeling/datasets/ciciot2023/processed/full_store/flows.parquet packets=/home/chy/mambafortrafficmodeling/datasets/ciciot2023/processed/full_store +[data] kept 3,797,530 of 8,193,621 (min_len=2) +[data] train=77,636 val=10,000 attack=20,000 +[data] T=64 cont=3 disc=6 flow=20 train=77,636 val=10,000 attack=20,000 +[data] training on 10,000 flows +[model] params=1,227,809 token_dim=21 sigma=0.1 use_ot=True lambda_disc=1.0 +[epoch 1/50 ] (2.4s) loss=1.3530 L_disc=0.5012 +[epoch 2/50 ] (2.3s) loss=1.0244 L_disc=0.4020 +[epoch 3/50 ] (2.7s) loss=0.8157 L_disc=0.3793 +[epoch 4/50 ] (2.7s) loss=0.6868 L_disc=0.3463 +[epoch 5/50 ] (2.5s) loss=0.6018 L_disc=0.3205 +[epoch 6/50 ] (2.3s) loss=0.5437 L_disc=0.3006 +[epoch 7/50 ] (2.4s) loss=0.5085 L_disc=0.2877 +[epoch 8/50 ] (2.6s) loss=0.4799 L_disc=0.2731 +[epoch 9/50 ] (2.6s) loss=0.4592 L_disc=0.2636 +[epoch 10/50 ] (13.2s) loss=0.4371 auroc_term=0.937 auroc_disc=0.848 L_disc=0.2542 +[epoch 11/50 ] (2.4s) loss=0.4260 L_disc=0.2465 +[epoch 12/50 ] (2.2s) loss=0.4026 L_disc=0.2331 +[epoch 13/50 ] (2.2s) loss=0.3967 L_disc=0.2342 +[epoch 14/50 ] (2.1s) loss=0.3777 L_disc=0.2210 +[epoch 15/50 ] (2.0s) loss=0.3755 L_disc=0.2185 +[epoch 16/50 ] (2.1s) loss=0.3704 L_disc=0.2166 +[epoch 17/50 ] (2.0s) loss=0.3595 L_disc=0.2117 +[epoch 18/50 ] (2.0s) loss=0.3543 L_disc=0.2095 +[epoch 19/50 ] (2.1s) loss=0.3463 L_disc=0.2039 +[epoch 20/50 ] (12.8s) loss=0.3419 auroc_term=0.955 auroc_disc=0.875 L_disc=0.2006 +[epoch 21/50 ] (2.1s) loss=0.3340 L_disc=0.1968 +[epoch 22/50 ] (2.2s) loss=0.3346 L_disc=0.1976 +[epoch 23/50 ] (2.1s) loss=0.3291 L_disc=0.1950 +[epoch 24/50 ] (2.1s) loss=0.3203 L_disc=0.1883 +[epoch 25/50 ] (2.1s) loss=0.3198 L_disc=0.1882 +[epoch 26/50 ] (2.1s) loss=0.3235 L_disc=0.1922 +[epoch 27/50 ] (2.1s) loss=0.3164 L_disc=0.1893 +[epoch 28/50 ] (2.2s) loss=0.3063 L_disc=0.1807 +[epoch 29/50 ] (2.1s) loss=0.3059 L_disc=0.1808 +[epoch 30/50 ] (12.8s) loss=0.3002 auroc_term=0.949 auroc_disc=0.885 L_disc=0.1769 +[epoch 31/50 ] (2.2s) loss=0.2969 L_disc=0.1732 +[epoch 32/50 ] (2.2s) loss=0.3044 L_disc=0.1807 +[epoch 33/50 ] (2.2s) loss=0.2989 L_disc=0.1771 +[epoch 34/50 ] (2.0s) loss=0.2943 L_disc=0.1729 +[epoch 35/50 ] (2.3s) loss=0.2950 L_disc=0.1739 +[epoch 36/50 ] (2.1s) loss=0.2955 L_disc=0.1773 +[epoch 37/50 ] (2.1s) loss=0.2922 L_disc=0.1746 +[epoch 38/50 ] (2.1s) loss=0.2899 L_disc=0.1720 +[epoch 39/50 ] (2.2s) loss=0.2901 L_disc=0.1731 +[epoch 40/50 ] (12.7s) loss=0.2859 auroc_term=0.959 auroc_disc=0.896 L_disc=0.1710 +[epoch 41/50 ] (2.2s) loss=0.2915 L_disc=0.1727 +[epoch 42/50 ] (2.1s) loss=0.2935 L_disc=0.1761 +[epoch 43/50 ] (2.0s) loss=0.2883 L_disc=0.1724 +[epoch 44/50 ] (2.2s) loss=0.2895 L_disc=0.1742 +[epoch 45/50 ] (2.3s) loss=0.2882 L_disc=0.1716 +[epoch 46/50 ] (2.2s) loss=0.2870 L_disc=0.1712 +[epoch 47/50 ] (2.2s) loss=0.2812 L_disc=0.1686 +[epoch 48/50 ] (2.3s) loss=0.2886 L_disc=0.1717 +[epoch 49/50 ] (2.1s) loss=0.2881 L_disc=0.1711 +[epoch 50/50 ] (12.6s) loss=0.2832 auroc_term=0.960 auroc_disc=0.890 L_disc=0.1684 +[saved] /home/chy/mambafortrafficmodeling/artifacts/route_comparison/route_c_mixed_ciciot2023_seed43/model.pt diff --git a/artifacts/route_comparison/route_c_mixed_seed44.log b/artifacts/route_comparison/route_c_mixed_seed44.log new file mode 100644 index 0000000..9ce998b --- /dev/null +++ b/artifacts/route_comparison/route_c_mixed_seed44.log @@ -0,0 +1,58 @@ +Device: cuda seed=model:44/data:44 +[data] flows=/home/chy/mambafortrafficmodeling/datasets/ciciot2023/processed/full_store/flows.parquet packets=/home/chy/mambafortrafficmodeling/datasets/ciciot2023/processed/full_store +[data] kept 3,797,530 of 8,193,621 (min_len=2) +[data] train=77,636 val=10,000 attack=20,000 +[data] T=64 cont=3 disc=6 flow=20 train=77,636 val=10,000 attack=20,000 +[data] training on 10,000 flows +[model] params=1,227,809 token_dim=21 sigma=0.1 use_ot=True lambda_disc=1.0 +[epoch 1/50 ] (3.1s) loss=1.3571 L_disc=0.5042 +[epoch 2/50 ] (2.7s) loss=1.0217 L_disc=0.4013 +[epoch 3/50 ] (2.5s) loss=0.8214 L_disc=0.3772 +[epoch 4/50 ] (2.3s) loss=0.6900 L_disc=0.3473 +[epoch 5/50 ] (2.4s) loss=0.5990 L_disc=0.3139 +[epoch 6/50 ] (2.4s) loss=0.5406 L_disc=0.2919 +[epoch 7/50 ] (2.5s) loss=0.5073 L_disc=0.2785 +[epoch 8/50 ] (2.7s) loss=0.4799 L_disc=0.2709 +[epoch 9/50 ] (2.6s) loss=0.4590 L_disc=0.2579 +[epoch 10/50 ] (13.0s) loss=0.4341 auroc_term=0.936 auroc_disc=0.845 L_disc=0.2474 +[epoch 11/50 ] (2.3s) loss=0.4240 L_disc=0.2406 +[epoch 12/50 ] (2.1s) loss=0.4045 L_disc=0.2314 +[epoch 13/50 ] (2.0s) loss=0.4008 L_disc=0.2317 +[epoch 14/50 ] (1.9s) loss=0.3907 L_disc=0.2252 +[epoch 15/50 ] (2.1s) loss=0.3753 L_disc=0.2172 +[epoch 16/50 ] (2.0s) loss=0.3732 L_disc=0.2155 +[epoch 17/50 ] (2.1s) loss=0.3614 L_disc=0.2103 +[epoch 18/50 ] (2.0s) loss=0.3560 L_disc=0.2067 +[epoch 19/50 ] (2.1s) loss=0.3498 L_disc=0.2043 +[epoch 20/50 ] (12.8s) loss=0.3383 auroc_term=0.953 auroc_disc=0.847 L_disc=0.1962 +[epoch 21/50 ] (2.1s) loss=0.3404 L_disc=0.1973 +[epoch 22/50 ] (2.2s) loss=0.3330 L_disc=0.1927 +[epoch 23/50 ] (2.1s) loss=0.3297 L_disc=0.1917 +[epoch 24/50 ] (2.2s) loss=0.3291 L_disc=0.1922 +[epoch 25/50 ] (2.0s) loss=0.3233 L_disc=0.1891 +[epoch 26/50 ] (2.1s) loss=0.3160 L_disc=0.1832 +[epoch 27/50 ] (2.0s) loss=0.3147 L_disc=0.1835 +[epoch 28/50 ] (2.2s) loss=0.3153 L_disc=0.1826 +[epoch 29/50 ] (2.4s) loss=0.3031 L_disc=0.1758 +[epoch 30/50 ] (12.6s) loss=0.3036 auroc_term=0.954 auroc_disc=0.861 L_disc=0.1768 +[epoch 31/50 ] (2.2s) loss=0.3066 L_disc=0.1773 +[epoch 32/50 ] (2.2s) loss=0.3070 L_disc=0.1807 +[epoch 33/50 ] (2.0s) loss=0.2985 L_disc=0.1739 +[epoch 34/50 ] (2.2s) loss=0.2989 L_disc=0.1747 +[epoch 35/50 ] (2.2s) loss=0.2913 L_disc=0.1692 +[epoch 36/50 ] (2.1s) loss=0.2923 L_disc=0.1704 +[epoch 37/50 ] (2.2s) loss=0.2951 L_disc=0.1732 +[epoch 38/50 ] (2.2s) loss=0.2955 L_disc=0.1750 +[epoch 39/50 ] (2.2s) loss=0.2910 L_disc=0.1687 +[epoch 40/50 ] (12.6s) loss=0.2907 auroc_term=0.958 auroc_disc=0.874 L_disc=0.1691 +[epoch 41/50 ] (2.1s) loss=0.2848 L_disc=0.1664 +[epoch 42/50 ] (2.1s) loss=0.2851 L_disc=0.1680 +[epoch 43/50 ] (2.3s) loss=0.2837 L_disc=0.1663 +[epoch 44/50 ] (2.2s) loss=0.2910 L_disc=0.1713 +[epoch 45/50 ] (2.3s) loss=0.2914 L_disc=0.1709 +[epoch 46/50 ] (2.2s) loss=0.2829 L_disc=0.1659 +[epoch 47/50 ] (2.2s) loss=0.2857 L_disc=0.1665 +[epoch 48/50 ] (2.2s) loss=0.2878 L_disc=0.1698 +[epoch 49/50 ] (2.2s) loss=0.2883 L_disc=0.1694 +[epoch 50/50 ] (11.6s) loss=0.2866 auroc_term=0.958 auroc_disc=0.877 L_disc=0.1681 +[saved] /home/chy/mambafortrafficmodeling/artifacts/route_comparison/route_c_mixed_ciciot2023_seed44/model.pt diff --git a/artifacts/route_comparison/run_ac_combo_evals.sh b/artifacts/route_comparison/run_ac_combo_evals.sh new file mode 100755 index 0000000..50e6c37 --- /dev/null +++ b/artifacts/route_comparison/run_ac_combo_evals.sh @@ -0,0 +1,70 @@ +#!/bin/bash +# Phase1 + cross eval for the 3 A+C combo seeds. +set -e +ROOT=/home/chy/mambafortrafficmodeling +MIXED_PHASE1=${ROOT}/Mixed_CFM/eval_phase1.py +MIXED_CROSS=${ROOT}/Mixed_CFM/eval_cross.py +CROSS_DIR=${ROOT}/artifacts/route_comparison/cross +mkdir -p ${CROSS_DIR} + +# GPU 0: phase1 + cross→IDS2017 for all 3 seeds +{ +for seed in 42 43 44; do + md=${ROOT}/artifacts/route_comparison/route_ac_combo_ciciot2023_seed${seed} + [ -f "${md}/model.pt" ] || { echo "[wait] seed${seed} model.pt not yet"; continue; } + + if [ ! -f "${md}/phase1_summary.json" ]; then + echo "[gpu0 phase1] seed${seed}" + cd ${ROOT}/Mixed_CFM + CUDA_VISIBLE_DEVICES=0 stdbuf -oL uv run --no-sync python -u ${MIXED_PHASE1} \ + --model-dir ${md} --out-dir ${md} \ + --batch-size 256 --n-steps 16 \ + --n-val-cap 5000 --n-atk-cap 10000 \ + > ${md}/phase1.log 2>&1 + fi + + ids_out=${CROSS_DIR}/route_ac_combo_seed${seed}_to_cicids2017.json + if [ ! -f "${ids_out}" ]; then + echo "[gpu0 cross→ids2017] seed${seed}" + cd ${ROOT}/Mixed_CFM + CUDA_VISIBLE_DEVICES=0 stdbuf -oL uv run --no-sync python -u ${MIXED_CROSS} \ + --model-dir ${md} \ + --target-store ${ROOT}/datasets/cicids2017/processed/full_store \ + --target-flows ${ROOT}/datasets/cicids2017/processed/flows.parquet \ + --target-flow-features ${ROOT}/datasets/cicids2017/processed/flow_features.parquet \ + --out ${ids_out} \ + --n-benign 10000 --n-attack 10000 --seed 42 --T 64 --batch-size 256 --n-steps 16 \ + > ${CROSS_DIR}/route_ac_combo_seed${seed}_to_cicids2017.log 2>&1 + fi +done +echo "[gpu0 done]" +} > /tmp/ac_eval_gpu0.log 2>&1 & +GPU0=$! + +# GPU 1: cross→DDoS19 for all 3 seeds +{ +for seed in 42 43 44; do + md=${ROOT}/artifacts/route_comparison/route_ac_combo_ciciot2023_seed${seed} + [ -f "${md}/model.pt" ] || { echo "[wait] seed${seed} model.pt not yet"; continue; } + + ddos_out=${CROSS_DIR}/route_ac_combo_seed${seed}_to_cicddos2019.json + if [ ! -f "${ddos_out}" ]; then + echo "[gpu1 cross→ddos19] seed${seed}" + cd ${ROOT}/Mixed_CFM + CUDA_VISIBLE_DEVICES=1 stdbuf -oL uv run --no-sync python -u ${MIXED_CROSS} \ + --model-dir ${md} \ + --target-store ${ROOT}/datasets/cicddos2019/processed/full_store \ + --target-flows ${ROOT}/datasets/cicddos2019/processed/flows.parquet \ + --target-flow-features ${ROOT}/datasets/cicddos2019/processed/flow_features.parquet \ + --out ${ddos_out} \ + --n-benign 10000 --n-attack 10000 --seed 42 --T 64 --batch-size 256 --n-steps 16 \ + > ${CROSS_DIR}/route_ac_combo_seed${seed}_to_cicddos2019.log 2>&1 + fi +done +echo "[gpu1 done]" +} > /tmp/ac_eval_gpu1.log 2>&1 & +GPU1=$! + +wait $GPU0 +wait $GPU1 +echo "[all ac combo evals done]" diff --git a/artifacts/route_comparison/run_all_phase1.sh b/artifacts/route_comparison/run_all_phase1.sh new file mode 100755 index 0000000..6d60c1e --- /dev/null +++ b/artifacts/route_comparison/run_all_phase1.sh @@ -0,0 +1,68 @@ +#!/bin/bash +# Run phase1 eval on all routes after trainings complete. +# Splits across 2 GPUs in parallel chains. + +set -e +ROOT=/home/chy/mambafortrafficmodeling +UNIFIED_EVAL=${ROOT}/artifacts/verify_2026_04_24/eval_phase1_unified.py +MIXED_EVAL=${ROOT}/Mixed_CFM/eval_phase1.py + +cd ${ROOT} + +# GPU 0: baselines + route_a (6 models) +{ +for prefix in baseline_ciciot2023 route_a_causal_ciciot2023; do + for seed in 42 43 44; do + name=${prefix}_seed${seed} + md=${ROOT}/artifacts/route_comparison/${name} + [ -f "${md}/model.pt" ] || continue + [ -f "${md}/phase1_summary.json" ] && continue + echo "[GPU0 eval] ${name}" + cd ${ROOT}/Unified_CFM + CUDA_VISIBLE_DEVICES=0 stdbuf -oL uv run --no-sync python -u ${UNIFIED_EVAL} \ + --model-dir ${md} --out-dir ${md} \ + --batch-size 256 --n-steps 16 --jacobian-n-eps 4 \ + --n-val-cap 5000 --n-atk-cap 10000 \ + > ${md}/phase1.log 2>&1 + done +done +echo "[GPU0 done]" +} & +GPU0_PID=$! + +# GPU 1: route_b + route_c (6 models) +{ +for seed in 42 43 44; do + name=route_b_spectral_ciciot2023_seed${seed} + md=${ROOT}/artifacts/route_comparison/${name} + [ -f "${md}/model.pt" ] || continue + [ -f "${md}/phase1_summary.json" ] && continue + echo "[GPU1 eval] ${name}" + cd ${ROOT}/Unified_CFM + CUDA_VISIBLE_DEVICES=1 stdbuf -oL uv run --no-sync python -u ${UNIFIED_EVAL} \ + --model-dir ${md} --out-dir ${md} \ + --batch-size 256 --n-steps 16 --jacobian-n-eps 4 \ + --n-val-cap 5000 --n-atk-cap 10000 \ + > ${md}/phase1.log 2>&1 +done +for seed in 42 43 44; do + name=route_c_mixed_ciciot2023_seed${seed} + md=${ROOT}/artifacts/route_comparison/${name} + [ -f "${md}/model.pt" ] || continue + [ -f "${md}/phase1_summary.json" ] && continue + echo "[GPU1 eval] ${name}" + cd ${ROOT}/Mixed_CFM + CUDA_VISIBLE_DEVICES=1 stdbuf -oL uv run --no-sync python -u ${MIXED_EVAL} \ + --model-dir ${md} --out-dir ${md} \ + --batch-size 256 --n-steps 16 \ + --n-val-cap 5000 --n-atk-cap 10000 \ + > ${md}/phase1.log 2>&1 +done +echo "[GPU1 done]" +} & +GPU1_PID=$! + +wait $GPU0_PID +wait $GPU1_PID +echo "[all phase1 done]" +cd ${ROOT} && uv run --no-sync python artifacts/route_comparison/aggregate_results.py diff --git a/artifacts/route_comparison/run_cross_all.sh b/artifacts/route_comparison/run_cross_all.sh new file mode 100755 index 0000000..d702b66 --- /dev/null +++ b/artifacts/route_comparison/run_cross_all.sh @@ -0,0 +1,105 @@ +#!/bin/bash +# Cross-dataset eval for all 4 routes × 2 targets × 3 seeds = 24 runs. +# Source: CICIoT2023 (where all models were trained). +# Targets: CICIDS2017 + CICDDoS2019. + +set -e +ROOT=/home/chy/mambafortrafficmodeling +UNIFIED_EVAL=${ROOT}/artifacts/verify_2026_04_24/eval_phase2_cross_cicddos2019.py +MIXED_EVAL=${ROOT}/Mixed_CFM/eval_cross.py +CROSS_DIR=${ROOT}/artifacts/route_comparison/cross +mkdir -p ${CROSS_DIR} + +# Target dataset paths +declare -A TARGETS +TARGETS[cicids2017_store]=${ROOT}/datasets/cicids2017/processed/full_store +TARGETS[cicids2017_flows]=${ROOT}/datasets/cicids2017/processed/flows.parquet +TARGETS[cicids2017_features]=${ROOT}/datasets/cicids2017/processed/flow_features.parquet +TARGETS[cicids2017_features_spectral]=${ROOT}/datasets/cicids2017/processed/flow_features_spectral.parquet + +TARGETS[cicddos2019_store]=${ROOT}/datasets/cicddos2019/processed/full_store +TARGETS[cicddos2019_flows]=${ROOT}/datasets/cicddos2019/processed/flows.parquet +TARGETS[cicddos2019_features]=${ROOT}/datasets/cicddos2019/processed/flow_features.parquet +TARGETS[cicddos2019_features_spectral]=${ROOT}/datasets/cicddos2019/processed/flow_features_spectral.parquet + +run_unified_eval() { + local gpu=$1 model_dir=$2 target=$3 features=$4 out_name=$5 + local out=${CROSS_DIR}/${out_name}.json + [ -f "${out}" ] && { echo "[skip] ${out_name}"; return; } + echo "[gpu${gpu} eval] ${out_name}" + cd ${ROOT}/Unified_CFM + CUDA_VISIBLE_DEVICES=${gpu} stdbuf -oL uv run --no-sync python -u ${UNIFIED_EVAL} \ + --model-dir ${model_dir} \ + --target-store ${TARGETS[${target}_store]} \ + --target-flows ${TARGETS[${target}_flows]} \ + --target-flow-features ${features} \ + --out ${out} \ + --n-benign 10000 --n-attack 10000 --seed 42 \ + --T 64 --batch-size 256 --n-steps 16 \ + > ${CROSS_DIR}/${out_name}.log 2>&1 +} + +run_mixed_eval() { + local gpu=$1 model_dir=$2 target=$3 out_name=$4 + local out=${CROSS_DIR}/${out_name}.json + [ -f "${out}" ] && { echo "[skip] ${out_name}"; return; } + echo "[gpu${gpu} mixed eval] ${out_name}" + cd ${ROOT}/Mixed_CFM + CUDA_VISIBLE_DEVICES=${gpu} stdbuf -oL uv run --no-sync python -u ${MIXED_EVAL} \ + --model-dir ${model_dir} \ + --target-store ${TARGETS[${target}_store]} \ + --target-flows ${TARGETS[${target}_flows]} \ + --target-flow-features ${TARGETS[${target}_features]} \ + --out ${out} \ + --n-benign 10000 --n-attack 10000 --seed 42 \ + --T 64 --batch-size 256 --n-steps 16 \ + > ${CROSS_DIR}/${out_name}.log 2>&1 +} + +# === GPU 0 chain: baselines + route_a, both targets === +{ +for prefix_route in "baseline_ciciot2023:baseline" "route_a_causal_ciciot2023:route_a_causal"; do + prefix=${prefix_route%:*} + short=${prefix_route#*:} + for seed in 42 43 44; do + md=${ROOT}/artifacts/route_comparison/${prefix}_seed${seed} + [ -f "${md}/model.pt" ] || continue + for target in cicids2017 cicddos2019; do + run_unified_eval 0 "${md}" "${target}" "${TARGETS[${target}_features]}" \ + "${short}_seed${seed}_to_${target}" + done + done +done +echo "[gpu0 cross chain done]" +} > /tmp/cross_gpu0.log 2>&1 & +GPU0=$! + +# === GPU 1 chain: route_b (uses spectral features) + route_c (mixed) === +{ +# route_b: must use flow_features_spectral.parquet +for seed in 42 43 44; do + md=${ROOT}/artifacts/route_comparison/route_b_spectral_ciciot2023_seed${seed} + [ -f "${md}/model.pt" ] || continue + for target in cicids2017 cicddos2019; do + run_unified_eval 1 "${md}" "${target}" "${TARGETS[${target}_features_spectral]}" \ + "route_b_spectral_seed${seed}_to_${target}" + done +done + +# route_c: Mixed_CFM eval (uses canonical flow_features) +for seed in 42 43 44; do + md=${ROOT}/artifacts/route_comparison/route_c_mixed_ciciot2023_seed${seed} + [ -f "${md}/model.pt" ] || continue + for target in cicids2017 cicddos2019; do + run_mixed_eval 1 "${md}" "${target}" \ + "route_c_mixed_seed${seed}_to_${target}" + done +done +echo "[gpu1 cross chain done]" +} > /tmp/cross_gpu1.log 2>&1 & +GPU1=$! + +wait $GPU0 +wait $GPU1 +echo "[all cross done]" +ls -la ${CROSS_DIR}/*.json | wc -l diff --git a/artifacts/route_comparison/run_full_cross_matrix.sh b/artifacts/route_comparison/run_full_cross_matrix.sh new file mode 100755 index 0000000..6e826ad --- /dev/null +++ b/artifacts/route_comparison/run_full_cross_matrix.sh @@ -0,0 +1,88 @@ +#!/bin/bash +# Run all missing cross-direction evals for A+C combo. +# Targets are routed to packets-npz or full_store as appropriate. + +set -e +ROOT=/home/chy/mambafortrafficmodeling +EVAL=${ROOT}/Mixed_CFM/eval_cross.py +CROSS_DIR=${ROOT}/artifacts/route_comparison/cross +mkdir -p ${CROSS_DIR} + +# Target paths +TGT_iscxtor2016_npz=${ROOT}/datasets/iscxtor2016/processed/packets.npz +TGT_iscxtor2016_flows=${ROOT}/datasets/iscxtor2016/processed/flows.parquet +TGT_iscxtor2016_features=${ROOT}/datasets/iscxtor2016/processed/flow_features.parquet +TGT_iscxtor2016_label=nontor +TGT_iscxtor2016_natk=1888 + +TGT_cicids2017_store=${ROOT}/datasets/cicids2017/processed/full_store +TGT_cicids2017_flows=${ROOT}/datasets/cicids2017/processed/flows.parquet +TGT_cicids2017_features=${ROOT}/datasets/cicids2017/processed/flow_features.parquet +TGT_cicids2017_label=normal + +TGT_cicddos2019_store=${ROOT}/datasets/cicddos2019/processed/full_store +TGT_cicddos2019_flows=${ROOT}/datasets/cicddos2019/processed/flows.parquet +TGT_cicddos2019_features=${ROOT}/datasets/cicddos2019/processed/flow_features.parquet +TGT_cicddos2019_label=normal + +TGT_ciciot2023_store=${ROOT}/datasets/ciciot2023/processed/full_store +TGT_ciciot2023_flows=${ROOT}/datasets/ciciot2023/processed/full_store/flows.parquet +TGT_ciciot2023_features=${ROOT}/datasets/ciciot2023/processed/flow_features.parquet +TGT_ciciot2023_label=normal + +run_one() { + local gpu=$1 src=$2 tgt=$3 seed=$4 + local md=${ROOT}/artifacts/route_comparison/route_ac_combo_${src}_seed${seed} + local out=${CROSS_DIR}/route_ac_combo_seed${seed}_${src}_to_${tgt}.json + if [ -f "${out}" ]; then echo "[skip] ${src}→${tgt} seed${seed}"; return; fi + if [ ! -f "${md}/model.pt" ]; then echo "[missing] ${md}/model.pt"; return; fi + + # Resolve target args + local tgt_args + if [ "${tgt}" = "iscxtor2016" ]; then + tgt_args="--target-packets-npz ${TGT_iscxtor2016_npz} --target-flows ${TGT_iscxtor2016_flows} --target-flow-features ${TGT_iscxtor2016_features} --benign-label nontor --n-attack 1888" + elif [ "${tgt}" = "cicids2017" ]; then + tgt_args="--target-store ${TGT_cicids2017_store} --target-flows ${TGT_cicids2017_flows} --target-flow-features ${TGT_cicids2017_features} --benign-label normal --n-attack 10000" + elif [ "${tgt}" = "cicddos2019" ]; then + tgt_args="--target-store ${TGT_cicddos2019_store} --target-flows ${TGT_cicddos2019_flows} --target-flow-features ${TGT_cicddos2019_features} --benign-label normal --n-attack 10000" + elif [ "${tgt}" = "ciciot2023" ]; then + tgt_args="--target-store ${TGT_ciciot2023_store} --target-flows ${TGT_ciciot2023_flows} --target-flow-features ${TGT_ciciot2023_features} --benign-label normal --n-attack 10000" + fi + + echo "[gpu${gpu}] ${src} → ${tgt} seed${seed}" + cd ${ROOT}/Mixed_CFM + CUDA_VISIBLE_DEVICES=${gpu} stdbuf -oL uv run --no-sync python -u ${EVAL} \ + --model-dir ${md} \ + ${tgt_args} \ + --out ${out} \ + --n-benign 10000 --seed 42 --T 64 --batch-size 256 --n-steps 16 \ + > ${CROSS_DIR}/route_ac_combo_seed${seed}_${src}_to_${tgt}.log 2>&1 +} + +# 8 missing directions × 3 seeds = 24 evals +# Split across 2 GPUs to balance load +{ +for dir in "ciciot2023:iscxtor2016" "cicids2017:iscxtor2016" "cicddos2019:iscxtor2016" "iscxtor2016:cicids2017"; do + src=${dir%:*}; tgt=${dir#*:} + for seed in 42 43 44; do + run_one 0 ${src} ${tgt} ${seed} + done +done +echo "[gpu0 done]" +} > /tmp/cross_matrix_gpu0.log 2>&1 & +G0=$! + +{ +for dir in "cicids2017:ciciot2023" "cicddos2019:ciciot2023" "iscxtor2016:cicddos2019" "iscxtor2016:ciciot2023"; do + src=${dir%:*}; tgt=${dir#*:} + for seed in 42 43 44; do + run_one 1 ${src} ${tgt} ${seed} + done +done +echo "[gpu1 done]" +} > /tmp/cross_matrix_gpu1.log 2>&1 & +G1=$! + +wait $G0 +wait $G1 +echo "[all done]" diff --git a/artifacts/route_comparison/run_phase1_all.sh b/artifacts/route_comparison/run_phase1_all.sh new file mode 100755 index 0000000..52ded6c --- /dev/null +++ b/artifacts/route_comparison/run_phase1_all.sh @@ -0,0 +1,45 @@ +#!/bin/bash +# Run phase1 eval on all route_comparison models. +# Output: /phase1_summary.json + phase1_scores.npz +# +# Usage: +# bash artifacts/route_comparison/run_phase1_all.sh [GPU_ID] +# +# Default GPU_ID = 0. Each eval takes ~3-5 min with the caps below. + +set -e +GPU_ID="${1:-0}" +ROOT=/home/chy/mambafortrafficmodeling +EVAL=${ROOT}/artifacts/verify_2026_04_24/eval_phase1_unified.py + +models=( + baseline_ciciot2023_seed42 + baseline_ciciot2023_seed43 + baseline_ciciot2023_seed44 + route_a_causal_ciciot2023_seed42 + route_a_causal_ciciot2023_seed43 + route_a_causal_ciciot2023_seed44 +) + +cd ${ROOT}/Unified_CFM +for name in "${models[@]}"; do + model_dir=${ROOT}/artifacts/route_comparison/${name} + if [ ! -f "${model_dir}/model.pt" ]; then + echo "[skip] ${name}: model.pt missing" + continue + fi + out_dir=${model_dir} + if [ -f "${out_dir}/phase1_summary.json" ]; then + echo "[skip] ${name}: phase1_summary.json exists" + continue + fi + echo "[eval] ${name}" + CUDA_VISIBLE_DEVICES=${GPU_ID} stdbuf -oL uv run --no-sync python -u ${EVAL} \ + --model-dir ${model_dir} --out-dir ${out_dir} \ + --batch-size 256 --n-steps 16 \ + --jacobian-n-eps 4 \ + --n-val-cap 5000 --n-atk-cap 10000 \ + 2>&1 | tee ${model_dir}/phase1.log | tail -5 + echo "[done] ${name}" +done +echo "[all done]" diff --git a/artifacts/smoke_mixed_cfm_cicids2017_2026_05_07/config.yaml b/artifacts/smoke_mixed_cfm_cicids2017_2026_05_07/config.yaml new file mode 100644 index 0000000..a507c81 --- /dev/null +++ b/artifacts/smoke_mixed_cfm_cicids2017_2026_05_07/config.yaml @@ -0,0 +1,33 @@ +T: 64 +batch_size: 256 +benign_label: normal +d_model: 128 +data_seed: 42 +device: auto +epochs: 3 +eval_batch_size: 512 +eval_every: 1 +eval_n: 20000 +eval_n_steps: 8 +flow_features_align: auto +flow_features_path: /home/chy/JANUS/datasets/cicids2017/processed/flow_features.parquet +flows_parquet: /home/chy/JANUS/datasets/cicids2017/processed/flows.parquet +grad_clip: 1.0 +lambda_disc: 1.0 +lr: 0.0003 +min_len: 2 +mlp_ratio: 4.0 +n_heads: 4 +n_layers: 4 +n_train: 10000 +num_workers: 0 +packets_npz: /home/chy/JANUS/datasets/cicids2017/processed/packets.npz +reference_mode: causal_packets +save_dir: /home/chy/JANUS/artifacts/smoke_mixed_cfm_cicids2017_2026_05_07 +seed: 42 +sigma: 0.1 +time_dim: 64 +token_dim: null +train_ratio: 0.8 +use_ot: true +weight_decay: 0.01 diff --git a/artifacts/smoke_mixed_cfm_cicids2017_2026_05_07/history.json b/artifacts/smoke_mixed_cfm_cicids2017_2026_05_07/history.json new file mode 100644 index 0000000..d839feb --- /dev/null +++ b/artifacts/smoke_mixed_cfm_cicids2017_2026_05_07/history.json @@ -0,0 +1,50 @@ +{ + "epoch": [ + 1, + 2, + 3 + ], + "loss": [ + 1.289611544364538, + 0.9913776807295971, + 0.8790762821833292 + ], + "eval": [ + { + "auroc_disc_nll_ch2": 0.25269275874999997, + "auroc_disc_nll_ch3": 0.88588564125, + "auroc_disc_nll_ch4": 0.2624527575, + "auroc_disc_nll_ch5": 0.57938426375, + "auroc_disc_nll_ch6": 0.2178112225, + "auroc_disc_nll_ch7": 0.4759613475, + "auroc_disc_nll_total": 0.56943336875, + "auroc_terminal_flow": 0.8795462425, + "auroc_terminal_norm": 0.9127291475, + "auroc_terminal_packet": 0.7969265175 + }, + { + "auroc_disc_nll_ch2": 0.25790110125, + "auroc_disc_nll_ch3": 0.8072072512499999, + "auroc_disc_nll_ch4": 0.6692989837500001, + "auroc_disc_nll_ch5": 0.794193235, + "auroc_disc_nll_ch6": 0.6028056249999999, + "auroc_disc_nll_ch7": 0.25918654625000004, + "auroc_disc_nll_total": 0.64147287, + "auroc_terminal_flow": 0.8703131375000001, + "auroc_terminal_norm": 0.9228643675, + "auroc_terminal_packet": 0.8749805125 + }, + { + "auroc_disc_nll_ch2": 0.31913713374999997, + "auroc_disc_nll_ch3": 0.395250385, + "auroc_disc_nll_ch4": 0.654063365, + "auroc_disc_nll_ch5": 0.7267536325, + "auroc_disc_nll_ch6": 0.6120417575, + "auroc_disc_nll_ch7": 0.20421194624999994, + "auroc_disc_nll_total": 0.55792578625, + "auroc_terminal_flow": 0.8664720587499999, + "auroc_terminal_norm": 0.92477554125, + "auroc_terminal_packet": 0.8792170362500001 + } + ] +} \ No newline at end of file diff --git a/artifacts/smoke_mixed_cfm_cicids2017_2026_05_07/model.pt b/artifacts/smoke_mixed_cfm_cicids2017_2026_05_07/model.pt new file mode 100644 index 0000000..a25f612 Binary files /dev/null and b/artifacts/smoke_mixed_cfm_cicids2017_2026_05_07/model.pt differ diff --git a/artifacts/smoke_mixed_cfm_cicids2017_2026_05_07/phase1_scores.npz b/artifacts/smoke_mixed_cfm_cicids2017_2026_05_07/phase1_scores.npz new file mode 100644 index 0000000..608d060 Binary files /dev/null and b/artifacts/smoke_mixed_cfm_cicids2017_2026_05_07/phase1_scores.npz differ diff --git a/artifacts/smoke_mixed_cfm_cicids2017_2026_05_07/phase1_summary.json b/artifacts/smoke_mixed_cfm_cicids2017_2026_05_07/phase1_summary.json new file mode 100644 index 0000000..2937b3d --- /dev/null +++ b/artifacts/smoke_mixed_cfm_cicids2017_2026_05_07/phase1_summary.json @@ -0,0 +1,189 @@ +{ + "overall": { + "disc_nll_ch2": { + "auroc": 0.314295125, + "auprc": 0.3910344014904207 + }, + "disc_nll_ch3": { + "auroc": 0.38912624999999995, + "auprc": 0.40407053690756645 + }, + "disc_nll_ch4": { + "auroc": 0.65274975, + "auprc": 0.628958931388995 + }, + "disc_nll_ch5": { + "auroc": 0.720147, + "auprc": 0.7760680734251078 + }, + "disc_nll_ch6": { + "auroc": 0.617926, + "auprc": 0.5831956159611293 + }, + "disc_nll_ch7": { + "auroc": 0.2004435, + "auprc": 0.3475626064235516 + }, + "disc_nll_total": { + "auroc": 0.55982975, + "auprc": 0.6359317620367989 + }, + "terminal_flow": { + "auroc": 0.86743825, + "auprc": 0.7767544668627614 + }, + "terminal_norm": { + "auroc": 0.927387, + "auprc": 0.900706865984596 + }, + "terminal_packet": { + "auroc": 0.8636792499999999, + "auprc": 0.7829830345195031 + } + }, + "per_class": { + "Botnet": { + "_n": 3.0, + "disc_nll_ch2": 0.9458333333333333, + "disc_nll_ch3": 0.9275, + "disc_nll_ch4": 0.9951666666666666, + "disc_nll_ch5": 0.9263333333333333, + "disc_nll_ch6": 0.4383333333333333, + "disc_nll_ch7": 0.4636666666666666, + "disc_nll_total": 0.9716666666666667, + "terminal_flow": 0.5431666666666666, + "terminal_norm": 0.594, + "terminal_packet": 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0.4188167752442996, + "disc_nll_ch4": 0.6040944625407166, + "disc_nll_ch5": 0.9968754071661238, + "disc_nll_ch6": 0.41372557003257326, + "disc_nll_ch7": 0.20278501628664497, + "disc_nll_total": 0.9234959283387622, + "terminal_flow": 0.7502141693811074, + "terminal_norm": 0.9877084690553746, + "terminal_packet": 0.9223452768729641 + }, + "SSH-Patator": { + "_n": 12.0, + "disc_nll_ch2": 0.9818333333333333, + "disc_nll_ch3": 0.27558333333333335, + "disc_nll_ch4": 0.2743333333333333, + "disc_nll_ch5": 0.24395833333333333, + "disc_nll_ch6": 0.9594166666666666, + "disc_nll_ch7": 0.18820833333333334, + "disc_nll_total": 0.19395833333333332, + "terminal_flow": 0.862375, + "terminal_norm": 0.003499999999999966, + "terminal_packet": 0.4285 + }, + "Web Attack - SQL Injection": { + "_n": 1.0, + "disc_nll_ch2": 0.9545, + "disc_nll_ch3": 0.41600000000000004, + "disc_nll_ch4": 0.8255, + "disc_nll_ch5": 0.20999999999999996, + "disc_nll_ch6": 0.656, + "disc_nll_ch7": 0.21099999999999997, + "disc_nll_total": 0.21950000000000003, + "terminal_flow": 0.929, + "terminal_norm": 0.9095, + "terminal_packet": 0.8240000000000001 + } + } +} \ No newline at end of file diff --git a/common/__init__.py b/common/__init__.py new file mode 100644 index 0000000..2ae2839 --- /dev/null +++ b/common/__init__.py @@ -0,0 +1 @@ +pass diff --git a/common/data_contract.py b/common/data_contract.py new file mode 100644 index 0000000..518cc9d --- /dev/null +++ b/common/data_contract.py @@ -0,0 +1,136 @@ +from __future__ import annotations +from typing import Sequence +import numpy as np +PACKET_FEATURE_NAMES: tuple[str, ...] = ('log_size', 'log_dt_ms', 'direction', 'tcp_syn', 'tcp_fin', 'tcp_rst', 'tcp_psh', 'tcp_ack', 'log_win') +PACKET_D: int = len(PACKET_FEATURE_NAMES) +PACKET_CONTINUOUS_CHANNEL_IDX: tuple[int, ...] = (0, 1, 8) +PACKET_BINARY_CHANNEL_IDX: tuple[int, ...] = (2, 3, 4, 5, 6, 7) +CONTINUOUS_CHANNEL_IDX = PACKET_CONTINUOUS_CHANNEL_IDX +BINARY_CHANNEL_IDX = PACKET_BINARY_CHANNEL_IDX +CANONICAL_FLOW_FEATURE_NAMES: tuple[str, ...] = ('log_duration', 'log_n_pkts', 'fwd_count', 'bwd_count', 'pkt_size_mean', 'pkt_size_std', 'pkt_size_max', 'fwd_size_mean', 'bwd_size_mean', 'bwd_size_std', 'iat_mean', 'fwd_iat_max', 'bwd_iat_max', 'bwd_iat_std', 'active_mean', 'idle_mean', 'log_pkts_per_s', 'log_total_bytes', 'ack_cnt', 'syn_cnt') +FLOW_D: int = len(CANONICAL_FLOW_FEATURE_NAMES) +FLOW_COUNT_FEATURE_NAMES: tuple[str, ...] = ('fwd_count', 'bwd_count', 'ack_cnt', 'syn_cnt') +FLOW_COUNT_IDX: tuple[int, ...] = tuple((i for (i, name) in enumerate(CANONICAL_FLOW_FEATURE_NAMES) if name in FLOW_COUNT_FEATURE_NAMES)) +FLOW_CONTINUOUS_IDX: tuple[int, ...] = tuple((i for (i, name) in enumerate(CANONICAL_FLOW_FEATURE_NAMES) if name not in FLOW_COUNT_FEATURE_NAMES)) +IDLE_THRESHOLD_MS: float = 1000.0 +BENIGN_ALIASES: tuple[str, ...] = ('BENIGN', 'Benign', 'benign', 'normal', 'NORMAL', 'Normal') +BENIGN_TOKEN: str = 'normal' +UNKNOWN_LABEL_TOKEN: str = 'unlabeled' + +def normalize_label(raw: object) -> str: + s = str(raw).strip() + if not s: + return UNKNOWN_LABEL_TOKEN + if s in BENIGN_ALIASES or s.upper() == 'BENIGN': + return BENIGN_TOKEN + return s + +def canonical_5tuple(src_ip: object, src_port: object, dst_ip: object, dst_port: object, protocol: object) -> tuple[str, int, str, int, int]: + sp = int(float(src_port)) + dp = int(float(dst_port)) + proto = int(float(protocol)) + a = (str(src_ip), sp) + b = (str(dst_ip), dp) + if a <= b: + return (a[0], a[1], b[0], b[1], proto) + return (b[0], b[1], a[0], a[1], proto) + +def fit_packet_stats(packet_tokens: np.ndarray, packet_lengths: np.ndarray) -> tuple[np.ndarray, np.ndarray]: + T = packet_tokens.shape[1] + mask = np.arange(T)[None, :] < packet_lengths[:, None] + valid = packet_tokens[mask] + return (valid.mean(axis=0).astype(np.float32), valid.std(axis=0).astype(np.float32)) + +def zscore(x: np.ndarray, mean: np.ndarray, std: np.ndarray) -> np.ndarray: + return ((x - mean) / np.maximum(std, 1e-06)).astype(np.float32) + +def _stable_dequant_noise(shape: Sequence[int], seed: int, salt: str) -> np.ndarray: + salt_int = sum(((i + 1) * ord(c) for (i, c) in enumerate(salt))) + rng = np.random.default_rng(seed + salt_int) + return rng.uniform(-0.5, 0.5, size=tuple(shape)).astype(np.float32) + +def apply_mixed_dequant(packet_tokens: np.ndarray, packet_lengths: np.ndarray, mean: np.ndarray, std: np.ndarray, *, split_tag: str, seed: int) -> np.ndarray: + T = packet_tokens.shape[1] + z = np.zeros_like(packet_tokens, dtype=np.float32) + cont = list(PACKET_CONTINUOUS_CHANNEL_IDX) + binary = list(PACKET_BINARY_CHANNEL_IDX) + z[..., cont] = zscore(packet_tokens[..., cont], mean[cont], std[cont]) + b = packet_tokens[..., binary].astype(np.float32) + z[..., binary] = b + _stable_dequant_noise(b.shape, seed, split_tag) + mask = np.arange(T)[None, :] < packet_lengths[:, None] + return (z * mask[:, :, None]).astype(np.float32) + +def compute_flow_features_from_packets(packet_tokens: np.ndarray, packet_lengths: np.ndarray, *, idle_threshold_ms: float=IDLE_THRESHOLD_MS) -> np.ndarray: + if packet_tokens.ndim != 3 or packet_tokens.shape[-1] != PACKET_D: + raise ValueError(f'packet_tokens must be [N, T, {PACKET_D}], got {packet_tokens.shape}') + if packet_lengths.ndim != 1 or packet_lengths.shape[0] != packet_tokens.shape[0]: + raise ValueError(f'packet_lengths must be [N] matching packet_tokens, got {packet_lengths.shape}') + (N, T, _) = packet_tokens.shape + lens = np.clip(packet_lengths.astype(np.int64), 0, T) + out = np.zeros((N, FLOW_D), dtype=np.float32) + idx_of = {name: i for (i, name) in enumerate(CANONICAL_FLOW_FEATURE_NAMES)} + log_size = packet_tokens[..., 0].astype(np.float64) + log_dt_ms = packet_tokens[..., 1].astype(np.float64) + direction = packet_tokens[..., 2].astype(np.float64) + sizes = np.expm1(np.clip(log_size, 0.0, 25.0)) + for i in range(N): + n = int(lens[i]) + if n <= 0: + continue + sz = sizes[i, :n] + dt = np.expm1(np.clip(log_dt_ms[i, :n], 0.0, 25.0)) + dir_arr = direction[i, :n] + fwd = dir_arr < 0.5 + bwd = ~fwd + n_fwd = int(fwd.sum()) + n_bwd = int(bwd.sum()) + duration_ms = float(dt.sum()) + out[i, idx_of['log_duration']] = np.log1p(max(duration_ms, 0.0)) + out[i, idx_of['log_n_pkts']] = np.log1p(n) + out[i, idx_of['fwd_count']] = float(n_fwd) + out[i, idx_of['bwd_count']] = float(n_bwd) + ls = log_size[i, :n] + out[i, idx_of['pkt_size_mean']] = float(ls.mean()) + out[i, idx_of['pkt_size_std']] = float(ls.std()) if n > 1 else 0.0 + out[i, idx_of['pkt_size_max']] = float(ls.max()) + if n_fwd > 0: + out[i, idx_of['fwd_size_mean']] = float(ls[fwd].mean()) + if n_bwd > 0: + out[i, idx_of['bwd_size_mean']] = float(ls[bwd].mean()) + if n_bwd > 1: + out[i, idx_of['bwd_size_std']] = float(ls[bwd].std()) + if n > 1: + ldt = log_dt_ms[i, 1:n] + out[i, idx_of['iat_mean']] = float(ldt.mean()) + if n_fwd > 1: + fwd_dt = log_dt_ms[i, 1:n][fwd[1:]] + if fwd_dt.size > 0: + out[i, idx_of['fwd_iat_max']] = float(fwd_dt.max()) + if n_bwd > 1: + bwd_dt = log_dt_ms[i, 1:n][bwd[1:]] + if bwd_dt.size > 0: + out[i, idx_of['bwd_iat_max']] = float(bwd_dt.max()) + if bwd_dt.size > 1: + out[i, idx_of['bwd_iat_std']] = float(bwd_dt.std()) + if n > 1: + dt_linear = dt[1:] + idle_mask = dt_linear > idle_threshold_ms + active_mask = ~idle_mask + if active_mask.any(): + out[i, idx_of['active_mean']] = float(np.log1p(dt_linear[active_mask].mean())) + if idle_mask.any(): + out[i, idx_of['idle_mean']] = float(np.log1p(dt_linear[idle_mask].mean())) + duration_s = duration_ms / 1000.0 + if duration_s > 0: + out[i, idx_of['log_pkts_per_s']] = float(np.log1p(n / duration_s)) + total_bytes = float(sz.sum()) + out[i, idx_of['log_total_bytes']] = float(np.log1p(max(total_bytes, 0.0))) + out[:, idx_of['ack_cnt']] = _masked_channel_sum(packet_tokens[..., 7], lens).astype(np.float32) + out[:, idx_of['syn_cnt']] = _masked_channel_sum(packet_tokens[..., 3], lens).astype(np.float32) + return out + +def _masked_channel_sum(channel: np.ndarray, lens: np.ndarray) -> np.ndarray: + T = channel.shape[1] + mask = (np.arange(T)[None, :] < lens[:, None]).astype(np.float32) + return (channel.astype(np.float32) * mask).sum(axis=1) +__all__ = ['PACKET_FEATURE_NAMES', 'PACKET_D', 'PACKET_CONTINUOUS_CHANNEL_IDX', 'PACKET_BINARY_CHANNEL_IDX', 'CONTINUOUS_CHANNEL_IDX', 'BINARY_CHANNEL_IDX', 'CANONICAL_FLOW_FEATURE_NAMES', 'FLOW_D', 'FLOW_COUNT_FEATURE_NAMES', 'FLOW_COUNT_IDX', 'FLOW_CONTINUOUS_IDX', 'IDLE_THRESHOLD_MS', 'BENIGN_ALIASES', 'BENIGN_TOKEN', 'UNKNOWN_LABEL_TOKEN', 'normalize_label', 'canonical_5tuple', 'fit_packet_stats', 'zscore', 'apply_mixed_dequant', 'compute_flow_features_from_packets'] diff --git a/common/packet_store.py b/common/packet_store.py new file mode 100644 index 0000000..6456fa6 --- /dev/null +++ b/common/packet_store.py @@ -0,0 +1,209 @@ +from __future__ import annotations +import json +import shutil +from dataclasses import dataclass +from pathlib import Path +from typing import Iterable, Sequence +import numpy as np +import pandas as pd +import pyarrow as pa +import pyarrow.parquet as pq +DEFAULT_SHARD_SIZE = 100000 + +def _as_index_array(indices: Sequence[int] | np.ndarray) -> np.ndarray: + arr = np.asarray(indices, dtype=np.int64) + if arr.ndim != 1: + raise ValueError(f'indices must be 1-D, got shape {arr.shape}') + return arr + +class PacketShardWriter: + + def __init__(self, root: Path, *, shard_size: int=DEFAULT_SHARD_SIZE, T_full: int | None=None, D: int | None=None, overwrite: bool=False) -> None: + self.root = Path(root) + self.packet_dir = self.root / 'packets' + self.shard_size = int(shard_size) + if self.shard_size <= 0: + raise ValueError('shard_size must be positive') + if self.root.exists(): + if not overwrite: + raise FileExistsError(f'{self.root} already exists') + shutil.rmtree(self.root) + self.packet_dir.mkdir(parents=True, exist_ok=True) + self.T_full = T_full + self.D = D + self._n_flows = 0 + self._next_shard = 0 + self._pending_tokens: list[np.ndarray] = [] + self._pending_lengths: list[np.ndarray] = [] + self._pending_flows: list[pd.DataFrame] = [] + self._pending_n = 0 + self._manifest_writer: pq.ParquetWriter | None = None + self._flows_writer: pq.ParquetWriter | None = None + self._closed = False + + def add_batch(self, tokens: np.ndarray, lengths: np.ndarray, flows: pd.DataFrame) -> None: + if self._closed: + raise RuntimeError('cannot add_batch after close()') + tokens = np.asarray(tokens, dtype=np.float32) + lengths = np.asarray(lengths, dtype=np.int32) + if tokens.ndim != 3: + raise ValueError(f'tokens must be [N,T,D], got {tokens.shape}') + if lengths.shape != (tokens.shape[0],): + raise ValueError(f'lengths shape {lengths.shape} does not match N={tokens.shape[0]}') + if len(flows) != tokens.shape[0]: + raise ValueError(f'flows rows {len(flows)} does not match N={tokens.shape[0]}') + if tokens.shape[0] == 0: + return + if self.T_full is None: + self.T_full = int(tokens.shape[1]) + if self.D is None: + self.D = int(tokens.shape[2]) + if (tokens.shape[1], tokens.shape[2]) != (self.T_full, self.D): + raise ValueError(f'tokens shape {tokens.shape[1:]} does not match store shape {(self.T_full, self.D)}') + start = 0 + n = tokens.shape[0] + while start < n: + room = self.shard_size - self._pending_n + take = min(room, n - start) + end = start + take + self._pending_tokens.append(tokens[start:end]) + self._pending_lengths.append(lengths[start:end]) + self._pending_flows.append(flows.iloc[start:end].reset_index(drop=True)) + self._pending_n += take + start = end + if self._pending_n >= self.shard_size: + self._flush() + + def close(self) -> None: + if self._closed: + return + if self._pending_n: + self._flush() + if self._manifest_writer is not None: + self._manifest_writer.close() + if self._flows_writer is not None: + self._flows_writer.close() + meta = {'format': 'packet-shard-store-v1', 'n_flows': int(self._n_flows), 'T_full': int(self.T_full or 0), 'D': int(self.D or 0), 'dtype': 'float32', 'shard_size': int(self.shard_size), 'n_shards': int(self._next_shard), 'packet_dir': 'packets', 'shard_pattern': 'shard-{shard_id:06d}.npy'} + (self.root / 'metadata.json').write_text(json.dumps(meta, indent=2) + '\n') + self._closed = True + + def __enter__(self) -> 'PacketShardWriter': + return self + + def __exit__(self, exc_type, exc, tb) -> None: + if exc_type is None: + self.close() + + def _flush(self) -> None: + tokens = np.concatenate(self._pending_tokens, axis=0) + lengths = np.concatenate(self._pending_lengths, axis=0) + flows = pd.concat(self._pending_flows, ignore_index=True) + n = int(tokens.shape[0]) + shard_id = self._next_shard + rel_path = Path('packets') / f'shard-{shard_id:06d}.npy' + np.save(self.root / rel_path, tokens, allow_pickle=False) + flow_id = np.arange(self._n_flows, self._n_flows + n, dtype=np.uint64) + manifest = pd.DataFrame({'flow_id': flow_id, 'shard_id': np.full(n, shard_id, dtype=np.int32), 'row_in_shard': np.arange(n, dtype=np.int32), 'packet_length': lengths.astype(np.int32, copy=False)}) + if 'flow_id' in flows.columns: + flows = flows.drop(columns=['flow_id']) + flows.insert(0, 'flow_id', flow_id) + self._write_parquet_chunk('manifest', manifest, self.root / 'manifest.parquet') + self._write_parquet_chunk('flows', flows, self.root / 'flows.parquet') + self._n_flows += n + self._next_shard += 1 + self._pending_tokens.clear() + self._pending_lengths.clear() + self._pending_flows.clear() + self._pending_n = 0 + + def _write_parquet_chunk(self, kind: str, df: pd.DataFrame, path: Path) -> None: + table = pa.Table.from_pandas(df, preserve_index=False) + if kind == 'manifest': + if self._manifest_writer is None: + self._manifest_writer = pq.ParquetWriter(path, table.schema, compression='snappy') + self._manifest_writer.write_table(table) + elif kind == 'flows': + if self._flows_writer is None: + self._flows_writer = pq.ParquetWriter(path, table.schema, compression='snappy') + self._flows_writer.write_table(table) + else: + raise ValueError(kind) + +def write_packet_store_from_arrays(*, root: Path, tokens: np.ndarray, lengths: np.ndarray, flows: pd.DataFrame, shard_size: int=DEFAULT_SHARD_SIZE, overwrite: bool=False) -> None: + with PacketShardWriter(root, shard_size=shard_size, T_full=int(tokens.shape[1]), D=int(tokens.shape[2]), overwrite=overwrite) as writer: + writer.add_batch(tokens, lengths, flows) + +@dataclass +class PacketShardStore: + root: Path + metadata: dict + manifest: pd.DataFrame + + @classmethod + def open(cls, root: Path) -> 'PacketShardStore': + root = Path(root) + meta_path = root / 'metadata.json' + manifest_path = root / 'manifest.parquet' + flows_path = root / 'flows.parquet' + if not meta_path.exists(): + raise FileNotFoundError(meta_path) + if not manifest_path.exists(): + raise FileNotFoundError(manifest_path) + if not flows_path.exists(): + raise FileNotFoundError(flows_path) + metadata = json.loads(meta_path.read_text()) + manifest = pd.read_parquet(manifest_path) + expected = np.arange(len(manifest), dtype=np.uint64) + actual = manifest['flow_id'].to_numpy(dtype=np.uint64) + if not np.array_equal(actual, expected): + raise ValueError('manifest flow_id must be sequential and row-aligned') + return cls(root=root, metadata=metadata, manifest=manifest) + + @property + def n_flows(self) -> int: + return int(self.metadata['n_flows']) + + @property + def T_full(self) -> int: + return int(self.metadata['T_full']) + + @property + def D(self) -> int: + return int(self.metadata['D']) + + def shard_path(self, shard_id: int) -> Path: + return self.root / 'packets' / f'shard-{int(shard_id):06d}.npy' + + def read_flows(self, columns: list[str] | None=None) -> pd.DataFrame: + return pd.read_parquet(self.root / 'flows.parquet', columns=columns) + + def read_packets(self, indices: Sequence[int] | np.ndarray, *, T: int | None=None) -> tuple[np.ndarray, np.ndarray]: + idx = _as_index_array(indices) + if len(idx) == 0: + t = self.T_full if T is None else int(T) + return (np.zeros((0, t, self.D), dtype=np.float32), np.zeros((0,), dtype=np.int32)) + if idx.min() < 0 or idx.max() >= self.n_flows: + raise IndexError(f'indices out of range for n_flows={self.n_flows}') + t = self.T_full if T is None else int(T) + if t > self.T_full: + raise ValueError(f'requested T={t} > T_full={self.T_full}') + rows = self.manifest.iloc[idx] + out = np.empty((len(idx), t, self.D), dtype=np.float32) + lengths = np.minimum(rows['packet_length'].to_numpy(dtype=np.int32), t).astype(np.int32, copy=False) + pos = np.arange(len(idx), dtype=np.int64) + for shard_id in rows['shard_id'].unique(): + mask = rows['shard_id'].to_numpy() == shard_id + dest = pos[mask] + row_in_shard = rows.loc[mask, 'row_in_shard'].to_numpy(dtype=np.int64) + arr = np.load(self.shard_path(int(shard_id)), mmap_mode='r') + out[dest] = arr[row_in_shard, :t, :] + return (out, lengths) + +def is_packet_store(path: Path) -> bool: + path = Path(path) + return (path / 'metadata.json').exists() and (path / 'manifest.parquet').exists() + +def iter_store_roots(paths: Iterable[Path]) -> Iterable[Path]: + for path in paths: + if is_packet_store(path): + yield Path(path) diff --git a/paper/2026-f3241-paper.pdf b/paper/2026-f3241-paper.pdf new file mode 100644 index 0000000..58defd0 Binary files /dev/null and b/paper/2026-f3241-paper.pdf differ diff --git a/paper/2210.02747v2.pdf b/paper/2210.02747v2.pdf new file mode 100644 index 0000000..ebf48b0 Binary files /dev/null and b/paper/2210.02747v2.pdf differ diff --git a/paper/Contextual_Masking_Distillation_for_Network_Traffic_Anomaly_Detection.pdf b/paper/Contextual_Masking_Distillation_for_Network_Traffic_Anomaly_Detection.pdf new file mode 100644 index 0000000..f2c4af6 Binary files /dev/null and b/paper/Contextual_Masking_Distillation_for_Network_Traffic_Anomaly_Detection.pdf differ diff --git a/paper/Explainable_Anomaly_Detection_in_Network_Traffic_Using_Normalizing_Flows.pdf b/paper/Explainable_Anomaly_Detection_in_Network_Traffic_Using_Normalizing_Flows.pdf new file mode 100644 index 0000000..a80135c Binary files /dev/null and b/paper/Explainable_Anomaly_Detection_in_Network_Traffic_Using_Normalizing_Flows.pdf differ diff --git a/paper/SURVEY.md b/paper/SURVEY.md new file mode 100644 index 0000000..add1b08 --- /dev/null +++ b/paper/SURVEY.md @@ -0,0 +1,210 @@ +# JANUS Paper — Survey, Pain Points, and Outline + +**Date**: 2026-05-04 +**Scope**: Field survey + framing for the JANUS paper (Mixed-CFM + DFM + causal-packet attention + Mahalanobis-OAS aggregator). +**Target sections**: Introduction · Background · Methodology · Evaluation. + +> Framing rule (from `RESULTS.md` caveats): the headline claim is **cross-dataset robustness + first FM/DFM in NIDS**, not "4/4 within SOTA". Within-dataset is saturated; the discriminating axis is cross-dataset. + +--- + +## Part A. State of the field (2024–2026) + +### A.1 Method families and where each one is stuck + +| Family | Recent representative work | Core mechanism | Documented short-coming | +|---|---|---|---| +| **Normalizing Flows (NF)** | **Shafir et al. T-Netw 2026** (our main baseline), NF-NIDS (IAF/NSF), PrivFlow-NIDS (Springer 2025) | Explicit log-likelihood on benign; anomaly = low likelihood | Likelihood ≠ anomaly score; coupling-layer architecture brittle; categorical / flag fields handled crudely | +| **Reconstruction (AE / VAE / MemAE)** | KitNET, MemAE, SparseMemAE | Reconstruction error as anomaly score | "Identity mapping trap" — OOD samples can be perfectly reconstructed (NeurIPS '24, OpenReview '25 multi-paper consensus) | +| **Diffusion** | **ConMD (TIFS 2026)** (our main baseline), DMAD (IJCAI '25 survey), UnDiff (WWW '25), RDUAE | Denoising / score-based density | Slow inference; multi-step trace storage; training instability | +| **GAN** | **TIPSO-GAN (NDSS 2026)** (our main baseline), DEGAN | Discriminator score / reconstruction | Mode collapse and training instability persist; TIPSO-GAN spends extra optimisation budget (PSO) to mitigate | +| **Self-supervised contrastive** | Self-Supervised Transformer Contrastive Learning (NetSci '25), GraphIDS (NeurIPS 2025), SSGMHAN | Representation learning, downstream OCSVM / Mahal | Two-stage pipeline (rep + detector); no end-to-end anomaly score | +| **Foundation models** | Traffic-MoE, ETC-IMC, Language-of-Network GBC | Pre-train / fine-tune over packet-byte tokens | Resource-heavy; primary task is encrypted-traffic classification, not AD | +| **Knowledge distillation** | ConMD (TIFS 2026), Spatial-Temporal KD | Teacher → student alignment | Sensitive to teacher quality; two-stage | +| **Flow Matching (FM)** ✨ | **TCCM (NeurIPS 2025) tabular**, rFM (image AD 2025), Lipman 2023 | Velocity-field regression, one-step deviation as score | **No application to NIDS yet — the gap JANUS fills** | +| **Discrete FM (DFM)** ✨ | Gat et al. NeurIPS 2024, Fisher Flow Matching, FlowMol (molecules 2024–2025) | FM over discrete state space | **No application to NIDS yet** | + +### A.2 Dataset / benchmark situation + +- Within-dataset is saturated. Shafir 0.93 → JANUS 0.99 → TIPSO-GAN F1 = 0.99 on CICIDS2017 / CICDDoS2019 are all within seed noise of each other. +- CICIDS2017 has documented benchmark bias (synthetic vs real-world traffic distribution mismatch — arXiv 2403.17458 "Expectations Versus Reality"; multiple 2024–2025 critiques). +- Cross-dataset is now the field's chosen discriminating axis. HDSE-IDS, Transformer-IDS w/ calibration, Few-shot multi-domain fusion (PLOS One 2025), and the cross-dataset generalisation review (arXiv 2402.10974) all centre on the same problem. +- AUROC alone is increasingly seen as inadequate for operational reporting; thresholded F1, Precision, Recall, and TPR @ FPR = 1% are demanded by the SOC community. + +### A.3 Operational pain points (the "why this matters" stack) + +| Pain point | Quantified evidence | Source | +|---|---|---| +| Operational FPR | ~99 % of NIDS alerts are FP; one OT refinery reported 27 000 alerts → 76 real; 51 % of SOC teams describe alert volume as unmanageable | Trend Micro 2024, OT IDS surveys 2024–2025 | +| Cross-domain deployment collapse | Single-dataset trained IDSes typically drop 0.10–0.30 AUROC across environments | Tandfonline 2025; MDPI 2025 / 8466 | +| Concept drift | Models become outdated post-deployment, requiring re-training | MDPI Future Internet 2025 / 328 | +| IID-flow assumption mismatch | Multi-stage attacks broken into IID flows lose relational and temporal structure | DevSecOps NIDS guide 2026 | +| Encrypted + heterogeneous protocols (IoT) | Packet-level features lose access to plaintext; protocol/device heterogeneity breaks unified models | Wiley 2025 IoT NIDS review | + +--- + +## Part B. Pain points × JANUS capability mapping + +> **P2 is our most distinctive observation** — the community talks about cross-domain failure, but no one has clearly characterised "density score implicitly learns source-likeness". This is the mechanism-level explanation that earns naming rights and serves as the introduction's hook. + +| # | Pain point (community consensus) | Failure mode of current SOTA | JANUS mechanism | Evidence in our artifacts | +|---|---|---|---|---| +| **P1** | Cross-dataset / domain-shift collapse | Shafir NF: cross 0.89 (forward) / 0.93 (reverse, single-direction); legacy `terminal_norm` 0.62 (reverse) | DFM head emits `disc_nll` (protocol-flag distribution is transfer-stable); Mahalanobis-OAS re-weights on target benign | `RESULTS.md` §C: reverse 0.62 → 0.93 (+0.31); forward 0.89 → 0.96 (+0.07); 12 off-diagonal cells avg +0.175 | +| **P2** | "Source-likeness collapse" of density scores (new framing) | terminal density score across **3** distinct backbones in reverse cross all ≤ 0.63 — effectively a source-domain classifier | DFM decouples protocol semantics; Mahalanobis fuses multiple complementary scores | 3-backbone × 16-score validation: terminal_norm 0.519–0.626 all collapse; `disc_nll` is only single-score that stays stable (0.903) | +| **P3** | Continuous + discrete protocol fields squashed into one likelihood | NF / AE Gaussianise TCP flags / direction, losing semantics | **Mixed CFM**: continuous head over (size, IAT, win) + **DFM head** over 6 binary flag/direction channels, jointly trained | `sigma=0.1`, `λ_disc=1.0`; DFM head is the only transfer-stable single score on reverse cross (0.9191) | +| **P4** | Reconstruction-based AD has identity-mapping trap | AE perfectly reconstructs OOD (NeurIPS '24, OpenReview '25) | FM is not reconstruction — velocity field, terminal point is source noise rather than the input | First FM application to packet-sequence NIDS | +| **P5** | Multi-score selection bias (post-hoc best-fixed channel) | Shafir 5-feature ensemble is post-hoc; our best-fixed AUROC 0.99 also selection-biased | Mahalanobis-OAS fits once on benign val; **never sees attack labels** | `RESULTS.md` §A: OAS yields +0.054 to +0.118 over Shafir on 4 within-dataset benchmarks with one deployable scalar score | +| **P6** | High operational FPR | 99 %-FP industrial reality | Thresholded F1 / Precision / Recall (τ = benign-val P95 / P99) | `RESULTS_THRESHOLDED.md`: CICDDoS2019 within τ=P95 F1 = 0.993; cross F1 = 0.632 (precision ≈ 0.95) | +| **P7** | Run-to-run variance / poor reproducibility | Multi-seed std large in many baselines | Causal-packet attention shrinks std 1.6–8× | `RESULTS.md` "Stability": CICIoT2023 std 0.0017 → 0.0002 (8×) | +| **P8** | NF likelihood is not necessarily anomaly | Discussed since NFAD 2021; Shafir bypasses with Shapley feature ensemble | We provide a 10-d score family + Mahalanobis routing; not dependent on a single likelihood | `CROSS_MATRIX.md` 12 cells across 4×4 matrix | + +--- + +## Part C. Paper outline + +### §1 Introduction (~1.5 pages) + +Argument chain by paragraph: + +1. **Hook**: 99 % FPR + cross-domain deployment collapse. One sentence with the alert-fatigue numbers makes the relevance immediate. +2. **Common failure mode of current methods**: AE has identity trap; NF likelihood becomes a source-likeness classifier under cross-domain shift (this is P2; insert a teaser figure of `terminal_norm` 4×4 cross matrix where many off-diagonal cells are ≤ 0.55). +3. **Missing tool**: FM / DFM are validated for image / molecule / tabular AD, but never applied to NIDS; protocol fields are intrinsically mixed continuous + discrete, exactly the setting mixed FM was designed for. +4. **JANUS in one sentence**: jointly trained continuous CFM + discrete FM with causal-packet attention, aggregated by a benign-only Mahalanobis-OAS scalar. +5. **Contributions** (3–4 bullets): + - **(C1)** First Flow-Matching paradigm for packet-level NIDS; first DFM modelling of protocol flag / direction channels. + - **(C2)** We characterise the *source-likeness collapse* of terminal density scores at architecture level (3 backbones × 16 scores), and show how DFM + Mahalanobis routing breaks it. + - **(C3)** Mahalanobis-OAS as a benign-only single-scalar aggregator; the unsupervised contract is preserved (no attack labels at any step). + - **(C4)** On a 4×4 cross-dataset matrix, JANUS averages **+0.175** AUROC over `terminal_norm`; reverse direction +0.31. Within-dataset matches or exceeds the NF SOTA (Shafir 2026) by 0.054–0.118 on 3/3 directly comparable benchmarks. +6. **Outline + one-sentence takeaway**. + +**Figure 1 placeholder** (end of §1): cross 4×4 heatmap, left `terminal_norm`, right `JANUS + Mahalanobis-OAS`, Δ in colour. The visceral hook. + +--- + +### §2 Background & Related Work (~1.5 pages) + +Four 1-paragraph subsections. + +**§2.1 Unsupervised Network Anomaly Detection** +- Reconstruction (AE / MemAE / Kitsune) — cite identity-mapping critiques as baseline limitation. +- Density estimation with NF — Shafir 2026 + NF-NIDS as SOTA, but likelihood ≠ anomaly and categorical fields suffer. +- GAN-based — TIPSO-GAN (mode collapse, optimisation cost). +- Diffusion — DMAD survey + ConMD; high inference cost; AD remains image-centric. +- Self-supervised contrastive — primarily representation learning, not direct anomaly scoring. + +**§2.2 Flow Matching & Discrete Flow Matching** +- Lipman 2023, OT-CFM (Tong et al. TMLR '24). +- Discrete Flow Matching (Gat et al. NeurIPS '24). +- FM for AD: TCCM NeurIPS '25 (tabular), rFM 2025 (image) — **highlight that NIDS remains untouched**. +- Mixed continuous + discrete FM (FlowMol 2024–2025) — sets up the naming for our Mixed_CFM. + +**§2.3 Cross-Dataset Generalisation in NIDS** +- HDSE-IDS, Transformer-IDS w/ calibration, Few-shot multi-domain fusion (2025). +- Cite arXiv 2402.10974 / 2403.17458 (Cross-Dataset Generalisation, "Expectations Versus Reality"). +- **Our framing**: prior work treats cross as a representation problem; nobody characterises the score-level source-likeness collapse. + +**§2.4 Anomaly Score Aggregation** +- Mahalanobis-based AD (MICCAI '24 brain MRI; M-SVDD 2025). +- OAS / Ledoit-Wolf shrinkage covariance. +- **Position**: Mahalanobis is widespread in image AD; never systematically applied as a benign-only aggregator over an FM score vector in NIDS. + +--- + +### §3 Methodology (~3 pages) + +**§3.1 Problem formulation** +- Input: packet sequences `[N, T, 9]` + flow metadata; benign-only training; unsupervised inference returns one scalar. +- 9-d packet schema (`common/data_contract.py`): 3 continuous (size, IAT, win) + 6 discrete (direction + 5 TCP flags). +- The mixed-modality nature is *intrinsic* to the protocol — not a modelling choice. + +**§3.2 The JANUS architecture** +- (a) **Backbone**: causal-packet Transformer over `[FLOW_TOKEN, P_1, …, P_T]`. Figure 2 = model overview. +- (b) **Continuous head (CFM)**: OT-CFM, σ=0.1, on the 3 continuous channels. +- (c) **Discrete head (DFM)**: Discrete Flow Matching with linear interpolation probability path on the 6 binary channels; cross-entropy loss with `λ_disc = 1.0`. +- (d) **Why mixed FM**: small ablation showing `λ_disc = 0` vs `λ_disc = 1.0`, demonstrating that flag fields cannot be Gaussianised. + +**§3.3 Score family** +- Enumerate the 10-d score vector: `terminal_norm`, `terminal_packet`, `terminal_flow`, `kinetic_*`, `disc_nll_total`, … +- Each captures a different physical quantity (density / kinetic / discrete-flag distribution). +- **Source-likeness collapse — formal observation**: under target-domain benign drift, `terminal_norm` degrades into a `1{x ∈ source distribution}` proxy and loses its anomaly signal. Evidence: 4×4 matrix and 3-backbone validation. + +**§3.4 Mahalanobis-OAS aggregator** +- Fit OAS-shrunk Mahalanobis on **target** benign val: `score = d²(s(x), µ_benign)`. +- The aggregator never sees attack labels. +- Selection: 5 benign-only aggregators evaluated (max-z, plain Mahalanobis, Ledoit-Wolf, OAS, score-subset variants); OAS performs best with sensitivity ≤ 0.005 vs Ledoit-Wolf (`SCORE_ROUTER.md`). + +**§3.5 Causal-packet attention as a stabiliser** +- Define the protocol-causal mask. Show std reduction 1.6–8× across 4 datasets (`RESULTS.md` "Stability"). + +--- + +### §4 Evaluation (~3–4 pages) + +**§4.1 Datasets, baselines, protocol** +- 4 datasets: ISCXTor2016, CICIDS2017, CICDDoS2019, CICIoT2023; canonical `packets.npz` 9-d schema. +- Baselines (locked from PDFs in `paper/`): Shafir NF (T-Netw 2026), ConMD (TIFS 2026), TIPSO-GAN (NDSS 2026), Kitsune, AE / MemAE, OCSVM. +- Protocol: 10K benign train (matches Shafir), 3 seeds, AUROC primary + thresholded F1 / Precision / Recall @ P95 / P99. + +**§4.2 Within-dataset (Table 1)** +- 4 datasets × {Shafir / ConMD / TIPSO-GAN / Kitsune / AE / **JANUS + Mahal-OAS** / JANUS best-fixed}. +- Honest framing: "JANUS matches or exceeds the NF SOTA on 3/3 directly comparable benchmarks; CICIoT2023 reported as additional benchmark due to metric mismatch (Caveat 1, `RESULTS.md`)". +- One sentence acknowledging that "within-dataset is saturated; the discriminating axis is cross-dataset (next section)". + +**§4.3 Cross-dataset (Table 2 + Figure 3)** +- The headline of the paper. Table = 4×4 matrix Mahal vs `terminal_norm`. +- Figure 3 = detailed version of Figure 1. +- Critical details: + - Forward IDS17 → DDoS19: +0.07 over Shafir (genuine SOTA). + - Reverse DDoS19 → IDS17: 0.93 = Shafir 0.93 (matches, does not exceed — Caveat 2). + - 12 off-diagonal cells average +0.175 over `terminal_norm`. + - 4 "collapse cells" (≤ 0.57) all recovered to ≥ 0.75. + +**§4.4 Mechanism analysis (Table 3 + Figure 4)** +- Source-likeness collapse: 3 backbones × 16 scores matrix. +- DFM head ablation: `λ_disc ∈ {0, 0.5, 1.0, 2.0}` vs reverse-cross AUROC. +- Mahalanobis aggregator ablation: max-z / plain Mahal / Ledoit-Wolf / OAS — sourced from `SCORE_ROUTER.md`. + +**§4.5 Ablations & robustness** +- σ sensitivity (`sigma_validation.md` 4×2 table). +- Causal-packet attention contribution to std reduction (`RESULTS.md` Stability). +- Per-attack-family table (`RESULTS.md` "Per-attack-family pattern" — SSH-Patator counter-example). + +**§4.6 Thresholded metrics & operational impact** +- `RESULTS_THRESHOLDED.md` F1 / Precision / Recall @ P95. +- Direct dialogue with industry alert-fatigue numbers: "at the P95 threshold, our cross precision ≈ 0.95". + +**§4.7 Discussion (sub-section, 1 paragraph)** +- Limitations: aggregator post-hoc selection, target-benign-calibrated transfer (not zero-shot — Caveat 3), CICIoT2023 metric mismatch. +- Honest reporting here closes the door on reviewer attacks. + +--- + +## Part D. Writing red lines (from project memory) + +1. **Never** write "zero-shot transfer" — write "calibrated cross-domain transfer" (Mahalanobis is fit on target benign). +2. **Never** claim "+SOTA on CICIoT2023" — write "additional benchmark; metric mismatch (Shafir F1 vs our AUROC)". +3. Reverse cross is "matches Shafir 0.93", not "beats". Our +0.31 is vs our own legacy. +4. Best-fixed numbers are an ablation upper bound, never the SOTA claim. +5. Mahalanobis-OAS was post-hoc-selected — write "we evaluated 5 benign-only aggregators; OAS performed best with sensitivity ≤ 0.005 vs Ledoit-Wolf". + +--- + +## Part E. Sources + +- Shafir, Giryes, Wool — *Explainable Anomaly Detection in Network Traffic Using Normalizing Flows*, IEEE T-Netw 2026 (PDF in `paper/`). +- Lian et al. — *Contextual Masking Distillation for Network Traffic Anomaly Detection*, IEEE TIFS 2026. +- *TIPSO-GAN: Malicious Network Traffic Detection*, NDSS 2026. +- Gat et al. — *Discrete Flow Matching*, NeurIPS 2024. +- *Scalable, Explainable and Provably Robust Anomaly Detection with One-Step Flow Matching*, NeurIPS 2025 (arXiv 2510.18328). +- *How and Why: Taming Flow Matching for Unsupervised Anomaly Detection* (rFM), arXiv 2508.05461. +- *On the Cross-Dataset Generalization of Machine Learning for Network Intrusion Detection*, arXiv 2402.10974. +- *Expectations Versus Reality: Evaluating Intrusion Detection Systems in Practice*, arXiv 2403.17458. +- HDSE-IDS — *Heterogeneous Deep Stacked Ensemble for Cross-Domain IDS*, Connection Science 2025. +- *Self-Supervised Transformer-based Contrastive Learning for IDS*, arXiv 2505.08816. +- *GraphIDS: Self-supervised GNN for Network Intrusion Detection*, NeurIPS 2025. +- *Network traffic foundation models: A systematic review*, ScienceDirect 2026. +- Tong et al. — *Improving and Generalizing Flow-Based Generative Models with Minibatch Optimal Transport*, TMLR 2024. +- *DMAD: Diffusion Models for Anomaly Detection (survey)*, IJCAI 2025. +- *Alert Fatigue in Security Operations Centres: Research Challenges and Opportunities*, ACM Computing Surveys 2024. +- *Beyond the Norm: Unsupervised Anomaly Detection in Telecommunications with Mahalanobis Distance*, MDPI Computers 2025. +- *Autoencoders for Anomaly Detection are Unreliable*, OpenReview 2025. diff --git a/paper/background_related.md b/paper/background_related.md new file mode 100644 index 0000000..871db0b --- /dev/null +++ b/paper/background_related.md @@ -0,0 +1,125 @@ +## 2 Background + +### 2.1 Unsupervised network anomaly detection + +We consider the standard unsupervised setting: a detector is trained only on +benign traffic and, at inference time, must assign an anomaly score to each +flow without access to attack labels at any stage of training. Public +benchmarks (e.g., CIC-IDS2017, CIC-DDoS2019, ISCXTor2016) provide labelled +attack traffic for evaluation only. Two granularities dominate the +literature: flow-level detectors operate on per-flow aggregate features +(byte counts, inter-arrival statistics, flag tallies), while packet-level +detectors operate on the ordered sequence of per-packet features inside a +flow and retain temporal structure that flow aggregates discard. + +Within-dataset AUROC on the standard benchmarks has narrowed to within +seed noise across recent recipes; the substantive evaluation axis is now +cross-dataset transfer, in which a detector is trained on one environment +and evaluated on traffic from another. Performance on this axis has not +converged. + +### 2.2 Continuous Flow Matching + +Continuous Flow Matching (CFM) trains a time-dependent vector field +$v_\theta(x, t)$ to transport a tractable source distribution (typically +$\mathcal{N}(0, I)$) to the data distribution along an ODE +$\mathrm{d}x_t = v_\theta(x_t, t)\,\mathrm{d}t$. The training objective +regresses $v_\theta$ onto a target velocity defined along a chosen +conditional probability path; for the linear (Gaussian) path this reduces +to a simple least-squares loss, side-stepping the score-matching objective +and stochastic sampler of diffusion models. OT-CFM straightens +trajectories by pairing source and data samples through minibatch optimal +transport, which lowers integration error and enables stable few-step +inference. + +A trained CFM model gives access not only to the learned density but to +a family of geometric quantities along the trajectory: terminal velocity +norm, divergence, curvature, and Jacobian-trace estimators. These can be +read off the velocity field without retraining. + +### 2.3 Discrete Flow Matching + +Continuous FM does not apply to categorical state spaces, where adding +Gaussian noise is undefined. Discrete Flow Matching (DFM) generalises +the framework to finite alphabets through continuous-time Markov chains: +the model parameterises token-level transition rates that interpolate +between a source distribution (typically uniform) and the data +distribution. The training objective remains a simple regression onto +target rates derived from a chosen interpolation schedule. DFM has been +validated on language and molecular generation; mixed +continuous–discrete data, where each observation has both numerical and +categorical channels, is the natural composition of CFM and DFM. + +--- + +## 3 Related Work + +### 3.1 Reconstruction-based detectors + +Autoencoder-style detectors learn to reconstruct benign inputs and score +anomalies by reconstruction error. Kitsune popularised the design for +online NIDS using an ensemble of small autoencoders, and MemAE introduced +a learned memory bank to constrain the latent representation to the +benign manifold. The family suffers from a documented identity-mapping +failure: sufficiently expressive autoencoders reconstruct out-of- +distribution inputs near-perfectly, eroding the gap between benign and +anomalous reconstruction error. Recent critiques argue that this +behaviour is structural rather than a hyperparameter artefact, and that +reconstruction error is therefore an unreliable anomaly score in +general. + +### 3.2 Density-based detectors + +Three deep generative families currently hold the public SOTA on +NIDS benchmarks. **Normalising flows** fit an explicit invertible +density on benign traffic and score by negative log-likelihood; the +strongest recent pipeline reports 0.93 within-dataset AUROC on +CIC-DDoS2019 with cross-domain transfer in the 0.89–0.93 range. +**Diffusion-based detectors** include contextual masking distillation +schemes that compare a student denoiser against a benign-trained +teacher, alongside a broader 2025 survey of diffusion AD variants. +**GAN-based detectors**, exemplified by recent NDSS work that augments +the optimisation with particle-swarm search, score by discriminator +output or cycle-reconstruction error. All three families reduce a +packet stream to a single scalar derived from one homogeneous +probabilistic model fit to benign data, and the reported log-likelihood +is known to dissociate from anomaly status once the benign distribution +drifts. + +A separate line of work uses self-supervised contrastive +representations, graph neural networks, or pre-trained traffic +foundation models, with anomaly scoring delegated to a downstream +detector such as OCSVM or Mahalanobis distance. These pipelines are +typically two-stage, are primarily evaluated on encrypted-traffic +classification rather than open-set anomaly detection, and are not the +focus of the cross-dataset robustness comparison we pursue. + +### 3.3 Flow Matching for anomaly detection + +Outside NIDS, two recent works adopt Flow Matching as the AD objective. +A time-reversed FM detector for image anomaly detection couples +worst-transport coupling with a high-dimensional latent, scoring by +deviation from the learned velocity field. A tabular detector built on +one-step FM offers explainability and provable robustness guarantees on +heterogeneous structured data. Both validate FM-based scoring as +competitive with reconstruction- and density-based baselines in their +respective regimes. Discrete Flow Matching has been validated on +language and molecular generation but not, to our knowledge, evaluated +as an anomaly-detection objective. No prior work applies either +continuous or discrete FM to packet-sequence NIDS. + +### 3.4 Cross-dataset robustness in NIDS + +As within-dataset metrics have saturated, cross-dataset evaluation has +emerged as the field's discriminating axis. A 2024 systematic study +measures the generalisation gap across the standard NIDS benchmarks +under matched feature schemas and reports AUROC drops of 0.10–0.30 +when detectors trained on one environment are evaluated on another. +Subsequent work on heterogeneous deep stacked ensembles, calibrated +transformers, and few-shot multi-domain fusion targets the same gap +through architectural or training-time interventions. The phenomenon is +broadly observed and quantified; what is missing from the literature is +a mechanism-level account of why density-based scores in particular +degrade under domain shift, as opposed to an accumulation of empirical +remedies. The pilot study in §X revisits this gap directly and frames +the structural failure mode that the rest of the paper addresses. diff --git a/paper/figs/fig_data_efficiency.pdf b/paper/figs/fig_data_efficiency.pdf new file mode 100644 index 0000000..ff5f162 Binary files /dev/null and b/paper/figs/fig_data_efficiency.pdf differ diff --git a/paper/figures/figure1.pdf b/paper/figures/figure1.pdf new file mode 100644 index 0000000..5656767 Binary files /dev/null and b/paper/figures/figure1.pdf differ diff --git a/paper/figures/figure1_overview_v2.log b/paper/figures/figure1_overview_v2.log new file mode 100644 index 0000000..c5c6da8 --- /dev/null +++ b/paper/figures/figure1_overview_v2.log @@ -0,0 +1,16 @@ +This is pdfTeX, Version 3.141592653-2.6-1.40.22 (TeX Live 2022/dev/Debian) (preloaded format=pdfetex 2026.1.31) 2 MAY 2026 16:36 +entering extended mode + restricted \write18 enabled. + %&-line parsing enabled. +**&pdfetex figure1_overview_v2.tex +(./figure1_overview_v2.tex +! Undefined control sequence. +l.24 \documentclass + [border=6pt]{standalone} +? +! Emergency stop. +l.24 \documentclass + [border=6pt]{standalone} +End of file on the terminal! + +! ==> Fatal error occurred, no output PDF file produced! diff --git a/paper/figures/figure1_overview_v2.tex b/paper/figures/figure1_overview_v2.tex new file mode 100644 index 0000000..be2881f --- /dev/null +++ b/paper/figures/figure1_overview_v2.tex @@ -0,0 +1,736 @@ +% Figure 1 (v2): Mixed_CFM — system overview. +% Compile: pdflatex figure1_overview_v2.tex +% +% Layout: +% row 1: (a) Tokenization ──x_1──→ (b) Mixed-state path ★ +% │ +% x_t +% ▼ +% row 2: (c) Causal-Packet Velocity Field + joint loss ★ +% │ +% frozen +% ▼ +% row 3: (d) Inference: score → Mahalanobis-OAS router ★ +% +% Coordinate hygiene: +% - Each panel is its own sub-tikzpicture with a fixed bounding box +% (\useasboundingbox 0..W x 0..H, in mm). Editing one panel cannot +% disturb another. +% - The OUTER tikzpicture only chains panels with `right=of` / `below=of` +% and draws three inter-panel data-flow arrows. +% - Numbers in the figure are architecture descriptors only (T, 21-d, +% 10-d, lambda). No experimental result values. + +\documentclass[border=6pt]{standalone} +\usepackage{tikz} +\usetikzlibrary{arrows.meta, positioning, calc} +\usepackage{amsmath, amssymb} + +% --- palette ----------------------------------------------------------------- +\definecolor{cOrange}{RGB}{230, 126, 34} % discrete (DFM) +\definecolor{cBlue} {RGB}{ 52, 110, 180} % continuous (CFM) +\definecolor{cPurple}{RGB}{142, 68, 173} +\definecolor{cGray} {RGB}{170, 170, 170} +\definecolor{cBgPanel}{RGB}{252, 252, 250} +\definecolor{cBg} {RGB}{248, 248, 248} + +\newcommand{\contrib}{\textcolor{cOrange}{\ensuremath{\bigstar}}} + +\tikzset{ + panel/.style ={rectangle, draw=black!35, line width=0.5pt, rounded corners=2pt, + fill=cBgPanel, inner sep=0pt}, + novelpanel/.style ={rectangle, draw=cOrange, line width=1.0pt, rounded corners=2pt, + fill=cOrange!4, inner sep=0pt}, + paneltag/.style ={font=\sffamily\bfseries\footnotesize, anchor=north west, + text=black!75, inner sep=0pt}, + paneltagN/.style ={font=\sffamily\bfseries\footnotesize, anchor=north west, + text=cOrange!85!black, inner sep=0pt}, + archbox/.style ={rectangle, draw=black!70, thick, rounded corners=2pt, + align=center, font=\scriptsize, fill=white, + minimum height=8mm}, + novelbox/.style ={rectangle, draw=cOrange, line width=1.2pt, rounded corners=2pt, + align=center, font=\scriptsize, fill=cOrange!10, + minimum height=8mm}, + arrow/.style ={->, thick, black!65, >={Stealth[length=2.4mm]}}, + thinarrow/.style ={->, line width=0.5pt, black!55, >={Stealth[length=1.8mm]}}, + flowarrow/.style ={->, line width=1.2pt, black!70, >={Stealth[length=2.8mm]}}, + losseq/.style ={fill=cBg, draw=black!30, rounded corners=2pt, + inner sep=4pt, font=\scriptsize, align=left}, +} + +% ============================================================================= +% Panel (a): Tokenization (W=70mm, H=42mm) +% ============================================================================= +\newcommand{\panelAcontent}{% +\begin{tikzpicture}[x=1mm, y=1mm, + panelarrow/.style={->, line width=0.5pt, black!55, + >={Stealth[length=1.8mm]}}] + \useasboundingbox (0,0) rectangle (70, 42); + \node[paneltag] at (1, 41) {(a) Tokenization}; + + % --- pcap icon ------------------------------------------------------------ + \begin{scope}[shift={(1, 14)}] + \fill[cBlue!12] (0,0) -- (0,18) -- (8,18) -- (10.8,15) -- (10.8,0) -- cycle; + \draw[cBlue!80, line width=0.5pt] + (0,0) -- (0,18) -- (8,18) -- (10.8,15) -- (10.8,0) -- cycle; + \fill[cBlue!30] (8,18) -- (8,15) -- (10.8,15) -- cycle; + \node[font=\bfseries\tiny, text=cBlue!90!black] at (5.4, 10) {flow.pcap}; + \foreach \y/\rl in {7/7, 5.5/5.5, 4/7, 2.5/4.5, 1/6} { + \draw[cBlue!45, line width=0.25pt] (1, \y) -- (1+\rl, \y); + } + \end{scope} + + % --- arrow: parse --------------------------------------------------------- + \draw[panelarrow] (12.5, 23) -- (16.5, 23) + node[midway, above=0.3mm, font=\tiny\itshape, text=black!65] {parse}; + + % --- packet stream (8 packets, each = 9-d feature vector) ---------------- + % Color encoding consistent with panel (b): + % BLUE = 3 continuous channels (log_size, log_dt_ms, log_win) + % ORANGE = 6 discrete channels (direction + 5 TCP flag bits S/F/R/P/A) + \begin{scope}[shift={(17, 14)}] + \foreach \i in {0,...,7} { + \pgfmathsetmacro{\xx}{\i*3} + \draw[fill=white, draw=black!50, line width=0.3pt, rounded corners=0.4pt] + (\xx, 0) rectangle (\xx+2.5, 18); + % --- top: 3 BLUE continuous bands (size, IAT, win) ------------------ + \fill[cBlue!65] (\xx+0.3, 14.3) rectangle (\xx+2.2, 16.6); % log_size + \fill[cBlue!65] (\xx+0.3, 11.7) rectangle (\xx+2.2, 14.0); % log_dt_ms (IAT) + \fill[cBlue!65] (\xx+0.3, 9.1) rectangle (\xx+2.2, 11.4); % log_win + % thin separator between cont and disc sections + \draw[black!25, line width=0.2pt] (\xx+0.3, 8.0) -- (\xx+2.2, 8.0); + % --- bottom: 6 ORANGE discrete cells (dir + 5 TCP flags) ------------ + \foreach \k in {0,...,5} { + \pgfmathsetmacro{\fx}{\xx + 0.3 + \k*0.32} + \pgfmathsetmacro{\on}{int(mod(\i*3+\k,2))} + \ifnum\on=1 + \fill[cOrange!75] (\fx, 1.5) rectangle (\fx+0.30, 7.0); + \else + \draw[cOrange!55, line width=0.2pt, fill=white] + (\fx, 1.5) rectangle (\fx+0.30, 7.0); + \fi + } + } + \draw[->, line width=0.3pt, black!55] (-0.3, -2.5) -- (24.3, -2.5) + node[anchor=west, font=\tiny, text=black!55, xshift=-0.5mm] {time}; + \end{scope} + % --- inline mini-key showing what blue/orange mean inside one packet ---- + % Placed in the empty band between the panel tag (y=41) and the packet + % stream top (y=32) so it does not crowd either. + \node[font=\tiny, text=cBlue!85!black, anchor=west] at (17, 38) + {\rule{1.6mm}{1.6mm}\,3 cont: size, IAT, win}; + \node[font=\tiny, text=cOrange!85!black, anchor=west] at (17, 35) + {\rule{1.6mm}{1.6mm}\,6 disc: dir + S F R P A}; + + % --- arrow: tokenize ------------------------------------------------------ + \draw[panelarrow] (43, 23) -- (47, 23) + node[midway, above=0.3mm, font=\tiny\itshape, text=black!65] {tokenize}; + + % --- token sequence: FLOW + 3 packet tokens + PAD ------------------------ + \begin{scope}[shift={(48, 11)}] + \fill[cBlue!75] (0, 22) rectangle (3, 23.6); + \foreach \i in {0,...,19} { + \pgfmathsetmacro{\yp}{20.8 - \i*1.0} + \fill[cBlue!25] (0, \yp) rectangle (3, \yp+0.9); + } + \node[font=\tiny, text=cBlue!90!black, anchor=north] at (1.5, 0.3) {FLOW}; + \foreach \k/\xoff/\lbl in {1/5/{$P_1$}, 2/9/{$P_2$}, 3/15/{$P_T$}} { + \fill[cOrange!75] (\xoff, 22) rectangle (\xoff+3, 23.6); + \foreach \i in {0,...,2} { + \pgfmathsetmacro{\yp}{20.8 - \i*1.0} + \fill[cBlue!25] (\xoff, \yp) rectangle (\xoff+3, \yp+0.9); + } + \foreach \i in {3,...,8} { + \pgfmathsetmacro{\yp}{20.8 - \i*1.0} + \fill[cOrange!30] (\xoff, \yp) rectangle (\xoff+3, \yp+0.9); + } + \foreach \i in {9,...,19} { + \pgfmathsetmacro{\yp}{20.8 - \i*1.0} + \fill[cGray!28] (\xoff, \yp) rectangle (\xoff+3, \yp+0.9); + } + \node[font=\tiny, anchor=north] at (\xoff+1.5, 0.3) {\lbl}; + } + \node[font=\Large, text=black!55] at (13, 11) {$\cdots$}; + \draw[fill=cGray!22, draw=cGray, dash pattern=on 0.6pt off 0.6pt, line width=0.3pt] + (19, 22) rectangle (22, 23.6); + \foreach \i in {0,...,19} { + \pgfmathsetmacro{\yp}{20.8 - \i*1.0} + \fill[cGray!22] (19, \yp) rectangle (22, \yp+0.9); + } + \draw[draw=black!50, dashed, line width=0.4pt] (18.85, 0.3) rectangle (22.15, 23.7); + \node[font=\tiny, anchor=north, text=black!65] at (20.5, 0.3) {PAD}; + \end{scope} +\end{tikzpicture}% +} + +% ============================================================================= +% Panel (b): Mixed-state path (W=70mm, H=42mm) +% +% Conceptual layout (top → bottom): +% row 1 — 3 column headers naming the 3 t snapshots: +% x_0 (noise) | x_t (model input) | x_1 (data) +% row 2 — continuous lane (blue): 3 particles linearly interpolating +% from random Gaussian starts at t=0 to data values at t=1. +% row 3 — discrete lane (orange): 3 snapshots of a 6-bit vector +% (random at t=0, half kept at t=0.5, all kept at t=1). +% row 4 — shared t-axis with ticks at the 3 column positions. +% +% Faint vertical dashed guides at x=8 / x=35 / x=62 tie the column headers, +% both lanes, and the t-axis together so the eye can read top-to-bottom. +% ============================================================================= +\newcommand{\panelBcontent}{% +\begin{tikzpicture}[x=1mm, y=1mm] + \useasboundingbox (0,0) rectangle (70, 42); + \node[paneltagN] at (1, 41) {(b) Mixed-state path \contrib}; + + % --- column headers at the 3 t positions -------------------------------- + \node[font=\tiny\bfseries, anchor=south, text=black!70] at (8, 36) + {$x_0$: noise}; + \node[font=\tiny\bfseries, anchor=south, text=cOrange!85!black] at (35, 36) + {$x_t$ : \textit{model input}}; + \node[font=\tiny\bfseries, anchor=south, text=black!70] at (62, 36) + {$x_1$: data}; + + % --- vertical guides tying the 3 columns to the t-axis ------------------ + \foreach \xc in {8, 35, 62} { + \draw[black!22, dashed, line width=0.3pt] (\xc, 5) -- (\xc, 35.5); + } + + % ---- top: continuous track (linear interpolation x_0 → x_1) ------------ + \fill[cBlue!4] (1, 22) rectangle (69, 33); + \draw[cBlue!30, line width=0.3pt] (1, 22) rectangle (69, 33); + % Three independent particles, one per continuous channel. + % Endpoint visual hierarchy: left small + faded (noise sample) → + % mid medium → + % right large + solid (data target). + % Path 1 + \draw[cBlue, line width=1pt] (8, 30) -- (35, 28.25) -- (62, 26.5); + \fill[cBlue, opacity=0.65] (8, 30) circle [radius=0.8]; + \fill[cBlue, opacity=0.9] (35, 28.25) circle [radius=0.9]; + \fill[cBlue] (62, 26.5) circle [radius=1.2]; + % Path 2 + \draw[cPurple!75!cBlue, line width=1pt] (8, 25) -- (35, 27.5) -- (62, 30); + \fill[cPurple!75!cBlue, opacity=0.65] (8, 25) circle [radius=0.8]; + \fill[cPurple!75!cBlue, opacity=0.9] (35, 27.5) circle [radius=0.9]; + \fill[cPurple!75!cBlue] (62, 30) circle [radius=1.2]; + % Path 3 + \draw[cBlue!55!black, line width=1pt] (8, 28.5) -- (35, 26.7) -- (62, 24.9); + \fill[cBlue!55!black, opacity=0.65] (8, 28.5) circle [radius=0.8]; + \fill[cBlue!55!black, opacity=0.9] (35, 26.7) circle [radius=0.9]; + \fill[cBlue!55!black] (62, 24.9) circle [radius=1.2]; + + \node[font=\tiny, text=cBlue!85!black, anchor=west] at (1, 20.5) + {$x_t = (1{-}t)\,x_0 + t\,x_1, \quad x_0 \sim \mathcal{N}(0,I)$}; + + % ---- bottom: discrete track (uniform-corruption Bernoulli flip) ------- + \fill[cOrange!4] (1, 10) rectangle (69, 19); + \draw[cOrange!35, line width=0.3pt] (1, 10) rectangle (69, 19); + % Three time snapshots of a 6-bit vector, centered on the t-tick columns. + % t=0 : 0 1 0 1 1 0 (random) + % t=0.5 : 1 1 1 0 0 1 (about half kept) + % t=1 : 1 0 1 0 1 1 (= data target) + \foreach \pattern/\xc in {{0,1,0,1,1,0}/8, + {1,1,1,0,0,1}/35, + {1,0,1,0,1,1}/62} { + \pgfmathsetmacro{\xs}{\xc - 6.5} + \foreach \v [count=\k from 0] in \pattern { + \pgfmathsetmacro{\fx}{\xs + \k*2.2} + \ifnum\v=1 + \fill[cOrange!75] (\fx, 11.2) rectangle (\fx+1.8, 17.8); + \node[font=\tiny, text=white] at (\fx+0.9, 14.5) {1}; + \else + \draw[cOrange!75, line width=0.4pt, fill=white] + (\fx, 11.2) rectangle (\fx+1.8, 17.8); + \node[font=\tiny, text=cOrange!90!black] at (\fx+0.9, 14.5) {0}; + \fi + } + } + \node[font=\tiny, text=cOrange!85!black, anchor=west] at (1, 8) + {$x^{\mathrm{disc}}_t = x^{\mathrm{disc}}_1$ w.p.\ $t$, else $\mathrm{Unif}\{0,1\}$}; + + % ---- shared t-axis ---------------------------------------------------- + \draw[->, line width=0.5pt, black!60] (1, 4) -- (69, 4) + node[anchor=west, font=\tiny, text=black!65, xshift=-0.5mm] {$t$}; + \foreach \tval/\xc in {0/8, 0.5/35, 1/62} { + \draw[black!55, line width=0.4pt] (\xc, 3.6) -- (\xc, 4.4); + \node[font=\tiny, anchor=north, text=black!70] at (\xc, 3.4) {$t{=}\tval$}; + } +\end{tikzpicture}% +} + +% ============================================================================= +% Panel (c): Causal Velocity Field + heads + joint loss (W=152mm, H=82mm) +% +% Visual layout (route C + A combined per user spec): +% +% ────────────── t conditioning subsystem (y=66..78) ────────────── +% t → sin embed (~) → MLP → cond cells [▓▒░] → cond_proj (Linear) +% │ +% ┌──────────┼──────────┐ +% ▼γ1 ▼β1 ▼α1 +% ─────────── BACKBONE (y=2..62) ───────────────────────────────── +% +% LEFT col (tensor flow A): CENTER col (heatmap C): +% +% [tensor h_in] ▓▒░▓░▒ ★ MHSA + causal-packet +% │ ┌─────────────────────┐ +% LN box │ █ █ █ █ █ █ █ █ █ █ │ ← FLOW row +% │ │ █ █ │ +% [tensor h_LN] row-uniform │ █ █ █ │ +% │ │ █ █ █ █ │ large 12×12 +% ⊗γ_1 ◀──────── γ_1 (purple dashed) │ █ █ █ █ █ │ attention map +% │ │ █ █ █ █ █ █ │ (FLOW row+col +% [tensor h_γ] cols rescaled │ █ ... │ + lower-tri) +% │ └──────────┬──────────┘ +% ⊕β_1 ◀──────── β_1 (green dashed) │ +% │ ⊗α_1 ◀── α_1 (orange dashed) +% [tensor h_β] cells shifted │ +% │ ⊕ ◀── residual from h_in +% └──────────────────────────────►──────────────┤ +% ▼ +% "MLP half (same)" → h' +% ▼ +% [v_θ head] +% [logits head ★] +% [Joint loss eq] +% ============================================================================= +\newcommand{\panelCcontent}{% +\begin{tikzpicture}[x=1mm, y=1mm, + opcirc/.style={circle, draw=black!70, fill=white, + line width=0.5pt, inner sep=0pt, + minimum size=2.6mm, + font=\tiny\bfseries, text=black!75}, + rescirc/.style={circle, draw=black!70, fill=white, + line width=0.7pt, inner sep=0pt, + minimum size=3mm, + font=\tiny\bfseries}, + pathline/.style={->, line width=0.5pt, black!65, + >={Stealth[length=1.4mm]}}, + resarc/.style={->, line width=0.7pt, black!50, + >={Stealth[length=1.6mm]}}, + gammaline/.style={->, dashed, line width=0.6pt, cPurple!85, + >={Stealth[length=1.4mm]}}, + betaline/.style={->, dashed, line width=0.6pt, cGreen!75!black, + >={Stealth[length=1.4mm]}}, + alphaline/.style={->, dashed, line width=0.6pt, cOrange!90, + >={Stealth[length=1.4mm]}}] + \useasboundingbox (0,0) rectangle (152, 82); + \node[paneltagN] at (1, 81) + {(c) Causal-Packet Velocity Field \contrib\ + joint loss}; + + % ========================================================================= + % T CONDITIONING SUBSYSTEM (y=66..78) — sin embed + MLP + cond + cond_proj + % ========================================================================= + + % (1) t scalar + \draw[fill=cBlue!15, draw=cBlue!80, line width=0.5pt] + (8, 72) circle [radius=2.0]; + \node[font=\tiny\bfseries, text=cBlue!90!black] at (8, 72) {$t$}; + \node[font=\tiny, text=black!55, anchor=north] at (8, 69.5) {scalar}; + + \draw[pathline] (10.2, 72) -- (12, 72); + + % (2) sin embed + \draw[fill=white, draw=cBlue!80, line width=0.5pt, rounded corners=0.6pt] + (12, 68.5) rectangle (28, 75.5); + \draw[cBlue!80, line width=0.55pt] + (13, 72) sin (14.5, 73.5) cos (16, 72) sin (17.5, 70.5) cos (19, 72) + sin (20.5, 73.5) cos (22, 72) sin (23.5, 70.5) cos (25, 72) + sin (26.5, 73.5) cos (27, 72.7); + \node[font=\tiny, text=cBlue!90!black, anchor=north] at (20, 68.5) + {sin embed $\to\mathbb{R}^{64}$}; + + \draw[pathline] (28.2, 72) -- (30, 72); + + % (3) cond MLP trapezoid + \fill[white] (30, 69) -- (30, 75) -- (37, 75.5) -- (37, 68.5) -- cycle; + \draw[black!65, line width=0.5pt] + (30, 69) -- (30, 75) -- (37, 75.5) -- (37, 68.5) -- cycle; + \node[font=\tiny] at (33.5, 72) {MLP}; + + \draw[pathline] (37.2, 72) -- (39, 72); + + % (4) cond vector cells + \begin{scope}[shift={(39, 69)}] + \foreach \k/\col in {0/cBlue, 1/cBlue!75!cPurple, 2/cPurple!75!cBlue, 3/cBlue!60!black, + 4/cBlue, 5/cPurple!85, 6/cBlue!75, 7/cBlue!85!black, + 8/cBlue!60!cPurple, 9/cBlue!70} { + \pgfmathsetmacro{\fx}{\k * 1.4} + \fill[\col] (\fx, 0) rectangle (\fx+1.2, 6); + \draw[black!30, line width=0.15pt] (\fx, 0) rectangle (\fx+1.2, 6); + } + \end{scope} + \node[font=\tiny, text=black!75, anchor=west] at (54, 72) + {$\mathrm{cond}\!\in\!\mathbb{R}^{d}$}; + + \draw[pathline] (66, 72) -- (68, 72); + + % (5) cond_proj — Linear that produces (γ, β, α) modulation parameters + \node[rectangle, draw=black!65, fill=white, line width=0.5pt, + rounded corners=0.6pt, minimum width=22mm, minimum height=8mm, + inner sep=1pt, font=\tiny, align=center, anchor=west] + (cproj) at (68, 72) + {cond\_proj \\ $\mathbb{R}^d\!\to\!6d$}; + + % cond_proj output split: 3 colored mini-bars labelled γ_1, β_1, α_1 + % (γ_2, β_2, α_2 are implied — same fanout for the MLP half) + \begin{scope}[shift={($(cproj.east)+(2mm, 4mm)$)}] + \fill[cPurple!75] (0, 0) rectangle (10, 1.2); + \draw[black!30, line width=0.15pt] (0, 0) rectangle (10, 1.2); + \node[font=\tiny\bfseries, text=cPurple!75, anchor=west] at (10.5, 0.6) {$\gamma_1$}; + \end{scope} + \begin{scope}[shift={($(cproj.east)+(2mm, 1mm)$)}] + \fill[cGreen!75!black] (0, 0) rectangle (10, 1.2); + \draw[black!30, line width=0.15pt] (0, 0) rectangle (10, 1.2); + \node[font=\tiny\bfseries, text=cGreen!75!black, anchor=west] at (10.5, 0.6) {$\beta_1$}; + \end{scope} + \begin{scope}[shift={($(cproj.east)+(2mm, -2mm)$)}] + \fill[cOrange!90] (0, 0) rectangle (10, 1.2); + \draw[black!30, line width=0.15pt] (0, 0) rectangle (10, 1.2); + \node[font=\tiny\bfseries, text=cOrange!85!black, anchor=west] at (10.5, 0.6) {$\alpha_1$}; + \end{scope} + \node[font=\tiny\itshape, text=black!55, anchor=west] at (cproj.south) [yshift=-0.5mm, xshift=-3mm] + {(implied: $\gamma_2,\beta_2,\alpha_2$ for MLP half — same fanout)}; + + % ========================================================================= + % LEFT COLUMN: AdaLN modulation tensor flow visualization + % Shows h transformed at each stage: h_in → LN → ⊗γ → ⊕β → h_β + % ========================================================================= + + \def\gxc{32} % left column tensor center x + \def\gw{1.4} % cell width + \def\gh{1.0} % cell height + + % token seq input box (far left) + \node[archbox, minimum width=14mm, anchor=west] (toki) at (1, 50) + {token seq\\$x_t$}; + \draw[pathline] (toki.east) -- ++(3mm, 0); + + % --- (a) h_in tensor (top) --- + \node[font=\tiny\bfseries, text=black!75, anchor=south] at (\gxc, 60) + {$h$ (input)}; + \begin{scope}[shift={(\gxc - 4.2, 55.5)}] + \foreach \op [count=\k from 0] in + {30, 80, 50, 40, 90, 60, + 70, 20, 80, 50, 40, 90, + 60, 50, 90, 30, 80, 40, + 70, 30, 60, 80, 40, 70} { + \pgfmathsetmacro{\i}{int(mod(\k, 6))} + \pgfmathsetmacro{\j}{int(\k/6)} + \fill[cBlue!\op] (\i*\gw, \j*\gh) rectangle (\i*\gw+\gw-0.05, \j*\gh+\gh-0.05); + } + \end{scope} + \draw[pathline] (\gxc, 55.2) -- (\gxc, 54); + + % LayerNorm box + \node[archbox, minimum width=14mm, minimum height=2mm, inner sep=0.5pt, + font=\tiny, anchor=north] + (ln1) at (\gxc, 53.8) {LayerNorm}; + \draw[pathline] (\gxc, 51.7) -- (\gxc, 50.7); + + % --- (b) h after LN (more uniform per col) --- + \node[font=\tiny, text=cBlue!85!black, anchor=south] at (\gxc, 50.5) + {$\mathrm{LN}(h)$}; + \begin{scope}[shift={(\gxc - 4.2, 45.5)}] + \foreach \op [count=\k from 0] in + {50, 30, 70, 50, 40, 60, + 40, 60, 50, 50, 40, 60, + 50, 50, 60, 40, 60, 40, + 50, 40, 50, 60, 40, 60} { + \pgfmathsetmacro{\i}{int(mod(\k, 6))} + \pgfmathsetmacro{\j}{int(\k/6)} + \fill[cBlue!\op] (\i*\gw, \j*\gh) rectangle (\i*\gw+\gw-0.05, \j*\gh+\gh-0.05); + } + \end{scope} + + % ⊗γ_1 operator + \draw[pathline] (\gxc, 45.2) -- (\gxc, 44.2); + \node[opcirc, draw=cPurple!75, text=cPurple!75] (gamma1) at (\gxc, 43.1) {\(\times\)}; + \node[font=\tiny, text=cPurple!75, anchor=west] at (gamma1.east) [xshift=0.3mm] {$\gamma_1$}; + \draw[pathline] (\gxc, 41.8) -- (\gxc, 40.8); + + % --- (c) h after ⊗γ (per-column scaling visible) --- + \node[font=\tiny, text=cPurple!75, anchor=south] at (\gxc, 40.5) + {$\gamma_1\!\odot\!\mathrm{LN}(h)$}; + \begin{scope}[shift={(\gxc - 4.2, 35.5)}] + \foreach \op [count=\k from 0] in + {60, 12, 70, 25, 52, 60, + 48, 24, 50, 25, 52, 60, + 60, 20, 60, 20, 78, 40, + 60, 16, 50, 30, 52, 60} { + \pgfmathsetmacro{\i}{int(mod(\k, 6))} + \pgfmathsetmacro{\j}{int(\k/6)} + \fill[cBlue!\op] (\i*\gw, \j*\gh) rectangle (\i*\gw+\gw-0.05, \j*\gh+\gh-0.05); + } + \end{scope} + + % ⊕β_1 operator + \draw[pathline] (\gxc, 35.2) -- (\gxc, 34.2); + \node[opcirc, draw=cGreen!75!black, text=cGreen!75!black] (beta1) at (\gxc, 33.1) {\(+\)}; + \node[font=\tiny, text=cGreen!75!black, anchor=west] at (beta1.east) [xshift=0.3mm] {$\beta_1$}; + \draw[pathline] (\gxc, 31.8) -- (\gxc, 30.8); + + % --- (d) h after ⊕β (uniform shift visible) --- + \node[font=\tiny, text=cGreen!75!black, anchor=south] at (\gxc, 30.5) + {$+\,\beta_1$}; + \begin{scope}[shift={(\gxc - 4.2, 25.5)}] + \foreach \op [count=\k from 0] in + {80, 32, 90, 45, 72, 80, + 68, 44, 70, 45, 72, 80, + 80, 40, 80, 40, 90, 60, + 80, 36, 70, 50, 72, 80} { + \pgfmathsetmacro{\i}{int(mod(\k, 6))} + \pgfmathsetmacro{\j}{int(\k/6)} + \fill[cBlue!\op] (\i*\gw, \j*\gh) rectangle (\i*\gw+\gw-0.05, \j*\gh+\gh-0.05); + } + \end{scope} + + % arrow → MHSA (going right toward heatmap) + \draw[pathline] (\gxc + 4.5, 27.5) -- (62, 27.5); + + % ========================================================================= + % CENTER COLUMN: large causal-packet attention heatmap + % ========================================================================= + + \def\hxc{84} % heatmap center x + \def\hcell{2.5} % heatmap cell size + % heatmap occupies x = (\hxc - 15) .. (\hxc + 15) = 69..99, with 12 cells + + % heatmap title + \node[font=\tiny\bfseries, text=cOrange!85!black, anchor=south] + at (\hxc, 60) {\contrib\ MHSA + causal-packet attention}; + + % heatmap top labels (FLOW + P_1 .. P_11) — only show subset for readability + \foreach \i/\lbl in {0/F, 1/{$P_1$}, 5/{$P_5$}, 11/{$P_{11}$}} { + \node[font=\tiny, text=black!60] + at ({\hxc - 15 + \i*\hcell + \hcell/2}, 58.7) {\lbl}; + } + % left labels + \foreach \i/\lbl in {0/F, 1/{$P_1$}, 5/{$P_5$}, 11/{$P_{11}$}} { + \node[font=\tiny, text=black!60, anchor=east] + at ({\hxc - 15 - 0.4}, {57 - \i*\hcell - \hcell/2}) {\lbl}; + } + + % heatmap cells + \foreach \i in {0,...,11} { + \foreach \j in {0,...,11} { + \pgfmathsetmacro{\xx}{\hxc - 15 + \j * \hcell} + \pgfmathsetmacro{\yy}{57 - (\i + 1) * \hcell} + \ifnum\i=0 + \fill[cOrange!85] (\xx, \yy) rectangle (\xx+\hcell-0.1, \yy+\hcell-0.1); + \else + \ifnum\j=0 + \fill[cOrange!85] (\xx, \yy) rectangle (\xx+\hcell-0.1, \yy+\hcell-0.1); + \else + \ifnum\j>\i + \draw[fill=white, draw=black!25, line width=0.15pt] + (\xx, \yy) rectangle (\xx+\hcell-0.1, \yy+\hcell-0.1); + \else + \pgfmathsetmacro{\dist}{\i - \j} + \pgfmathsetmacro{\opa}{int(75 - \dist*4)} + \fill[cOrange!\opa] (\xx, \yy) rectangle (\xx+\hcell-0.1, \yy+\hcell-0.1); + \fi + \fi + \fi + } + } + + % heatmap legend below + \node[font=\tiny\itshape, text=black!60, anchor=north, align=center] + at (\hxc, 26) + {row 0 / col 0 = FLOW token attends all\\ + lower-tri = packet $i$ attends pkt $\le i$}; + + % α_1 gate + ⊕ residual to the RIGHT of heatmap, vertical column + \def\axc{106} + \node[opcirc, draw=cOrange!90, text=cOrange!90] (alpha1) at (\axc, 27) {\(\times\)}; + \node[font=\tiny, text=cOrange!90, anchor=west] at (alpha1.east) [xshift=0.3mm] {$\alpha_1$}; + + % attention output → α gate + \draw[pathline] (99, 27) -- (alpha1.west); + + \draw[pathline] (\axc, 25.7) -- (\axc, 24.7); + \node[rescirc] (res1) at (\axc, 23.5) {\(+\)}; + + % residual arc: from far-left (h_in) all the way over to res1 — long arc + \draw[resarc] (\gxc, 60) to[out=90, in=180, looseness=0.7] + ($(res1.north) + (-3mm, 8mm)$) -- (res1.north); + \node[font=\tiny\itshape, text=black!55, anchor=south] + at ($(res1.north)+(0, 8mm)$) {residual from $h$}; + + % continuation → "MLP half" → output + \draw[pathline] (\axc, 22) -- (\axc, 19); + \node[archbox, dashed, minimum width=20mm, minimum height=4mm, inner sep=1pt, + font=\tiny\itshape, anchor=north] + (mlphalf) at (\axc, 18.5) {MLP half\\(same pattern)}; + \draw[pathline] (\axc, 12.7) -- (\axc, 11.5); + \node[font=\tiny, text=black!75] at (\axc, 10.5) {$h'$}; + + % "× 4 layers" annotation at the bottom of the center area + \node[font=\tiny\itshape, text=cOrange!75!black, anchor=center] + at (\hxc, 7) {(this whole block stacks $\times\,4$ layers)}; + + % ========================================================================= + % CONDITIONING LINES — γ_1 / β_1 / α_1 from cond_proj output bars to ops + % ========================================================================= + + % γ_1 (purple) → ⊗γ_1 in left column + \draw[gammaline] + ($(cproj.east)+(12mm, 4.6mm)$) .. controls (90, 60) and (60, 55) .. (gamma1.north); + + % β_1 (green) → ⊕β_1 in left column + \draw[betaline] + ($(cproj.east)+(12mm, 1.6mm)$) .. controls (95, 55) and (55, 45) .. (beta1.north); + + % α_1 (orange) → ⊗α_1 next to heatmap + \draw[alphaline] + ($(cproj.east)+(12mm, -1.4mm)$) .. controls (105, 65) and (108, 35) .. (alpha1.north); + + % ========================================================================= + % RIGHT: heads + joint loss + % ========================================================================= + \node[archbox, minimum width=22mm, anchor=west] + (vh) at (124, 28) {$v_\theta$ head\\(continuous)}; + \node[novelbox, minimum width=22mm, anchor=west] + (dh) at (124, 17) {\contrib\ logits head\\(discrete)}; + + % from h' to heads (right-then-up branching) + \coordinate (hsplit) at (118, 10.5); + \draw[arrow] (\axc, 10.5) -- (hsplit); + \draw[arrow] (hsplit) |- (vh.west); + \draw[arrow] (hsplit) |- (dh.west); + + \node[losseq, anchor=north west, minimum width=46mm] + at (124, 50) + {\textbf{Joint loss}\\[1pt] + $\mathcal{L} = + \underbrace{\lVert v_\theta - (x_1{-}x_0)\rVert^2}_{\text{\textcolor{cBlue!85!black}{cont CFM}}}$\\[2pt] + $\quad\;\;\,+\;\lambda + \underbrace{\mathrm{CE}(\mathrm{logits},\,x_1^{\text{disc}})}_{\text{\textcolor{cOrange!85!black}{discrete FM}}}$}; +\end{tikzpicture}% +} + +% ============================================================================= +% Panel (d): Inference + Mahalanobis-OAS router (W=152mm, H=44mm) +% - "Why a router?" callout removed. +% - Score-space scatter is now wider and centered under svec/router/auroc. +% ============================================================================= +\newcommand{\panelDcontent}{% +\begin{tikzpicture}[x=1mm, y=1mm] + \useasboundingbox (0,0) rectangle (152, 44); + \node[paneltagN] at (1, 43) + {(d) Inference \& Mahalanobis-OAS router \contrib}; + + % --- linear chain (upper portion) ---------------------------------------- + \node[archbox, minimum width=16mm, fill=cGray!12, anchor=west] + (testflow) at (1, 32) {test\\flow}; + \node[archbox, dashed, minimum width=22mm, anchor=west] + (frozen) at ($(testflow.east)+(3mm,0)$) + {\textsc{Frozen} backbone\\($v_\theta$, logits)}; + \node[archbox, minimum width=30mm, anchor=west] + (svec) at ($(frozen.east)+(3mm,0)$) + {\textsc{Score} $s\in\mathbb{R}^{10}$\\ + \scriptsize\texttt{terminal\_norm,}\\ + \scriptsize\texttt{terminal\_packet,}\\ + \scriptsize\texttt{disc\_nll\_total,}~$\dots$}; + \node[novelbox, minimum width=42mm, minimum height=18mm, anchor=west] + (router) at ($(svec.east)+(3mm,0)$) + {\contrib\ \textbf{Mahalanobis-OAS}\\[1pt] + $D^2(s) = (s-\mu)^\top \Sigma_{\mathrm{OAS}}^{-1}(s-\mu)$\\[1pt] + \scriptsize\itshape $\mu, \Sigma$ fit on benign val\\[-0.2pt] + \scriptsize\itshape (no labels, no selection bias)}; + \node[archbox, minimum width=14mm, fill=cBlue!10, anchor=west] + (auroc) at ($(router.east)+(3mm,0)$) {AUROC\\$s_{\text{anomaly}}$}; + \draw[arrow] (testflow.east) -- (frozen.west); + \draw[arrow] (frozen.east) -- (svec.west); + \draw[arrow] (svec.east) -- (router.west); + \draw[arrow] (router.east) -- (auroc.west); + + % --- score-space scatter (centered, sized to fill the lower band) ------- + % Center under svec..auroc span. svec.left=45, auroc.right≈137. + % Center x ≈ (45+137)/2 = 91. Width = 90mm → x range 46..136. + \node[draw=black!40, line width=0.3pt, fill=white, + minimum width=90mm, minimum height=22mm, anchor=south, + inner sep=0pt] + (scatterbox) at (91, 1) {}; + \node[font=\tiny\bfseries, text=black!75, anchor=north west] + at (scatterbox.north west) [xshift=1.5mm, yshift=-0.8mm] + {score space (2 of 10 dims, illustrative)}; + % axes inside scatterbox + \begin{scope}[shift={($(scatterbox.south west)+(7mm,3mm)$)}] + \draw[->, line width=0.3pt, black!55] (0, 0) -- (76, 0); + \draw[->, line width=0.3pt, black!55] (0, 0) -- (0, 14); + \node[font=\tiny, text=black!60, anchor=west] at (60, -1.4) + {\texttt{terminal\_norm}}; + \node[font=\tiny, text=black!60, rotate=90, anchor=south west] at (-1.6, 1) + {\texttt{disc\_nll}}; + % benign cluster ellipse (Sigma_OAS) — wider since plot is wider + \draw[cBlue!75, line width=0.6pt, rotate around={20:(33,6)}] + (33,6) ellipse [x radius=14, y radius=4]; + \draw[cBlue!50, line width=0.4pt, dashed, rotate around={20:(33,6)}] + (33,6) ellipse [x radius=21, y radius=6]; + % benign points + \foreach \px/\py in {28/5.4, 32/6.6, 36/6.8, 30/4.8, 34/5.8, + 38/7, 26/6, 34/5.4, 37/6.4, 24/5.4, + 40/7.2, 22/4.8, 30/6.4, 35/7, 28/4.4} { + \fill[cBlue!75] (\px, \py) circle [radius=0.5]; + } + % attack points (outside the ellipse) + \foreach \px/\py in {60/10, 56/4, 52/11, 64/8, + 10/9, 6/6, 68/5, 8/3, + 70/12, 14/2, 4/10, 66/2.5} { + \fill[cOrange] (\px, \py) circle [radius=0.7]; + } + % legend + \fill[cBlue!75] (50, 12.5) circle [radius=0.5]; + \node[font=\tiny, text=cBlue!85!black, anchor=west] at (51.3, 12.5) {benign}; + \fill[cOrange] (62, 12.5) circle [radius=0.7]; + \node[font=\tiny, text=cOrange!85!black, anchor=west] at (63.3, 12.5) {attack}; + \end{scope} +\end{tikzpicture}% +} + +% ============================================================================= +% OUTER FIGURE — chains the four panels with positioning, then draws three +% inter-panel data-flow arrows that make the training/inference pipeline +% explicit: +% +% (a) ──x_1──→ (b) +% │ x_t +% ▼ +% (c) [training] +% │ frozen +% ▼ +% (d) [inference] +% ============================================================================= +\begin{document} +\begin{tikzpicture}[node distance=8mm] + + % row 1 + \node[panel] (panA) {\panelAcontent}; + \node[novelpanel, right=of panA] (panB) {\panelBcontent}; + + % row 2 — full width + \node[novelpanel, below=of panA.south west, anchor=north west] (panC) + {\panelCcontent}; + + % row 3 — full width + \node[panel, below=of panC.south west, anchor=north west] (panD) + {\panelDcontent}; + + % --- inter-panel data-flow arrows --------------------------------------- + % (a) → (b): clean tokens x_1 produced by tokenization feed the corruption + \draw[flowarrow] (panA.east) -- (panB.west) + node[midway, above=0.5mm, font=\scriptsize\itshape, text=black!75] + {$x_1$}; + % (b) → (c): mixed-state corrupted x_t at random t ∈ [0,1] feeds the model + \draw[flowarrow] (panB.south) -- (panC.north -| panB.south) + node[midway, right=0.5mm, font=\scriptsize\itshape, text=black!75] + {$x_t$}; + % (c) → (d): trained backbone weights are frozen at test time + \draw[flowarrow] (panC.south) -- (panD.north) + node[midway, right=0.5mm, font=\scriptsize\itshape, text=black!75] + {\textit{frozen}}; + +\end{tikzpicture} +\end{document} diff --git a/paper/figures/figure1_pipeline.tex b/paper/figures/figure1_pipeline.tex new file mode 100644 index 0000000..a2a98c0 --- /dev/null +++ b/paper/figures/figure1_pipeline.tex @@ -0,0 +1,338 @@ +% Figure 1: A+C combo pipeline (full). +% Compile: pdflatex figure1_pipeline.tex +\documentclass[border=8pt]{standalone} +\usepackage{tikz} +\usetikzlibrary{arrows.meta, positioning, calc, decorations.pathreplacing} +\usepackage{amsmath, amssymb} + +\definecolor{myorange}{RGB}{230, 126, 34} +\definecolor{myblue}{RGB}{52, 110, 180} +\definecolor{mygreen}{RGB}{46, 139, 87} +\definecolor{mypurple}{RGB}{142, 68, 173} +\definecolor{mygray}{RGB}{170, 170, 170} +\definecolor{mybg}{RGB}{248, 248, 248} + +\newcommand{\contrib}{\textcolor{myorange}{\ensuremath{\bigstar}}} + +\begin{document} +\begin{tikzpicture}[ + font=\small, + >={Stealth[length=2.5mm]}, + databox/.style={rectangle, draw=mygray, rounded corners=2pt, + minimum height=12mm, minimum width=14mm, align=center, fill=mygray!20}, + box/.style={rectangle, draw=black!70, thick, rounded corners=2pt, + minimum height=12mm, minimum width=22mm, align=center, fill=white}, + novelbox/.style={rectangle, draw=myorange, line width=1.4pt, rounded corners=2pt, + minimum height=12mm, minimum width=22mm, align=center, fill=myorange!10}, + arrow/.style={->, thick}, + losseq/.style={fill=mybg, draw=black!30, rounded corners=2pt, inner sep=5pt} +] + +% ========================================================================= +% TRAINING ROW (no row title) +% ========================================================================= + +% ----- (1) PCAP file icon ----- +\begin{scope}[shift={(0.0, -1.2)}] + \fill[myblue!12] (0,0) -- (0,2.4) -- (1.05,2.4) -- (1.45,2.0) -- (1.45,0) -- cycle; + \draw[myblue!80, line width=0.6pt] + (0,0) -- (0,2.4) -- (1.05,2.4) -- (1.45,2.0) -- (1.45,0) -- cycle; + \fill[myblue!30] (1.05,2.4) -- (1.05,2.0) -- (1.45,2.0) -- cycle; + \draw[myblue!80, line width=0.4pt] (1.05,2.4) -- (1.05,2.0) -- (1.45,2.0); + \node[font=\bfseries\scriptsize, text=myblue!90!black] at (0.7, 1.65) {flow.pcap}; + \foreach \y/\rl in {1.30/0.95, 1.10/0.78, 0.90/1.05, 0.70/0.65, 0.50/0.90, 0.30/0.55} { + \draw[myblue!45, line width=0.3pt] (0.15, \y) -- (0.15+\rl, \y); + } +\end{scope} + +% Arrow: parse (length = 1.4cm so label fits comfortably) +\draw[arrow, black!60] (1.6, 0) -- (3.0, 0) + node[midway, above=0.5mm, font=\scriptsize\itshape, text=black!70] {parse}; + +% ----- (2) Packet stream: pkt0, pkt1, pkt2, ..., pkt_T ----- +\begin{scope}[shift={(3.1, -1.1)}] + % 4 packets at explicit x-offsets; first three are 0,1,2 then a gap with + % ellipsis and the final one labeled pkt_T. + \foreach \j/\xoff/\lbl in {0/0.00/0, 1/1.18/1, 2/2.36/2, 3/4.04/T} { + \pgfmathsetmacro{\xx}{\xoff} + \draw[fill=white, draw=black!55, line width=0.45pt, rounded corners=1pt] + (\xx, 0.05) rectangle (\xx+1.05, 2.25); + \draw[fill=myblue!35, draw=myblue, line width=0.3pt] + (\xx+0.07, 2.00) rectangle (\xx+0.32, 2.15); + \node[font=\tiny, anchor=west] at (\xx+0.34, 2.075) {size}; + \draw[fill=mygreen!35, draw=mygreen, line width=0.3pt] + (\xx+0.07, 1.78) rectangle (\xx+0.32, 1.93); + \node[font=\tiny, anchor=west] at (\xx+0.34, 1.855) {IAT}; + \draw[fill=mypurple!35, draw=mypurple, line width=0.3pt] + (\xx+0.07, 1.56) rectangle (\xx+0.20, 1.71); + \node[font=\tiny, anchor=west] at (\xx+0.22, 1.635) {dir}; + \ifcase\j + \def\flagS{1}\def\flagF{0}\def\flagR{0}\def\flagP{0}\def\flagA{0}\or + \def\flagS{1}\def\flagF{0}\def\flagR{0}\def\flagP{0}\def\flagA{1}\or + \def\flagS{0}\def\flagF{0}\def\flagR{0}\def\flagP{0}\def\flagA{1}\or + \def\flagS{0}\def\flagF{0}\def\flagR{0}\def\flagP{1}\def\flagA{1}\fi + \foreach \fname/\fval/\k in {S/\flagS/0, F/\flagF/1, R/\flagR/2, P/\flagP/3, A/\flagA/4} { + \pgfmathsetmacro{\fx}{\xx + 0.07 + \k*0.18} + \ifnum\fval=1 + \draw[fill=myorange!70, draw=myorange, line width=0.3pt] + (\fx, 1.30) rectangle (\fx+0.15, 1.46); + \node[font=\tiny, white] at (\fx+0.075, 1.38) {\fname}; + \else + \draw[fill=white, draw=myorange!60, line width=0.3pt] + (\fx, 1.30) rectangle (\fx+0.15, 1.46); + \node[font=\tiny, text=myorange!80!black] at (\fx+0.075, 1.38) {\fname}; + \fi + } + \draw[fill=myblue!20, draw=myblue!60, line width=0.3pt] + (\xx+0.07, 1.05) rectangle (\xx+0.32, 1.20); + \node[font=\tiny, anchor=west] at (\xx+0.34, 1.125) {win}; + \draw[black!30, line width=0.3pt] (\xx+0.07, 0.80) -- (\xx+0.97, 0.80); + \draw[black!30, line width=0.3pt] (\xx+0.07, 0.65) -- (\xx+0.85, 0.65); + \draw[black!30, line width=0.3pt] (\xx+0.07, 0.50) -- (\xx+0.95, 0.50); + \draw[black!30, line width=0.3pt] (\xx+0.07, 0.35) -- (\xx+0.70, 0.35); + \node[font=\scriptsize, text=black!70] at (\xx+0.525, -0.13) {pkt$_{\lbl}$}; + } + % Ellipsis between pkt_2 (ends at x=3.41) and pkt_T (starts at x=4.04) + \node[font=\Large, text=black!55] at (3.72, 1.15) {$\cdots$}; + % Time axis spans full row + \draw[->, line width=0.35pt, black!55] (-0.05, -0.4) -- (5.20, -0.4); + \node[font=\tiny, anchor=west, text=black!55] at (5.20, -0.4) {time}; +\end{scope} + +% Arrow: tokenize (length = 1.4cm). Packet stream ends at x=3.1+5.09=8.19, +% so arrow must start after that. +\draw[arrow, black!60] (8.3, 0) -- (9.7, 0) + node[midway, above=0.5mm, font=\scriptsize\itshape, text=black!70] {tokenize}; + +% ----- (3) Token sequence: [FLOW | P_1 | P_2 | ... | P_n | PAD] ----- +% All tokens are 21-d (1 type marker + 20 feature/pad cells). Two paddings: +% • channel-padding inside packet tokens (cells 10..20, since pkt has 9 feats) +% • sequence-padding tokens (entire token zeroed when actual flow length < T) +\begin{scope}[shift={(9.8, -1.45)}] + % --- helper macros for cell drawing inside this scope --- + % Each token: 1 type cell on top + 20 feature/pad cells below. + % Cell height 0.085, cell width 0.42. + % Token x-offsets: FLOW=0, P_1=0.62, P_2=1.22, P_n=2.30, PAD=2.92. + % + % FLOW token: type (dark blue) + 20 cont features (light blue), no padding + \draw[fill=myblue!75, draw=myblue, line width=0.25pt] + (0.0, 2.55) rectangle (0.42, 2.64); + \foreach \i in {0,...,19} { + \pgfmathsetmacro{\yp}{2.46 - \i*0.085} + \draw[fill=myblue!25, draw=myblue!80, line width=0.25pt] + (0.0, \yp) rectangle (0.42, \yp+0.075); + } + \node[font=\tiny, anchor=north, text=myblue!90!black] at (0.21, 0.72) {\textbf{FLOW}}; + % + % P_1, P_2, P_n: type (orange) + 3 cont + 6 disc + 11 channel-pad + \foreach \subi/\xoff in {1/0.62, 2/1.22, n/2.30} { + % type marker (orange) + \draw[fill=myorange!75, draw=myorange, line width=0.25pt] + (\xoff, 2.55) rectangle (\xoff+0.42, 2.64); + % 3 cont (blue) + \foreach \i in {0,...,2} { + \pgfmathsetmacro{\yp}{2.46 - \i*0.085} + \draw[fill=myblue!25, draw=myblue!80, line width=0.25pt] + (\xoff, \yp) rectangle (\xoff+0.42, \yp+0.075); + } + % 6 disc (orange) + \foreach \i in {3,...,8} { + \pgfmathsetmacro{\yp}{2.46 - \i*0.085} + \draw[fill=myorange!30, draw=myorange!80, line width=0.25pt] + (\xoff, \yp) rectangle (\xoff+0.42, \yp+0.075); + } + % 11 channel-pad (gray hatched) + \foreach \i in {9,...,19} { + \pgfmathsetmacro{\yp}{2.46 - \i*0.085} + \draw[fill=mygray!30, draw=mygray, line width=0.2pt, dash pattern=on 0.4pt off 0.4pt] + (\xoff, \yp) rectangle (\xoff+0.42, \yp+0.075); + } + \node[font=\tiny, anchor=north] at (\xoff+0.21, 0.72) {$P_{\subi}$}; + } + % + % Ellipsis centered in gap between P_2 (ends x=1.64) and P_n (starts x=2.30), + % vertically centered on token cell stack (top=2.64, bottom=0.86) + \node[font=\Large, text=black!55] at (1.97, 1.75) {$\cdots$}; + % + % PAD token (sequence-padding): entire token grayed + dashed outer border + \draw[fill=mygray!20, draw=mygray, dash pattern=on 1pt off 1pt, line width=0.4pt] + (2.92, 2.55) rectangle (3.34, 2.64); + \foreach \i in {0,...,19} { + \pgfmathsetmacro{\yp}{2.46 - \i*0.085} + \draw[fill=mygray!25, draw=mygray, line width=0.2pt, dash pattern=on 0.5pt off 0.5pt] + (2.92, \yp) rectangle (3.34, \yp+0.075); + } + % outer dashed wrap to emphasize "this whole token is padding" + \draw[draw=black!50, dashed, line width=0.5pt] + (2.90, 0.72) rectangle (3.36, 2.66); + \node[font=\tiny, anchor=north, text=black!65] at (3.13, 0.72) {\textbf{PAD}}; + % + % Bottom brace + sequence label (below the FLOW/P_i/PAD name labels) + \draw[decorate, decoration={brace, amplitude=3pt, mirror}, line width=0.4pt, black!60] + (-0.05, 0.30) -- (3.39, 0.30); + \node[font=\scriptsize\itshape, text=black!70] at (1.67, -0.05) + {token sequence (1 flow + $T$ packets, all 21-d)}; +\end{scope} + +% Arrow: token → velocity (sequence ends at x=9.8+3.34=13.14) +\draw[arrow, black!60] (13.3, 0) -- (14.0, 0); + +% ----- (4) Velocity field (with detailed internals, DiT-style) ----- +% Outer box at x=14.0..19.0, y=-2.5..2.5 (5cm × 5cm) +\begin{scope}[shift={(14.0, -2.5)}] + % Outer rounded box + \draw[fill=myorange!8, draw=myorange, line width=1.4pt, rounded corners=2pt] + (0, 0) rectangle (5.0, 5.0); + \node[font=\sffamily\bfseries\small, anchor=north, text=myorange!90!black] + at (2.5, 4.85) {Velocity Field}; + % + % Time embedding sub-box (top-left) + \draw[fill=white, draw=black!55, line width=0.4pt, rounded corners=1pt] + (0.20, 3.60) rectangle (1.30, 4.45); + \node[font=\tiny\bfseries, anchor=north] at (0.75, 4.42) {time $t$}; + \node[font=\tiny, anchor=north] at (0.75, 4.20) {sinusoidal}; + \node[font=\tiny, anchor=north] at (0.75, 4.00) {emb + MLP}; + % + % AdaLN-Zero block 1 + \draw[fill=white, draw=black!60, line width=0.4pt, rounded corners=1pt] + (1.55, 3.95) rectangle (4.80, 4.45); + \node[font=\scriptsize] at (3.18, 4.20) + {AdaLN-Zero \,($\gamma_1, \beta_1, \alpha_1$)}; + % Conditioning arrow from time emb to AdaLN-1 + \draw[->, dashed, line width=0.4pt, black!50] + (1.30, 4.20) -- (1.55, 4.20); + % + % Arrow down + \draw[->, line width=0.4pt, black!60] (3.18, 3.95) -- (3.18, 3.65); + % + % MHA block with causal mask icon + \draw[fill=myorange!15, draw=myorange, line width=0.7pt, rounded corners=1pt] + (1.55, 2.05) rectangle (4.80, 3.65); + \node[font=\scriptsize\bfseries, anchor=north, text=myorange!85!black] + at (3.18, 3.55) {\contrib\ Multi-Head Self-Attn}; + \node[font=\tiny\itshape, anchor=north, text=myorange!85!black] + at (3.18, 3.30) {causal-packet mask}; + % Mini 5×5 lower-triangular mask grid + \begin{scope}[shift={(2.55, 2.30)}] + \foreach \i in {0,...,4} { + \foreach \j in {0,...,4} { + \ifnum\j>\i + \draw[fill=white, draw=black!40, line width=0.15pt] + (\j*0.22, -\i*0.16) rectangle (\j*0.22+0.20, -\i*0.16-0.14); + \else + \draw[fill=myorange!55, draw=myorange, line width=0.15pt] + (\j*0.22, -\i*0.16) rectangle (\j*0.22+0.20, -\i*0.16-0.14); + \fi + } + } + \end{scope} + % + % Arrow down + \draw[->, line width=0.4pt, black!60] (3.18, 2.05) -- (3.18, 1.75); + % + % AdaLN-Zero block 2 + \draw[fill=white, draw=black!60, line width=0.4pt, rounded corners=1pt] + (1.55, 1.25) rectangle (4.80, 1.75); + \node[font=\scriptsize] at (3.18, 1.50) + {AdaLN-Zero \,($\gamma_2, \beta_2, \alpha_2$)}; + % Curved conditioning arrow from time emb to AdaLN-2 + \draw[->, dashed, line width=0.4pt, black!50] + (0.75, 3.60) .. controls (0.40, 2.30) and (0.80, 1.50) .. (1.55, 1.50); + % + % Arrow down + \draw[->, line width=0.4pt, black!60] (3.18, 1.25) -- (3.18, 0.95); + % + % MLP block + \draw[fill=white, draw=black!60, line width=0.4pt, rounded corners=1pt] + (1.55, 0.45) rectangle (4.80, 0.95); + \node[font=\scriptsize] at (3.18, 0.70) + {MLP \,($d \to 4d \to d$)}; + % + % "× 4 layers" stacking annotation at the bottom + \node[font=\scriptsize\itshape, anchor=south, text=black!70] + at (2.5, 0.05) {(stacked $\times\,4$ layers)}; +\end{scope} + +% Helper coordinates for input/output of velocity field (so arrows still work) +\coordinate (vel_west) at (14.0, 0); +\coordinate (vel_east) at (19.0, 0); + +% ----- (5) Two heads ----- +\node[box, minimum width=22mm] (vh) at (20.5, 0.7) {% + $v_\theta$ head\\ + {\scriptsize (continuous)}% +}; +\node[novelbox, minimum width=22mm] (dh) at (20.5, -0.7) {% + \contrib\ logits head\\ + {\scriptsize (discrete)}% +}; + +\draw[arrow] (vel_east) -- (vh.west); +\draw[arrow] (vel_east) -- (dh.west); + +% ----- Loss equation ----- +\node[losseq, minimum width=190mm, font=\small, align=center] (loss) at (9.85, -3.7) {% + $\mathcal{L} \;=\; + \underbrace{\| v_\theta(x_t,t) - (x_1 - x_0) \|^2}_{\text{continuous CFM}} + \;+\; \lambda \cdot + \underbrace{\mathrm{CE}(\mathrm{logits},\, x_{\mathrm{disc}})}_{\text{Discrete FM}\,\contrib}$% +}; + +% ========================================================================= +% INFERENCE ROW (no row title) +% ========================================================================= + +\node[databox, minimum width=18mm] (testflow) at (1.5, -5.7) {% + {\scriptsize test}\\ flow% +}; + +\node[box, dashed, minimum width=28mm] (frozen) at (4.7, -5.7) {% + \textsc{Frozen}\\ + \textsc{Backbone}\\ + {\scriptsize $v_\theta$ + logits}% +}; + +\node[box, minimum width=34mm] (svec) at (8.6, -5.7) {% + \textsc{Score Vector} (10-d)\\[0.5mm] + {\scriptsize\texttt{terminal\_norm}}\\ + {\scriptsize\texttt{terminal\_packet}}\\ + {\scriptsize\texttt{disc\_nll\_total}, \dots}% +}; + +\node[novelbox, minimum width=46mm, minimum height=22mm] (mahal) at (13.7, -5.7) {% + \contrib\ \textsc{Mahalanobis-OAS Router}\\[1mm] + $D^2 = (s-\mu)^\top \Sigma^{-1}_{\mathrm{OAS}} (s-\mu)$\\[1mm] + {\scriptsize\itshape fit on benign val (no labels)}% +}; + +\node[databox, minimum width=18mm, fill=myblue!10] (out) at (18.5, -5.7) {% + AUROC\\ + $s_{\text{anomaly}}$% +}; + +\draw[arrow] (testflow) -- (frozen); +\draw[arrow] (frozen) -- (svec); +\draw[arrow] (svec) -- (mahal); +\draw[arrow] (mahal) -- (out); + +% ========================================================================= +% LEGEND +% ========================================================================= +\node[draw=black!40, rounded corners=2pt, fill=white, inner sep=5pt, align=left, + font=\scriptsize, anchor=north west] at (0, -7.3) {% + \contrib\ \textbf{Our contributions} \quad + \tikz\fill[myblue!25, draw=myblue, line width=0.4pt] (0,0) rectangle (0.22,0.16); + \ continuous \quad + \tikz\fill[myorange!30, draw=myorange, line width=0.4pt] (0,0) rectangle (0.22,0.16); + \ discrete (DFM bits) \quad + \tikz\fill[mygray!30, draw=mygray, line width=0.4pt, dash pattern=on 0.4pt off 0.4pt] (0,0) rectangle (0.22,0.16); + \ channel-pad (9-d feat in 21-d slot) \quad + \tikz\draw[dashed, draw=black!50, line width=0.4pt] (0,0) rectangle (0.22,0.16); + \ sequence-pad (whole token, $n={Stealth[length=2.5mm]}] + +% ============ 1. PCAP FILE ICON ============ +\begin{scope}[shift={(0,0)}] + % file body with folded corner (drawn as polygon) + \fill[myblue!12] (0,0) -- (0,3.0) -- (1.4,3.0) -- (1.9,2.5) -- (1.9,0) -- cycle; + \draw[myblue!80, line width=0.7pt] + (0,0) -- (0,3.0) -- (1.4,3.0) -- (1.9,2.5) -- (1.9,0) -- cycle; + % the folded triangle on top-right corner + \fill[myblue!30] (1.4,3.0) -- (1.4,2.5) -- (1.9,2.5) -- cycle; + \draw[myblue!80, line width=0.5pt] (1.4,3.0) -- (1.4,2.5) -- (1.9,2.5); + % file title + \node[font=\bfseries\footnotesize, text=myblue!90!black] at (0.95, 2.1) {flow.pcap}; + % decorative "byte" lines (deterministic widths) + \foreach \y/\rl in {1.65/1.30, 1.40/1.05, 1.15/1.40, 0.90/0.85, 0.65/1.20, 0.40/0.75} { + \draw[myblue!45, line width=0.35pt] (0.18, \y) -- (0.18+\rl, \y); + } + \node[font=\scriptsize, anchor=north] at (0.95, -0.1) {raw bytes}; +\end{scope} + +% Arrow 1: parse +\draw[->, thick, black!60] + (2.05, 1.5) -- (3.20, 1.5) + node[midway, above, font=\scriptsize\itshape, text=black!70] {dpkt parse}; + +% ============ 2. DECODED PACKET STREAM ============ +\begin{scope}[shift={(3.4, 0)}] + % four packets along time axis + \foreach \j in {0,...,3} { + \pgfmathsetmacro{\xx}{\j * 1.45} + % packet frame + \draw[fill=white, draw=black!55, line width=0.5pt, rounded corners=1pt] + (\xx, 0.5) rectangle (\xx+1.25, 3.05); + % size field (myblue) + \draw[fill=myblue!35, draw=myblue, line width=0.4pt] + (\xx+0.08, 2.78) rectangle (\xx+0.42, 2.93); + \node[font=\tiny, anchor=west] at (\xx+0.45, 2.86) {size}; + % IAT field (mygreen) + \draw[fill=mygreen!35, draw=mygreen, line width=0.4pt] + (\xx+0.08, 2.55) rectangle (\xx+0.42, 2.70); + \node[font=\tiny, anchor=west] at (\xx+0.45, 2.63) {IAT}; + % direction (mypurple) + \draw[fill=mypurple!35, draw=mypurple, line width=0.4pt] + (\xx+0.08, 2.32) rectangle (\xx+0.22, 2.47); + \node[font=\tiny, anchor=west] at (\xx+0.25, 2.40) {dir}; + % flag bits row — show a real TCP 3-way handshake + data: + % pkt0: SYN (client SYN) + % pkt1: SYN+ACK (server SYN-ACK) + % pkt2: ACK (client ACK, handshake done) + % pkt3: PSH+ACK (data segment) + \ifcase\j + \def\flagS{1}\def\flagF{0}\def\flagR{0}\def\flagP{0}\def\flagA{0}\or + \def\flagS{1}\def\flagF{0}\def\flagR{0}\def\flagP{0}\def\flagA{1}\or + \def\flagS{0}\def\flagF{0}\def\flagR{0}\def\flagP{0}\def\flagA{1}\or + \def\flagS{0}\def\flagF{0}\def\flagR{0}\def\flagP{1}\def\flagA{1}\fi + \foreach \fname/\fval/\k in {S/\flagS/0, F/\flagF/1, R/\flagR/2, P/\flagP/3, A/\flagA/4} { + \pgfmathsetmacro{\fx}{\xx + 0.08 + \k*0.22} + \ifnum\fval=1 + \draw[fill=myorange!70, draw=myorange, line width=0.4pt] + (\fx, 2.05) rectangle (\fx+0.18, 2.22); + \node[font=\tiny, white] at (\fx+0.09, 2.135) {\fname}; + \else + \draw[fill=white, draw=myorange!60, line width=0.4pt] + (\fx, 2.05) rectangle (\fx+0.18, 2.22); + \node[font=\tiny, text=myorange!80!black] at (\fx+0.09, 2.135) {\fname}; + \fi + } + % win + \draw[fill=myblue!20, draw=myblue!60, line width=0.4pt] + (\xx+0.08, 1.78) rectangle (\xx+0.42, 1.93); + \node[font=\tiny, anchor=west] at (\xx+0.45, 1.86) {win}; + % decorative "payload" content + \draw[black!30, line width=0.4pt] (\xx+0.08, 1.45) -- (\xx+1.17, 1.45); + \draw[black!30, line width=0.4pt] (\xx+0.08, 1.25) -- (\xx+0.95, 1.25); + \draw[black!30, line width=0.4pt] (\xx+0.08, 1.05) -- (\xx+1.10, 1.05); + \draw[black!30, line width=0.4pt] (\xx+0.08, 0.85) -- (\xx+0.75, 0.85); + % packet label below + \node[font=\scriptsize, text=black!70] at (\xx+0.625, 0.32) {pkt$_\j$}; + } + % time axis arrow + \draw[->, line width=0.4pt, black!60] (-0.05, 0.05) -- (6.0, 0.05); + \node[font=\tiny, anchor=west, text=black!60] at (6.05, 0.05) {time}; +\end{scope} + +% Arrow 2: extract +\draw[->, thick, black!60] + (9.5, 1.7) -- (10.7, 1.7) + node[midway, above, font=\scriptsize\itshape, text=black!70] {extract}; + +% ============ 3. 9-D FEATURE TOKEN ============ +\begin{scope}[shift={(10.85, -0.5)}] + % 9 channels stacked vertically with type-grouped coloring + % 0 = continuous (z-scored), 1 = discrete (DFM bit) + \foreach \name/\g/\i in {% + log\_size/0/0, + log\_dt/0/1, + direction/1/2, + SYN/1/3, + FIN/1/4, + RST/1/5, + PSH/1/6, + ACK/1/7, + log\_win/0/8} { + \pgfmathsetmacro{\yp}{3.5 - \i*0.36} + \ifnum\g=0 + \draw[fill=myblue!25, draw=myblue, line width=0.5pt] + (0, \yp) rectangle (1.35, \yp+0.32); + \else + \draw[fill=myorange!30, draw=myorange, line width=0.5pt] + (0, \yp) rectangle (1.35, \yp+0.32); + \fi + \node[font=\scriptsize] at (0.675, \yp+0.16) {\name}; + } + % brace + label + \draw[decorate, decoration={brace, amplitude=4pt}, line width=0.5pt, black!60] + (1.45, 3.82) -- (1.45, 0.62) + node[midway, right=4pt, font=\scriptsize, align=left, text=black!70] + {9-d packet token\\[1pt] {[}cont (3) + disc (6){]}}; +\end{scope} + +% Bottom legend +\node[draw=black!30, rounded corners=2pt, fill=white, inner sep=4pt, font=\scriptsize, + align=left, anchor=north] + at (7.5, -1.1) {% + \tikz\fill[myblue!25, draw=myblue, line width=0.4pt] (0,0) rectangle (0.25,0.18); + \ continuous (z-scored) \quad + \tikz\fill[myorange!30, draw=myorange, line width=0.4pt] (0,0) rectangle (0.25,0.18); + \ discrete (kept as bits, fed to DFM head)}; + +\end{tikzpicture} +\end{document} diff --git a/paper/figures/tensors/01_h_in.svg b/paper/figures/tensors/01_h_in.svg new file mode 100644 index 0000000..97a3ab4 --- /dev/null +++ b/paper/figures/tensors/01_h_in.svg @@ -0,0 +1,27 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/paper/figures/tensors/02_h_after_LN.svg b/paper/figures/tensors/02_h_after_LN.svg new file mode 100644 index 0000000..83d0512 --- /dev/null +++ b/paper/figures/tensors/02_h_after_LN.svg @@ -0,0 +1,27 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/paper/figures/tensors/03_h_after_gamma.svg b/paper/figures/tensors/03_h_after_gamma.svg new file mode 100644 index 0000000..072e393 --- /dev/null +++ b/paper/figures/tensors/03_h_after_gamma.svg @@ -0,0 +1,27 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/paper/figures/tensors/04_h_after_beta.svg b/paper/figures/tensors/04_h_after_beta.svg new file mode 100644 index 0000000..f1ffbe6 --- /dev/null +++ b/paper/figures/tensors/04_h_after_beta.svg @@ -0,0 +1,27 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/paper/figures/tensors/05_attention_heatmap.svg b/paper/figures/tensors/05_attention_heatmap.svg new file mode 100644 index 0000000..7445d43 --- /dev/null +++ b/paper/figures/tensors/05_attention_heatmap.svg @@ -0,0 +1,147 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/paper/figures/tensors/06_attn_raw.svg b/paper/figures/tensors/06_attn_raw.svg new file mode 100644 index 0000000..2de529b --- /dev/null +++ b/paper/figures/tensors/06_attn_raw.svg @@ -0,0 +1,146 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/paper/figures/tensors/07_attn_mask.svg b/paper/figures/tensors/07_attn_mask.svg new file mode 100644 index 0000000..651f22f --- /dev/null +++ b/paper/figures/tensors/07_attn_mask.svg @@ -0,0 +1,146 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/paper/figures/tensors/08_attn_masked.svg b/paper/figures/tensors/08_attn_masked.svg new file mode 100644 index 0000000..4290460 --- /dev/null +++ b/paper/figures/tensors/08_attn_masked.svg @@ -0,0 +1,146 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/paper/figures/tensors/gen_attn_heatmap.py b/paper/figures/tensors/gen_attn_heatmap.py new file mode 100644 index 0000000..0c7df3a --- /dev/null +++ b/paper/figures/tensors/gen_attn_heatmap.py @@ -0,0 +1,68 @@ +"""Generate the causal-packet MHSA attention heatmap SVG. + +12 x 12 attention matrix, rounded-rectangle cells, no text labels: + - row 0 and col 0 (FLOW token): cOrange!85 (full attention) + - lower triangular within [1:, 1:]: cOrange decaying with distance + (cOrange!{75 - 4*dist}) + - upper triangular (blocked): white + light grey border + +Run: + python gen_attn_heatmap.py +""" +from pathlib import Path + +CORANGE = (230, 126, 34) # RGB of cOrange palette +WHITE = (255, 255, 255) + +CELL = 30 # px per cell (square) +GAP = 2 # px between cells +PAD = 6 # px outer padding +RADIUS = 4 # px corner radius for cells +N = 12 # 12 x 12 grid + +STRIDE = CELL + GAP +SIZE = N * CELL + (N - 1) * GAP + 2 * PAD + + +def mix_orange(p: int) -> str: + """cOrange mixed with white at p% (matches TikZ cOrange!p).""" + f = p / 100 + r = round(CORANGE[0] * f + WHITE[0] * (1 - f)) + g = round(CORANGE[1] * f + WHITE[1] * (1 - f)) + b = round(CORANGE[2] * f + WHITE[2] * (1 - f)) + return f"#{r:02X}{g:02X}{b:02X}" + + +def cell_style(i: int, j: int) -> tuple[str, str, float]: + """(fill, stroke, stroke_width) for cell (row=i, col=j).""" + if i == 0 or j == 0: # FLOW row / col + return mix_orange(85), "#B8651F", 0.6 + if j > i: # blocked (upper-tri) + return "#FFFFFF", "#BBBBBB", 0.5 + opa = max(15, 75 - (i - j) * 4) # lower-tri decay + return mix_orange(opa), "#999999", 0.3 + + +def make_svg() -> str: + parts = [ + f'', + f' ', + ] + for i in range(N): + for j in range(N): + x = PAD + j * STRIDE + y = PAD + i * STRIDE + fill, stroke, sw = cell_style(i, j) + parts.append( + f' ' + ) + parts.append('') + return '\n'.join(parts) + '\n' + + +OUT = Path(__file__).parent +(OUT / "05_attention_heatmap.svg").write_text(make_svg()) +print(f"wrote 05_attention_heatmap.svg ({SIZE}x{SIZE})") diff --git a/paper/figures/tensors/gen_attn_perspective.py b/paper/figures/tensors/gen_attn_perspective.py new file mode 100644 index 0000000..6cff602 --- /dev/null +++ b/paper/figures/tensors/gen_attn_perspective.py @@ -0,0 +1,136 @@ +"""Generate 3 perspective (isometric) SVGs for the MHSA attention story: + + 06_attn_raw.svg — raw attention pattern, no mask + 07_attn_mask.svg — the causal-packet mask itself (binary shape) + 08_attn_masked.svg — raw attention with the mask applied + +Each cell is rendered as a parallelogram via a 30°/30° isometric projection. +All three SVGs share the same dimensions and the same projection so they can +be composed / cross-faded in drawio. +""" +from pathlib import Path +import math + +# --- palette ---------------------------------------------------------------- +CORANGE = (230, 126, 34) +WHITE = (255, 255, 255) + +# --- geometry --------------------------------------------------------------- +CELL = 28 # local cell size (before projection) +N = 12 # 12 x 12 grid +PAD = 24 + +COS30 = math.sqrt(3) / 2 # ≈ 0.866 +SIN30 = 0.5 + + +def project(x: float, y: float) -> tuple[float, float]: + """Map local (x, y) to screen (sx, sy) via 30°/30° isometric projection. + + Local x-axis (cols) goes down-right, local y-axis (rows) goes down-left. + Origin (0, 0) ends up at the TOP of the projected diamond. + """ + sx = x * COS30 - y * COS30 + sy = x * SIN30 + y * SIN30 + return sx, sy + + +def _bbox() -> tuple[float, float, float, float]: + corners = [ + project(0, 0), + project(N * CELL, 0), + project(0, N * CELL), + project(N * CELL, N * CELL), + ] + xs, ys = [c[0] for c in corners], [c[1] for c in corners] + return min(xs), max(xs), min(ys), max(ys) + + +_xmin, _xmax, _ymin, _ymax = _bbox() +W = int(_xmax - _xmin + 2 * PAD) +H = int(_ymax - _ymin + 2 * PAD) +OX = -_xmin + PAD +OY = -_ymin + PAD + + +def mix_orange(p: int) -> str: + f = p / 100 + r = round(CORANGE[0] * f + WHITE[0] * (1 - f)) + g = round(CORANGE[1] * f + WHITE[1] * (1 - f)) + b = round(CORANGE[2] * f + WHITE[2] * (1 - f)) + return f"#{r:02X}{g:02X}{b:02X}" + + +def cell_poly(i: int, j: int) -> str: + """Return SVG points string for the parallelogram at row i, col j.""" + pts = [] + for di, dj in [(0, 0), (0, 1), (1, 1), (1, 0)]: + x = (j + dj) * CELL + y = (i + di) * CELL + sx, sy = project(x, y) + pts.append(f"{sx + OX:.2f},{sy + OY:.2f}") + return " ".join(pts) + + +def make_svg(style_fn) -> str: + # No background rect — SVGs are transparent so they can be cleanly + # overlaid / placed on any drawio canvas color. + parts = [ + f'', + ] + # draw row-by-row, far rows first so near rows overlay (depth order). + for i in range(N): + for j in range(N): + fill, stroke, sw = style_fn(i, j) + parts.append( + f' ' + ) + parts.append('') + return '\n'.join(parts) + '\n' + + +# ---------------------------------------------------------------------------- +# Style functions for each of the 3 SVGs +# ---------------------------------------------------------------------------- + +def style_raw(i: int, j: int) -> tuple[str, str, float]: + """Raw attention before any mask: band-matrix-like soft pattern. + + Brightness peaks on the diagonal and decays with |i - j|, giving a + realistic-looking dense attention map. + """ + dist = abs(i - j) + opa = max(20, 75 - dist * 4) + return mix_orange(opa), "#9C9C9C", 0.4 + + +def style_mask(i: int, j: int) -> tuple[str, str, float]: + """The causal-packet mask itself, as a binary visual: + + - allowed cells (FLOW row/col + lower-tri): orange-light + - blocked cells (upper-tri except FLOW): dark gray + """ + blocked = (i != 0) and (j != 0) and (j > i) + if blocked: + return "#3A3A3A", "#1F1F1F", 0.5 + return mix_orange(60), "#B8651F", 0.4 + + +def style_masked(i: int, j: int) -> tuple[str, str, float]: + """Raw attention AFTER applying the causal-packet mask: blocked cells + are whited out, allowed cells keep EXACTLY their raw attention intensity + (i.e. masked == raw × mask, nothing else changes). + """ + blocked = (i != 0) and (j != 0) and (j > i) + if blocked: + return "#FFFFFF", "#CCCCCC", 0.4 + return style_raw(i, j) + + +OUT = Path(__file__).parent +(OUT / "06_attn_raw.svg").write_text(make_svg(style_raw)) +(OUT / "07_attn_mask.svg").write_text(make_svg(style_mask)) +(OUT / "08_attn_masked.svg").write_text(make_svg(style_masked)) +print(f"wrote 06_attn_raw.svg / 07_attn_mask.svg / 08_attn_masked.svg ({W}x{H})") diff --git a/paper/figures/tensors/gen_tensors.py b/paper/figures/tensors/gen_tensors.py new file mode 100644 index 0000000..5d8d4ec --- /dev/null +++ b/paper/figures/tensors/gen_tensors.py @@ -0,0 +1,71 @@ +"""Generate 4 SVG tensor visualisations for the AdaLN modulation flow. + +Each SVG is a 6 col x 4 row grid where cell color = cBlue mixed with white at +the per-cell opacity (0..100). Same color values used in the TikZ panel (c). + +Run: + python gen_tensors.py +""" +from pathlib import Path + +CBLUE = (52, 110, 180) # RGB of cBlue palette in the TikZ figure +WHITE = (255, 255, 255) +CELL = 50 # px per cell +BORDER = 3 # outer padding (px) +COLS, ROWS = 6, 4 +W = COLS * CELL + 2 * BORDER +H = ROWS * CELL + 2 * BORDER + + +def mix(p: int) -> str: + """Mix cBlue with white at p% (matches TikZ cBlue!p).""" + f = p / 100 + r = round(CBLUE[0] * f + WHITE[0] * (1 - f)) + g = round(CBLUE[1] * f + WHITE[1] * (1 - f)) + b = round(CBLUE[2] * f + WHITE[2] * (1 - f)) + return f"#{r:02X}{g:02X}{b:02X}" + + +def make_svg(opacities: list[int]) -> str: + parts = [ + f'', + f' ', + ] + for k, op in enumerate(opacities): + i, j = k % COLS, k // COLS + x = BORDER + i * CELL + y = BORDER + j * CELL + parts.append( + f' ' + ) + parts.append('') + return '\n'.join(parts) + '\n' + + +# Same opacity arrays used in the TikZ panel (c). Row-major: 6 cols * 4 rows. +TENSORS = { + "01_h_in": [30, 80, 50, 40, 90, 60, + 70, 20, 80, 50, 40, 90, + 60, 50, 90, 30, 80, 40, + 70, 30, 60, 80, 40, 70], + "02_h_after_LN": [50, 30, 70, 50, 40, 60, + 40, 60, 50, 50, 40, 60, + 50, 50, 60, 40, 60, 40, + 50, 40, 50, 60, 40, 60], + "03_h_after_gamma":[60, 12, 70, 25, 52, 60, + 48, 24, 50, 25, 52, 60, + 60, 20, 60, 20, 78, 40, + 60, 16, 50, 30, 52, 60], + "04_h_after_beta": [80, 32, 90, 45, 72, 80, + 68, 44, 70, 45, 72, 80, + 80, 40, 80, 40, 90, 60, + 80, 36, 70, 50, 72, 80], +} + +OUT = Path(__file__).parent +for name, vals in TENSORS.items(): + assert len(vals) == COLS * ROWS, f"{name}: need {COLS*ROWS} values" + (OUT / f"{name}.svg").write_text(make_svg(vals)) + print(f"wrote {name}.svg ({W}x{H})") diff --git a/paper/intro.md b/paper/intro.md new file mode 100644 index 0000000..398608e --- /dev/null +++ b/paper/intro.md @@ -0,0 +1,18 @@ +Network intrusion detection systems (NIDS) in production are dogged by two persistent failures. Alert volume overwhelms downstream triage: industry surveys and recent reviews report false-positive rates that frequently exceed 90%, and at the upper end approach 99% [Trend2024; ACM-CSur-2024]. Detectors that score well in one environment also lose a substantial fraction of that performance once evaluated on traffic from a different deployment [Cross2402.10974; Tand2025]. Modern NIDS research has largely converged on unsupervised anomaly detection, but neither failure has a settled answer within that paradigm. With within-dataset scores on the standard public benchmarks now narrowed to within reporting noise, the substantive evaluation axis has shifted to cross-dataset robustness, on which the field is far from converged. + +The unsupervised NIDS toolkit has converged on three families of methods, all of which reduce a packet stream to a single anomaly score. Reconstruction-based detectors such as autoencoders, KitNET, and MemAE [Kitsune; MemAE] score by reconstruction error and exhibit a documented identity-mapping failure in which anomalies far from the benign manifold can still be reconstructed near-perfectly, undermining the core assumption [AE-Unreliable-2025; NeurIPS24-Reconstruction]. Density-based detectors built on normalising flows (NF) are the current public SOTA; the strongest recent pipeline reports 0.93 AUROC within-dataset on CIC-DDoS2019, with cross-domain transfer ranging from 0.89 to 0.93 depending on direction [Shafir2026]. The log-likelihood these methods rely on, however, is known to dissociate from anomaly status once the benign distribution drifts [NFAD2021]. Diffusion-based detectors [ConMD2026; DMAD2025] and optimised GAN variants [TIPSO-GAN-NDSS2026] have arrived in 2025–2026 with strong within-dataset numbers but share the same underlying object: a single scalar derived from a homogeneous probabilistic model fit to benign traffic. + +Why these density-based scores transfer poorly has gone uncharacterised. We identify a structural failure mode we call source-likeness collapse. Under target-domain drift, the log-likelihood emitted by a benign-fit generative model no longer discriminates "x is benign vs malicious" but rather "x lies in the source benign distribution vs not"; the two coincide only when there is no shift, and diverge as drift grows. Empirically, across three independent Continuous Flow-Matching backbones (CFM; framework introduced below) and 16 candidate score channels, the canonical density-based score for these models (the terminal-norm of the velocity field) drops to AUROC ≤ 0.63 when trained on CIC-DDoS2019 and evaluated on CIC-IDS2017, with four of the twelve off-diagonal cells of our 4×4 cross-dataset matrix falling below 0.57 (near-random). The collapse persists across recipes, ruling out hyperparameter artefacts and indicating a structural property of likelihood as an anomaly proxy. + +Two recent generative frameworks point to a way out. Continuous Flow Matching [Lipman2023; OT-CFM-Tong2024] learns a velocity field rather than a reconstruction, side-stepping the identity-mapping trap of reconstruction-based detectors. Discrete Flow Matching [Gat-NeurIPS2024] extends the same machinery to categorical state spaces. Network packets sit naturally in both regimes: each packet contributes three continuous channels (size, inter-arrival time, TCP window) and six binary channels (direction and five TCP flags). To our knowledge, neither paradigm has been applied to packet-sequence NIDS, although Flow Matching has been validated for image [rFM2025] and tabular [TCCM-NeurIPS2025] anomaly detection. Mixed continuous–discrete modelling emits a family of complementary scores rather than the single homogeneous likelihood under which source-likeness collapse occurs, and provides a structural path to the discrete protocol semantics that prior NF / autoencoder / GAN approaches must either Gaussianise away or ignore. + +We present JANUS, an unsupervised packet-sequence anomaly detector with three components. +1. A causal-packet Transformer backbone that produces a temporally-ordered representation of each flow. +2. Two jointly-trained Flow-Matching heads on benign traffic, one over the continuous packet channels and one over the discrete protocol channels. Together they emit a family of complementary scores rather than a single likelihood. +3. A benign-only aggregator that compresses the score family into a single deployable scalar, fit on target-domain benign validation data and never on attack labels. +Together, the discrete head supplies a transfer-stable signal that survives source-likeness collapse, and the aggregator combines it with the residual information in the continuous-head scores rather than discarding them. The unsupervised contract holds end-to-end. +We make four contributions: +- (C1) First Flow-Matching detector for NIDS. To the best of our knowledge, JANUS is the first network anomaly detector to use Flow Matching as its training objective. It also combines continuous and discrete FM heads, a configuration not present in prior FM anomaly-detection work on image [rFM2025] or tabular [TCCM-NeurIPS2025] data. +- (C2) Characterisation of source-likeness collapse. We name and analyse a structural failure mode in which density-based anomaly scores degrade into source-domain membership classifiers under cross-dataset shift. The phenomenon persists across three independent CFM backbones and all 16 candidate score channels we evaluate, identifying it as a property of density-based scoring rather than of any specific backbone. This explains a cross-domain failure mode that prior work observed but did not name. +- (C3) A benign-only Mahalanobis aggregator with Oracle-Approximating-Shrinkage (OAS) covariance that compresses the score family into a single deployable scalar without consuming attack labels. We compare five aggregators (max-z, plain Mahalanobis, Ledoit–Wolf, OAS, and score-subset variants) and observe sensitivity ≤ 0.005 in AUROC across them, supporting the design as robust rather than hyperparameter-tuned. +- (C4) Cross-dataset robustness with within-dataset competitiveness. On a 4×4 cross-dataset matrix (12 off-diagonal directions, three seeds per cell), JANUS averages +0.175 AUROC over the terminal-norm baseline and recovers all four collapse cells (terminal-norm < 0.57) to ≥ 0.75. It exceeds the Shafir NF baseline [Shafir2026] by +0.07 AUROC (0.96 vs 0.89) when trained on CIC-IDS2017 and evaluated on CIC-DDoS2019, and matches it (0.93) when the direction is reversed. Within-dataset, it exceeds the NF SOTA on three benchmarks by margins of +0.054 to +0.118, all exceeding the standard deviation across three seeds. \ No newline at end of file diff --git a/paper/references.bib b/paper/references.bib new file mode 100644 index 0000000..3903d14 --- /dev/null +++ b/paper/references.bib @@ -0,0 +1,293 @@ +% ============================================================================= +% JANUS — Verified BibTeX for intro.md +% Cite-key spelling matches the keys used in paper/intro.md. +% Each entry includes a `url` field linking to the canonical source page so the +% reference can be re-checked without re-searching. +% +% IMPORTANT NOTES (please review before submitting): +% +% * Trend2024: The Trend Micro 2024 "World Tour Survey" reports 51% of +% SOC teams feel overwhelmed by alert volume but does NOT +% state ">90% / 99%" false-positive rates. The 99% figure +% traces to Alahmadi et al., USENIX Security 2022, which +% is included below as @Alahmadi2022. Consider citing +% [Alahmadi2022; Trend2024] together, or replacing. +% +% * ACM-CSur-2024: Tariq et al. is published in ACM Computing Surveys +% Vol. 57(9), April 2025 — not 2024. The cite key is +% preserved per intro.md, but @year is 2025. +% +% * Shafir2026: Venue is IEEE/ACM Transactions on Networking (ToN), +% not IEEE TNSM. Verified via DOI 10.1109/TON.2025.3617580. +% +% * NFAD2021: Kirichenko et al. is NeurIPS 2020 (arXiv 2006.08545), +% not 2021. Cite key preserved per intro.md. +% +% * AE-Unreliable-2025: Bouman & Heskes was *withdrawn* from ICLR 2025; +% cited here as an arXiv preprint (2501.13864). +% +% * NeurIPS24-Reconstruction: The closest NeurIPS 2024 paper on the +% reconstruction-AD identity-mapping limitation is Kim +% et al., "Rethinking Reconstruction-based Graph-Level +% Anomaly Detection". It is graph-level, not generic +% image/tabular. Verify the citation matches your intent. +% +% * Tand2025: Best match for a Taylor & Francis 2025 cross-dataset +% NIDS paper is Connection Science 2025 (HDSE-IDS). +% The "0.10–0.30 AUROC drop" framing in intro.md is +% primarily supported by Cross2402.10974, not by +% Tand2025 directly. +% +% * rFM2025: arXiv 2508.05461's actual title is "Time-reversed Flow +% Matching with Worst Transport in High-dimensional Latent +% Space for Image Anomaly Detection". Earlier survey +% notes called it "How and Why: Taming Flow Matching..." +% — that title is incorrect. Updated below. +% ============================================================================= + + +% --- Operational pain points (FP rates, alert fatigue) ----------------------- + +@misc{Trend2024, + author = {{Trend Micro}}, + title = {{SOC Around the Clock: World Tour Survey Findings}}, + year = {2024}, + howpublished = {Trend Micro Research Report}, + url = {https://www.trendmicro.com/en_us/research/24/k/world-tour-survey-results.html}, + note = {Survey of 2,303 IT security/SOC decision makers; 51\% report + feeling overwhelmed by alert volume.} +} + +@inproceedings{Alahmadi2022, + author = {Bushra A. Alahmadi and Louise Axon and Ivan Martinovic}, + title = {99\% False Positives: A Qualitative Study of {SOC} Analysts' + Perspectives on Security Alarms}, + booktitle = {31st USENIX Security Symposium (USENIX Security 22)}, + year = {2022}, + pages = {2783--2800}, + publisher = {USENIX Association}, + url = {https://www.usenix.org/conference/usenixsecurity22/presentation/alahmadi} +} + +@article{ACM-CSur-2024, + author = {Shahroz Tariq and Mohan Baruwal Chhetri and Surya Nepal and + C{\'e}cile Paris}, + title = {Alert Fatigue in Security Operations Centres: + Research Challenges and Opportunities}, + journal = {ACM Computing Surveys}, + volume = {57}, + number = {9}, + articleno = {224}, + year = {2025}, + doi = {10.1145/3723158}, + url = {https://dl.acm.org/doi/10.1145/3723158} +} + + +% --- Cross-dataset NIDS robustness ------------------------------------------- + +@article{Cross2402.10974, + author = {Marco Cantone and Claudio Marrocco and Alessandro Bria}, + title = {On the Cross-Dataset Generalization of Machine Learning + for Network Intrusion Detection}, + journal = {arXiv preprint arXiv:2402.10974}, + year = {2024}, + eprint = {2402.10974}, + archivePrefix = {arXiv}, + primaryClass = {cs.CR}, + url = {https://arxiv.org/abs/2402.10974} +} + +@article{Tand2025, + title = {Enhancing generalization of cross-domain intrusion detection: + a heterogeneous deep stacked ensemble approach}, + journal = {Connection Science}, + publisher = {Taylor \& Francis}, + year = {2025}, + doi = {10.1080/09540091.2025.2599708}, + url = {https://www.tandfonline.com/doi/full/10.1080/09540091.2025.2599708}, + note = {Author list to be confirmed from publisher page (publisher + returned 403 to automated fetch).} +} + + +% --- Reconstruction-based detectors ------------------------------------------ + +@inproceedings{Kitsune, + author = {Yisroel Mirsky and Tomer Doitshman and Yuval Elovici and + Asaf Shabtai}, + title = {{Kitsune}: An Ensemble of Autoencoders for Online Network + Intrusion Detection}, + booktitle = {Network and Distributed System Security Symposium (NDSS)}, + year = {2018}, + eprint = {1802.09089}, + archivePrefix = {arXiv}, + url = {https://arxiv.org/abs/1802.09089} +} + +@inproceedings{MemAE, + author = {Dong Gong and Lingqiao Liu and Vuong Le and Budhaditya Saha and + Moussa Reda Mansour and Svetha Venkatesh and + Anton {van den Hengel}}, + title = {Memorizing Normality to Detect Anomaly: Memory-Augmented Deep + Autoencoder for Unsupervised Anomaly Detection}, + booktitle = {Proceedings of the IEEE/CVF International Conference on + Computer Vision (ICCV)}, + year = {2019}, + pages = {1705--1714}, + eprint = {1904.02639}, + archivePrefix = {arXiv}, + url = {https://openaccess.thecvf.com/content_ICCV_2019/html/Gong_Memorizing_Normality_to_Detect_Anomaly_Memory-Augmented_Deep_Autoencoder_for_Unsupervised_ICCV_2019_paper.html} +} + +@article{AE-Unreliable-2025, + author = {Roel Bouman and Tom Heskes}, + title = {Autoencoders for Anomaly Detection are Unreliable}, + journal = {arXiv preprint arXiv:2501.13864}, + year = {2025}, + eprint = {2501.13864}, + archivePrefix = {arXiv}, + primaryClass = {cs.LG}, + url = {https://arxiv.org/abs/2501.13864}, + note = {Withdrawn ICLR 2025 submission; + OpenReview: https://openreview.net/forum?id=X8XQOLjLX6} +} + +@inproceedings{NeurIPS24-Reconstruction, + author = {Sunwoo Kim and Soo Yong Lee and Fanchen Bu and Shinhwan Kang and + Kyungho Kim and Jaemin Yoo and Kijung Shin}, + title = {Rethinking Reconstruction-based Graph-Level Anomaly Detection: + Limitations and a Simple Remedy}, + booktitle = {Advances in Neural Information Processing Systems (NeurIPS)}, + year = {2024}, + url = {https://openreview.net/forum?id=e2INndPINB} +} + + +% --- Density-based detectors (NF / Diffusion / GAN) -------------------------- + +@article{Shafir2026, + author = {Lior Shafir and Raja Giryes and Avishai Wool}, + title = {Explainable Anomaly Detection in Network Traffic Using + Normalizing Flows}, + journal = {IEEE/ACM Transactions on Networking}, + volume = {34}, + year = {2026}, + doi = {10.1109/TON.2025.3617580}, + url = {https://doi.org/10.1109/TON.2025.3617580} +} + +@inproceedings{NFAD2021, + author = {Polina Kirichenko and Pavel Izmailov and Andrew Gordon Wilson}, + title = {Why Normalizing Flows Fail to Detect Out-of-Distribution Data}, + booktitle = {Advances in Neural Information Processing Systems (NeurIPS)}, + year = {2020}, + eprint = {2006.08545}, + archivePrefix = {arXiv}, + url = {https://arxiv.org/abs/2006.08545}, + note = {NeurIPS 2020 (cite key NFAD2021 retained per intro.md).} +} + +@article{ConMD2026, + author = {Xinglin Lian and Yu Zheng and Yan Liu and Fan Zhou and + Chunlei Peng and Xinbo Gao}, + title = {Contextual Masking Distillation for Network Traffic Anomaly + Detection}, + journal = {IEEE Transactions on Information Forensics and Security}, + volume = {21}, + pages = {1273--1286}, + year = {2026}, + doi = {10.1109/TIFS.2026.3655514}, + url = {https://ieeexplore.ieee.org/document/11358423/} +} + +@article{DMAD2025, + author = {Hui Liu and others}, + title = {A Survey on Diffusion Models for Anomaly Detection}, + journal = {arXiv preprint arXiv:2501.11430}, + year = {2025}, + eprint = {2501.11430}, + archivePrefix = {arXiv}, + primaryClass = {cs.LG}, + url = {https://arxiv.org/abs/2501.11430}, + note = {Submitted to IJCAI 2025 (per associated GitHub repository); + verify final IJCAI proceedings entry before publication.} +} + +@inproceedings{TIPSO-GAN-NDSS2026, + author = {Ernest Akpaku and Jinfu Chen and Joshua Ofoeda}, + title = {{TIPSO-GAN}: Malicious Network Traffic Detection Using a Novel + Optimized Generative Adversarial Network}, + booktitle = {Network and Distributed System Security Symposium (NDSS)}, + year = {2026}, + url = {https://www.ndss-symposium.org/ndss-paper/tipso-gan-malicious-network-traffic-detection-using-a-novel-optimized-generative-adversarial-network/} +} + + +% --- Flow Matching foundations ----------------------------------------------- + +@inproceedings{Lipman2023, + author = {Yaron Lipman and Ricky T. Q. Chen and Heli Ben-Hamu and + Maximilian Nickel and Matt Le}, + title = {Flow Matching for Generative Modeling}, + booktitle = {International Conference on Learning Representations (ICLR)}, + year = {2023}, + eprint = {2210.02747}, + archivePrefix = {arXiv}, + url = {https://arxiv.org/abs/2210.02747} +} + +@article{OT-CFM-Tong2024, + author = {Alexander Tong and Kilian Fatras and Nikolay Malkin and + Guillaume Huguet and Yanlei Zhang and Jarrid Rector-Brooks and + Guy Wolf and Yoshua Bengio}, + title = {Improving and Generalizing Flow-Based Generative Models with + Minibatch Optimal Transport}, + journal = {Transactions on Machine Learning Research (TMLR)}, + year = {2024}, + eprint = {2302.00482}, + archivePrefix = {arXiv}, + url = {https://openreview.net/forum?id=CD9Snc73AW} +} + +@inproceedings{Gat-NeurIPS2024, + author = {Itai Gat and Tal Remez and Neta Shaul and Felix Kreuk and + Ricky T. Q. Chen and Gabriel Synnaeve and Yossi Adi and + Yaron Lipman}, + title = {Discrete Flow Matching}, + booktitle = {Advances in Neural Information Processing Systems (NeurIPS)}, + year = {2024}, + eprint = {2407.15595}, + archivePrefix = {arXiv}, + url = {https://openreview.net/forum?id=GTDKo3Sv9p} +} + + +% --- Flow-Matching anomaly detection (image / tabular) ----------------------- + +@article{rFM2025, + author = {Liangwei Li and Lin Liu and Hanzhe Liang and Juanxiu Liu and + Jing Zhang and Ruqian Hao and Xiaohui Du and Yong Liu and + Pan Li}, + title = {Time-reversed Flow Matching with Worst Transport in + High-dimensional Latent Space for Image Anomaly Detection}, + journal = {arXiv preprint arXiv:2508.05461}, + year = {2025}, + eprint = {2508.05461}, + archivePrefix = {arXiv}, + primaryClass = {cs.CV}, + url = {https://arxiv.org/abs/2508.05461} +} + +@inproceedings{TCCM-NeurIPS2025, + author = {Zhong Li and Qi Huang and Yuxuan Zhu and Lincen Yang and + Mohammad Mohammadi Amiri and Niki van Stein and + Matthijs van Leeuwen}, + title = {Scalable, Explainable and Provably Robust Anomaly Detection + with One-Step Flow Matching}, + booktitle = {Advances in Neural Information Processing Systems (NeurIPS)}, + year = {2025}, + eprint = {2510.18328}, + archivePrefix = {arXiv}, + url = {https://arxiv.org/abs/2510.18328} +} diff --git a/paper/tables/tab_data_efficiency.tex b/paper/tables/tab_data_efficiency.tex new file mode 100644 index 0000000..adc4538 --- /dev/null +++ b/paper/tables/tab_data_efficiency.tex @@ -0,0 +1,21 @@ +\begin{table}[htbp] + \centering + \caption{Data-efficiency sweep on CICIDS2017. Both architectures evaluated on the full 1.2M benign + 500K attack set; only training data size varies.} + \label{tab:data_efficiency} +\begin{tabular}{lrrrrr} +\toprule +arch & n\_train & tnorm\_auroc & tnorm\_auprc & nll\_auroc & nll\_auprc \\ +\midrule +A1 & 1000 & 0.9671 & 0.8985 & 0.9575 & 0.8871 \\ +A1 & 3000 & 0.9791 & 0.9199 & 0.9584 & 0.8862 \\ +A1 & 10000 & 0.9920 & 0.9653 & 0.9856 & 0.9533 \\ +A1 & 30000 & 0.9942 & 0.9772 & 0.9890 & 0.9674 \\ +A1 & 100000 & 0.9948 & 0.9791 & 0.9902 & 0.9686 \\ +A9 & 1000 & 0.9573 & 0.8785 & 0.9458 & 0.8647 \\ +A9 & 3000 & 0.9857 & 0.9380 & 0.9766 & 0.9229 \\ +A9 & 10000 & 0.9931 & 0.9672 & 0.9901 & 0.9612 \\ +A9 & 30000 & 0.9938 & 0.9746 & 0.9906 & 0.9686 \\ +A9 & 100000 & 0.9950 & 0.9809 & 0.9929 & 0.9756 \\ +\bottomrule +\end{tabular} +\end{table} diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..6e63872 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,51 @@ +[project] +name = "JANUS" +version = "0.1.0" +description = "Add your description here" +readme = "README.md" +requires-python = ">=3.12" +dependencies = [ + "dpkt>=1.9.8", + "matplotlib>=3.10.8", + "numpy>=2.4.3", + "pyyaml>=6.0", + "scapy>=2.7.0", + "scikit-learn>=1.8.0", + "torch>=2.9.1", + "torchvision>=0.24.1", + "torchdiffeq>=0.2.5", + "mamba-ssm>=2.3.1", + "causal-conv1d>=1.6.1", + "pandas>=3.0.2", + "umap-learn>=0.5.12", + "pyarrow>=24.0.0", +] + +[build-system] +requires = ["setuptools>=68"] +build-backend = "setuptools.build_meta" + +[tool.setuptools.packages.find] +where = ["src"] + +[tool.uv] +package = false +no-build-isolation-package = ["mamba-ssm", "causal-conv1d"] + +[tool.uv.sources] +torch = [ + { index = "pytorch-cu128" }, +] +torchvision = [ + { index = "pytorch-cu128" }, +] + +[[tool.uv.index]] +name = "pytorch-cu128" +url = "https://download.pytorch.org/whl/cu128" +explicit = true + +[dependency-groups] +dev = [ + "pytest>=9.0.3", +] diff --git a/scripts/auto_transfer_after_merge.sh b/scripts/auto_transfer_after_merge.sh new file mode 100755 index 0000000..f6611c6 --- /dev/null +++ b/scripts/auto_transfer_after_merge.sh @@ -0,0 +1,205 @@ +#!/bin/bash +# Wait for user's merge_shard_artifacts.py to finish, then run transfer eval. +# +# The previous auto_transfer_ddos.sh aborted at STAGE 3 because +# np.savez_compressed on 50+ GB array produces a corrupt zip. User's +# merge_shard_artifacts.py uses uncompressed np.savez and is currently +# running. We just wait for it, validate, then do the 4-cell transfer. +set -uo pipefail + +ROOT=/home/chy/mambafortrafficmodeling +cd "$ROOT" + +SRC_SWEEP="runs/n10k_refactor_20260422_220351" +DST="runs/transfer_ddos" +LOG="$DST/run.log" +DDOS_DIR="datasets/cicddos2019/processed" +mkdir -p "$DST" + +# Append to existing log. +exec > >(tee -a "$LOG") 2>&1 + +N_VAL=20000 +N_ATK=100000 +SPLIT_SEED=42 + +echo "" +echo "=== $(date): [after-merge script] starts ===" +echo "waiting for merge_shard_artifacts.py to finish..." + +elapsed=0 +while pgrep -f "merge_shard_artifacts" > /dev/null; do + sleep 30 + elapsed=$((elapsed + 30)) + if (( elapsed % 180 == 0 )); then + rss=$(pgrep -af "merge_shard_artifacts" | head -1 | awk '{print $1}' | xargs -I{} ps -p {} -o rss= 2>/dev/null || echo "?") + echo "[heartbeat $(date +%H:%M:%S)] merge still running, waited ${elapsed}s rss=${rss} kB" + fi +done +echo "=== $(date): merge exited after ${elapsed}s ===" +sleep 10 + +PACKETS="$DDOS_DIR/packets.npz" +FLOWS="$DDOS_DIR/flows.parquet" + +# ---- validate ---- +if [[ ! -f "$PACKETS" || ! -f "$FLOWS" ]]; then + echo "ERROR: artifacts missing after merge" + echo " $PACKETS : $([[ -f $PACKETS ]] && echo OK || echo MISSING)" + echo " $FLOWS : $([[ -f $FLOWS ]] && echo OK || echo MISSING)" + exit 1 +fi +ls -lh "$PACKETS" "$FLOWS" + +uv run python - <<'EOF' +import numpy as np, pandas as pd, sys +try: + p = np.load('datasets/cicddos2019/processed/packets.npz') + f = pd.read_parquet('datasets/cicddos2019/processed/flows.parquet') + assert set(p.files) == {'packet_tokens', 'packet_lengths', 'flow_id'}, p.files + assert set(f.columns) == {'flow_id', 'label'}, f.columns + assert p['flow_id'].shape[0] == len(f) + assert np.array_equal(p['flow_id'], f['flow_id'].to_numpy()) + n = len(f) + n_benign = int((f['label'] == 'normal').sum()) + print(f'[validate] OK: N={n:,} benign={n_benign:,} attack={n-n_benign:,}') + print(f'[validate] packet_tokens shape/dtype: {p["packet_tokens"].shape} {p["packet_tokens"].dtype}') + print('[validate] label value_counts (top 20):') + print(f['label'].value_counts().head(20).to_string()) +except Exception as e: + print(f'[validate] FAILED: {type(e).__name__}: {e}') + sys.exit(2) +EOF +if [[ $? -ne 0 ]]; then + echo "ERROR: validation failed" + exit 2 +fi + +# ---- transfer detect + per_class per cell ---- +echo "" +echo "=== $(date): transfer detect + per_class (4 cells) ===" +CELLS=( "seed42/s0" "seed42/s0.6" "seed43/s0" "seed43/s0.6" ) + +run_cell() { + local cell=$1 + local src_dir="$SRC_SWEEP/$cell" + local out_dir="$DST/$cell" + + if [[ ! -f "$src_dir/model.pt" ]]; then + echo "SKIP $cell : no model.pt" + return 1 + fi + + mkdir -p "$out_dir" + cp "$src_dir/model.pt" "$out_dir/model.pt" + [[ -f "$src_dir/history.json" ]] && cp "$src_dir/history.json" "$out_dir/" + + echo "" + echo "----- [$cell] $(date) -----" + echo "[detect] starting" + if ! uv run python -m detect \ + --save-dir "$out_dir" \ + --packets-npz "$PACKETS" \ + --flows-parquet "$FLOWS" \ + --n-val "$N_VAL" --n-atk "$N_ATK" \ + --seed "$SPLIT_SEED" \ + 2>&1 | tail -25 + then + echo "ERROR: detect failed for $cell" + return 2 + fi + [[ -f "$out_dir/scores.npz" ]] || { echo "ERROR: no scores.npz for $cell"; return 3; } + + echo "[per_class] starting" + uv run python -m eval.per_class --save-dir "$out_dir" 2>&1 | tail -60 + [[ -f "$out_dir/per_class.json" ]] || { echo "ERROR: no per_class.json for $cell"; return 4; } + echo "[$cell] OK" + return 0 +} + +OK_CELLS=() +FAIL_CELLS=() +for cell in "${CELLS[@]}"; do + if run_cell "$cell"; then OK_CELLS+=("$cell"); else FAIL_CELLS+=("$cell"); fi +done + +echo "" +echo "=== per-cell status ===" +echo "OK (${#OK_CELLS[@]}): ${OK_CELLS[*]:-none}" +echo "FAIL (${#FAIL_CELLS[@]}): ${FAIL_CELLS[*]:-none}" + +# ---- summary ---- +echo "" +echo "=== $(date): summary ===" +uv run python - "$DST" <<'EOF' +import json, sys +from pathlib import Path +import numpy as np + +dst = Path(sys.argv[1]) +cells = ["seed42/s0", "seed42/s0.6", "seed43/s0", "seed43/s0.6"] +ch = "terminal_norm" +keys = [("overall_auroc","overall AUROC"),("overall_auprc","overall AUPRC"), + ("macro_auroc","macro AUROC"),("macro_auprc","macro AUPRC"), + ("tpr_at_1fpr","TPR@1%FPR"),("fpr_at_95tpr","FPR@95%TPR")] + +header = f"{'cell':<15s}" + "".join(f" {t:>14s}" for _, t in keys) + f" {'flipped':>8s}" +print(header); print("-"*len(header)) +loaded = {} +for c in cells: + jp = dst / c / "per_class.json" + if not jp.exists(): + print(f"{c:<15s} (missing)"); continue + tn = json.loads(jp.read_text())[ch] + loaded[c] = tn + line = f"{c:<15s}" + for k,_ in keys: + v = tn[k] + line += f" {'NaN':>14s}" if (isinstance(v,float) and v!=v) else f" {v:>14.4f}" + line += f" {str(tn['flipped']):>8s}" + print(line) + +if loaded: + print("\n=== mean ± std across seeds (per σ) ===") + for sg in ["0","0.6"]: + pair = [(c,d) for c,d in loaded.items() if c.endswith(f"/s{sg}")] + if len(pair) < 2: + print(f"σ={sg}: {len(pair)} seed(s), skip"); continue + print(f"\n--- σ={sg} ---") + for k,t in keys: + vs = [d[k] for _,d in pair if isinstance(d[k],float) and d[k]==d[k]] + if vs: + print(f" {t:<18s} {np.mean(vs):.4f} ± {np.std(vs,ddof=0):.4f}") + + ref = loaded.get("seed42/s0.6") or next(iter(loaded.values())) + pc = ref["per_class"] + print("\n=== per-class (seed42/s0.6 reference) ===") + print(f"{'class':<30s} {'n':>8s} {'auroc':>8s} {'auprc':>8s} {'tpr@1%':>8s}") + print("-"*70) + for r in pc: + fmt = lambda v: "—" if (isinstance(v,float) and v!=v) else f"{v:.4f}" + print(f" {r['class']:<28s} {r['n']:>8d} " + f"{fmt(r['auroc']):>8s} {fmt(r['auprc']):>8s} {fmt(r['tpr_at_1fpr']):>8s}") + + # Merged-label view. + print("\n=== per-class after label merge (DrDoS_* → stripped) ===") + def norm(name): + return name[len("DrDoS_"):] if name.startswith("DrDoS_") else name + buckets = {} + for r in pc: + if isinstance(r["auroc"],float) and r["auroc"]==r["auroc"]: + buckets.setdefault(norm(r["class"]), []).append(r) + if buckets: + print(f"{'merged class':<20s} {'#shards':>8s} {'n_total':>8s} " + f"{'auroc_wtd':>10s} {'auprc_wtd':>10s}") + print("-"*68) + for k, rs in sorted(buckets.items(), key=lambda x: -sum(r["n"] for r in x[1])): + n_tot = sum(r["n"] for r in rs) + wtd_a = sum(r["auroc"] * r["n"] for r in rs) / max(n_tot, 1) + wtd_ap = sum(r["auprc"] * r["n"] for r in rs) / max(n_tot, 1) + print(f" {k:<18s} {len(rs):>8d} {n_tot:>8d} " + f"{wtd_a:>10.4f} {wtd_ap:>10.4f}") +EOF + +echo "" +echo "=== $(date): done ===" diff --git a/scripts/auto_transfer_ddos.sh b/scripts/auto_transfer_ddos.sh new file mode 100755 index 0000000..a5f97df --- /dev/null +++ b/scripts/auto_transfer_ddos.sh @@ -0,0 +1,293 @@ +#!/bin/bash +# Unattended cross-dataset transfer eval (v2, with per-shard merge). +# +# Pipeline: +# STAGE 1 : wait for 01-12 re-extraction to finish +# STAGE 2 : merge packets.{01-12,03-11}.npz + flows.{01-12,03-11}.parquet +# → unified packets.npz + flows.parquet +# STAGE 3 : validate unified artifacts +# STAGE 4 : detect + per_class across 4 cells (seed × σ from CICIDS2017 sweep) +# STAGE 5 : summary table + merged-label view +# +# Log: runs/transfer_ddos/run.log +set -uo pipefail + +ROOT=/home/chy/mambafortrafficmodeling +cd "$ROOT" + +SRC_SWEEP="runs/n10k_refactor_20260422_220351" +DST="runs/transfer_ddos" +LOG="$DST/run.log" +DDOS_DIR="datasets/cicddos2019/processed" +mkdir -p "$DST" +exec > >(tee -a "$LOG") 2>&1 + +N_VAL=20000 +N_ATK=100000 +SPLIT_SEED=42 + +echo "=== $(date): script starts (v2 with merge) ===" +echo "source sweep : $SRC_SWEEP" +echo "destination : $DST" +echo "scoring : n_val=$N_VAL n_atk=$N_ATK split_seed=$SPLIT_SEED" + +# ===================================================================== +# STAGE 1: wait for extraction_cicddos2019 (01-12 shard) to finish +# ===================================================================== +echo "" +echo "=== $(date): STAGE 1 — waiting for 01-12 re-extraction ===" +elapsed=0 +while pgrep -f "scripts/extract_cicddos2019" > /dev/null; do + sleep 60 + elapsed=$((elapsed + 60)) + if (( elapsed % 600 == 0 )); then + # Heartbeat every 10 min + rss=$(pgrep -af "scripts/extract_cicddos2019" | head -1 | awk '{print $1}' | xargs -I{} ps -p {} -o rss= 2>/dev/null || echo "?") + echo "[heartbeat $(date +%H:%M:%S)] 01-12 extraction running, waited ${elapsed}s rss(parent)=${rss} kB" + fi +done +echo "=== $(date): extraction process exited after ${elapsed}s wait ===" +sleep 15 + +# ===================================================================== +# STAGE 2: merge per-shard artifacts +# ===================================================================== +echo "" +echo "=== $(date): STAGE 2 — merge shards ===" + +SHARDS_PACKETS="$DDOS_DIR/packets.01-12.npz $DDOS_DIR/packets.03-11.npz" +SHARDS_FLOWS="$DDOS_DIR/flows.01-12.parquet $DDOS_DIR/flows.03-11.parquet" + +missing=0 +for f in $SHARDS_PACKETS $SHARDS_FLOWS; do + if [[ ! -f "$f" ]]; then + echo "ERROR: shard artifact missing: $f" + missing=1 + fi +done +if (( missing )); then + echo "--- tail of 01-12 extraction log ---" + tail -40 runs/extract_logs/extract_ddos_0112.log 2>&1 || true + exit 1 +fi + +echo "all 4 shard artifacts present; running merge" +if ! uv run python scripts/merge_cicddos_shards.py 2>&1; then + echo "ERROR: merge failed" + exit 2 +fi + +# ===================================================================== +# STAGE 3: validate unified artifacts +# ===================================================================== +echo "" +echo "=== $(date): STAGE 3 — validate unified artifacts ===" +PACKETS="$DDOS_DIR/packets.npz" +FLOWS="$DDOS_DIR/flows.parquet" +if [[ ! -f "$PACKETS" || ! -f "$FLOWS" ]]; then + echo "ERROR: merge output missing" + echo " $PACKETS : $([[ -f $PACKETS ]] && echo OK || echo MISSING)" + echo " $FLOWS : $([[ -f $FLOWS ]] && echo OK || echo MISSING)" + exit 3 +fi +ls -lh "$PACKETS" "$FLOWS" + +uv run python - <<'EOF' +import numpy as np, pandas as pd, sys +try: + p = np.load('datasets/cicddos2019/processed/packets.npz') + f = pd.read_parquet('datasets/cicddos2019/processed/flows.parquet') + assert set(p.files) == {'packet_tokens', 'packet_lengths', 'flow_id'}, p.files + assert set(f.columns) == {'flow_id', 'label'}, f.columns + assert p['flow_id'].shape[0] == len(f) + assert np.array_equal(p['flow_id'], f['flow_id'].to_numpy()) + n = len(f) + n_benign = int((f['label'] == 'normal').sum()) + print(f'[validate] OK: N={n:,} benign={n_benign:,} attack={n-n_benign:,}') + print(f'[validate] packet_tokens shape/dtype: {p["packet_tokens"].shape} {p["packet_tokens"].dtype}') + print('[validate] label value_counts (top 20):') + print(f['label'].value_counts().head(20).to_string()) +except Exception as e: + print(f'[validate] FAILED: {type(e).__name__}: {e}') + sys.exit(2) +EOF +if [[ $? -ne 0 ]]; then + echo "ERROR: unified artifact validation failed" + exit 4 +fi + +# ===================================================================== +# STAGE 4: transfer detect + per_class +# ===================================================================== +echo "" +echo "=== $(date): STAGE 4 — transfer detect + per_class (4 cells) ===" +CELLS=( "seed42/s0" "seed42/s0.6" "seed43/s0" "seed43/s0.6" ) + +run_cell() { + local cell=$1 + local src_dir="$SRC_SWEEP/$cell" + local out_dir="$DST/$cell" + + if [[ ! -f "$src_dir/model.pt" ]]; then + echo "SKIP $cell : no model.pt at $src_dir" + return 1 + fi + + mkdir -p "$out_dir" + cp "$src_dir/model.pt" "$out_dir/model.pt" + [[ -f "$src_dir/history.json" ]] && cp "$src_dir/history.json" "$out_dir/" + + echo "" + echo "----- [$cell] $(date) -----" + echo "[detect] starting" + if ! uv run python -m detect \ + --save-dir "$out_dir" \ + --packets-npz "$PACKETS" \ + --flows-parquet "$FLOWS" \ + --n-val "$N_VAL" --n-atk "$N_ATK" \ + --seed "$SPLIT_SEED" \ + 2>&1 | tail -25 + then + echo "ERROR: detect failed for $cell" + return 2 + fi + + if [[ ! -f "$out_dir/scores.npz" ]]; then + echo "ERROR: detect produced no scores.npz for $cell" + return 3 + fi + + echo "[per_class] starting" + if ! uv run python -m eval.per_class --save-dir "$out_dir" 2>&1 | tail -80 + then + echo "ERROR: per_class failed for $cell" + return 4 + fi + + if [[ ! -f "$out_dir/per_class.json" ]]; then + echo "ERROR: per_class.json missing for $cell" + return 5 + fi + + echo "[$cell] OK" + return 0 +} + +OK_CELLS=() +FAIL_CELLS=() +for cell in "${CELLS[@]}"; do + if run_cell "$cell"; then + OK_CELLS+=("$cell") + else + FAIL_CELLS+=("$cell") + echo "[$cell] continuing despite failure" + fi +done + +echo "" +echo "=== per-cell status ===" +echo "OK (${#OK_CELLS[@]}): ${OK_CELLS[*]:-none}" +echo "FAIL (${#FAIL_CELLS[@]}): ${FAIL_CELLS[*]:-none}" + +# ===================================================================== +# STAGE 5: summary +# ===================================================================== +echo "" +echo "=== $(date): STAGE 5 — summary ===" + +uv run python - "$DST" <<'EOF' +import json, sys +from pathlib import Path +import numpy as np + +dst = Path(sys.argv[1]) +cells = ["seed42/s0", "seed42/s0.6", "seed43/s0", "seed43/s0.6"] + +ch = "terminal_norm" +keys = [("overall_auroc", "overall AUROC"), + ("overall_auprc", "overall AUPRC"), + ("macro_auroc", "macro AUROC"), + ("macro_auprc", "macro AUPRC"), + ("tpr_at_1fpr", "TPR@1%FPR"), + ("fpr_at_95tpr", "FPR@95%TPR")] + +header = f"{'cell':<15s}" + "".join(f" {t:>14s}" for _, t in keys) + f" {'flipped':>8s}" +print(header) +print("-" * len(header)) + +loaded: dict[str, dict] = {} +for c in cells: + jp = dst / c / "per_class.json" + if not jp.exists(): + print(f"{c:<15s} (missing per_class.json)") + continue + try: + tn = json.loads(jp.read_text())[ch] + except Exception as e: + print(f"{c:<15s} (parse error: {e})") + continue + loaded[c] = tn + row = [tn[k] for k, _ in keys] + line = f"{c:<15s}" + for v in row: + if isinstance(v, float) and (v != v): + line += f" {'NaN':>14s}" + else: + line += f" {v:>14.4f}" + line += f" {str(tn['flipped']):>8s}" + print(line) + +if not loaded: + print("\n(no cells loaded — nothing to aggregate)") + sys.exit(0) + +print("") +print("=== mean ± std across seeds (per σ) ===") +for sg in ["0", "0.6"]: + pair = [(c, d) for c, d in loaded.items() if c.endswith(f"/s{sg}")] + if len(pair) < 2: + print(f"σ={sg}: only {len(pair)} seed(s), skip aggregate") + continue + print(f"\n--- σ={sg} ({len(pair)} seeds) ---") + for k, t in keys: + vals = [d[k] for _, d in pair if isinstance(d[k], float) and d[k] == d[k]] + if not vals: + continue + m = float(np.mean(vals)); s = float(np.std(vals, ddof=0)) + print(f" {t:<18s} {m:.4f} ± {s:.4f}") + +ref = loaded.get("seed42/s0.6") or next(iter(loaded.values())) +print("") +print("=== per-class AUROC (seed42/s0.6 reference) ===") +pc = ref["per_class"] +print(f"{'class':<30s} {'n':>8s} {'auroc':>8s} {'auprc':>8s} {'tpr@1%':>8s}") +print("-" * 70) +for r in pc: + fmt = lambda v: "—" if (isinstance(v, float) and v != v) else f"{v:.4f}" + print(f" {r['class']:<28s} {r['n']:>8d} " + f"{fmt(r['auroc']):>8s} {fmt(r['auprc']):>8s} {fmt(r['tpr_at_1fpr']):>8s}") + +print("") +print("=== per-class after label merge (DrDoS_* → stripped) ===") +def norm(name): + if name.startswith("DrDoS_"): + return name[len("DrDoS_"):] + return name +buckets: dict[str, list] = {} +for r in pc: + if isinstance(r["auroc"], float) and r["auroc"] == r["auroc"]: + buckets.setdefault(norm(r["class"]), []).append(r) +if buckets: + print(f"{'merged class':<20s} {'shards':>6s} {'n_total':>8s} " + f"{'auroc_wtd':>10s} {'auroc_mean':>10s}") + print("-" * 64) + for k, rs in sorted(buckets.items(), key=lambda x: -sum(r["n"] for r in x[1])): + n_tot = sum(r["n"] for r in rs) + wtd = sum(r["auroc"] * r["n"] for r in rs) / max(n_tot, 1) + mean = sum(r["auroc"] for r in rs) / len(rs) + print(f" {k:<18s} {len(rs):>6d} {n_tot:>8d} " + f"{wtd:>10.4f} {mean:>10.4f}") +EOF + +echo "" +echo "=== $(date): script done ===" diff --git a/scripts/baselines/aggregate_anomaly_transformer.py b/scripts/baselines/aggregate_anomaly_transformer.py new file mode 100644 index 0000000..7d10866 --- /dev/null +++ b/scripts/baselines/aggregate_anomaly_transformer.py @@ -0,0 +1,106 @@ +from __future__ import annotations +import json +from pathlib import Path +import numpy as np +REPO = Path(__file__).resolve().parents[2] +ROOT = REPO / 'artifacts/baselines/anomaly_transformer_2026_04_29' +PROTOCOLS = ('iscxtor_within', 'cicids_within', 'cicddos_within', 'forward_cross', 'reverse_cross') +SEEDS = (42, 43, 44) +AGGS = ('mean', 'max', 'median', 'p90') +TERMINAL_NORM = {'iscxtor_within': (0.9945, 0.0011), 'cicids_within': (0.9858, 0.0021), 'cicddos_within': (0.996, 0.001), 'forward_cross': (0.9109, 0.0032), 'reverse_cross': (0.5999, None)} +PRETTY = {'iscxtor_within': 'ISCXTor2016 within', 'cicids_within': 'CICIDS2017 within (σ=0.6)', 'cicddos_within': 'CICDDoS2019 within', 'forward_cross': 'IDS2017→DDoS2019 forward', 'reverse_cross': 'DDoS2019→IDS2017 reverse'} + +def _load(protocol, seed): + p = ROOT / f'{protocol}_seed{seed}.json' + if not p.exists(): + return None + return json.loads(p.read_text()) + +def _ms(vals): + arr = np.asarray([v for v in vals if v is not None and (not np.isnan(v))], dtype=np.float64) + if len(arr) == 0: + return (float('nan'), float('nan')) + return (float(arr.mean()), float(arr.std(ddof=1)) if len(arr) > 1 else 0.0) + +def _abs_auroc(v): + return max(v, 1.0 - v) + +def main(): + rows = [] + full = {'protocols': {}} + per_class_collect = {p: {} for p in PROTOCOLS} + for protocol in PROTOCOLS: + agg_aurocs = {agg: [] for agg in AGGS} + agg_abs_aurocs = {agg: [] for agg in AGGS} + seeds_run = [] + for s in SEEDS: + r = _load(protocol, s) + if r is None: + continue + seeds_run.append(s) + for agg in AGGS: + ov = r['overall_by_agg'][agg] + agg_aurocs[agg].append(ov['auroc']) + agg_abs_aurocs[agg].append(_abs_auroc(ov['auroc'])) + for (cls, info) in r.get('per_class_by_agg', {}).get('mean', {}).items(): + per_class_collect[protocol].setdefault(cls, {'n': int(info['_n']), 'aurocs': []}) + per_class_collect[protocol][cls]['aurocs'].append(info['auroc']) + agg_summary = {} + for agg in AGGS: + (m, sd) = _ms(agg_aurocs[agg]) + (am, asd) = _ms(agg_abs_aurocs[agg]) + agg_summary[agg] = {'auroc_mean': m, 'auroc_std': sd, 'abs_auroc_mean': am, 'abs_auroc_std': asd} + full['protocols'][protocol] = {'seeds': seeds_run, 'by_agg': agg_summary} + best_agg = max(agg_summary, key=lambda a: agg_summary[a]['abs_auroc_mean']) + rows.append({'protocol': protocol, 'n_seeds': len(seeds_run), 'best_agg': best_agg, 'auroc_mean': agg_summary[best_agg]['auroc_mean'], 'auroc_std': agg_summary[best_agg]['auroc_std'], 'abs_auroc_mean': agg_summary[best_agg]['abs_auroc_mean'], 'abs_auroc_std': agg_summary[best_agg]['abs_auroc_std'], 'all_aggs': agg_summary}) + lines = ['# Anomaly-Transformer (ICLR 2022) Baseline — On Our 5-Protocol Layout', '', 'Date: 2026-04-29', '', 'Method: ICLR 2022 Anomaly-Transformer (association-discrepancy minimax). Vendored model class from `baselines/Anomaly-Transformer/model/AnomalyTransformer.py`; training + scoring loop reimplemented to match our protocol (input shape [B, T=64, D=9] = our z-scored packet sequences, same train/val/attack splits as eval_new_scores.py).', 'Hyperparams: d_model=128, n_heads=4, e_layers=3, batch=128, lr=1e-4, k_disc=3.0, temperature=50.0, epochs=15.', 'Score: per-position softmax(-association_KL · T) · MSE(rec, x), then aggregated per flow (mean / max / median / p90).', '', '## Headline AUROC (best aggregator per protocol, 3-seed mean ± std)', '', '| Protocol | terminal_norm (Unified_CFM) | **AT (ours)** | abs AUROC | best agg | Δ vs terminal |', '|---|---:|---:|---:|---|---:|'] + for row in rows: + p = row['protocol'] + (tn_m, tn_sd) = TERMINAL_NORM[p] + (m, sd) = (row['auroc_mean'], row['auroc_std']) + (am, asd) = (row['abs_auroc_mean'], row['abs_auroc_std']) + if np.isnan(m): + continue + tn_str = f'{tn_m:.4f} ± {tn_sd:.4f}' if tn_sd is not None else f'{tn_m:.4f}' + d_terminal = m - tn_m + lines.append(f"| {PRETTY[p]} | {tn_str} | **{m:.4f} ± {sd:.4f}** | {am:.4f} ± {asd:.4f} | `{row['best_agg']}` | {d_terminal:+.4f} |") + lines.append('') + lines.append('## All aggregators (3-seed mean ± std)') + lines.append('') + lines.append('| Protocol | mean | max | median | p90 |') + lines.append('|---|---:|---:|---:|---:|') + for row in rows: + cells = [PRETTY[row['protocol']]] + for agg in AGGS: + a = row['all_aggs'][agg] + m = a['auroc_mean'] + if np.isnan(m): + cells.append('—') + else: + cells.append(f"{m:.4f} ± {a['auroc_std']:.4f}") + lines.append('| ' + ' | '.join(cells) + ' |') + lines.append('') + lines.append('## Per-attack (forward + reverse, mean aggregator)') + for protocol in ('forward_cross', 'reverse_cross'): + lines.append(f'\n### {PRETTY[protocol]}') + d = per_class_collect[protocol] + if not d: + continue + lines.append('| attack | n | AT AUROC mean ± std |') + lines.append('|---|---:|---:|') + for cls in sorted(d): + n = d[cls]['n'] + (m, sd) = _ms(d[cls]['aurocs']) + lines.append(f'| `{cls}` | {n} | {m:.4f} ± {sd:.4f} |') + out = ROOT / 'summary.md' + out.write_text('\n'.join(lines)) + summary_json = {'rows': rows, 'per_class': per_class_collect, 'baselines': {'terminal_norm': TERMINAL_NORM}} + (ROOT / 'summary.json').write_text(json.dumps(summary_json, indent=2)) + print(f'[saved] {out}') + print(f"[saved] {ROOT / 'summary.json'}") + print() + for row in rows: + if not np.isnan(row['auroc_mean']): + print(f" {PRETTY[row['protocol']]:<34s} best={row['best_agg']:<6s} raw={row['auroc_mean']:.4f}±{row['auroc_std']:.4f} abs={row['abs_auroc_mean']:.4f}") +if __name__ == '__main__': + main() diff --git a/scripts/baselines/aggregate_kitsune.py b/scripts/baselines/aggregate_kitsune.py new file mode 100644 index 0000000..32fdfea --- /dev/null +++ b/scripts/baselines/aggregate_kitsune.py @@ -0,0 +1,109 @@ +from __future__ import annotations +import json +from pathlib import Path +import numpy as np +REPO = Path(__file__).resolve().parents[2] +ROOT = REPO / 'artifacts/baselines/kitsune_2026_04_29' +PROTOCOLS = ('iscxtor_within', 'cicids_within', 'cicddos_within', 'forward_cross', 'reverse_cross') +SEEDS = (42, 43, 44) +AGGS = ('mean', 'max', 'median', 'p90') +TERMINAL_NORM = {'iscxtor_within': (0.9945, 0.0011), 'cicids_within': (0.9858, 0.0021), 'cicddos_within': (0.996, 0.001), 'forward_cross': (0.9109, 0.0032), 'reverse_cross': (0.5999, None)} +KITSUNE_PAPER = {'iscxtor_within': (0.78, None), 'cicids_within': (0.85, None), 'cicddos_within': (None, None), 'forward_cross': (None, None), 'reverse_cross': (None, None)} +PRETTY = {'iscxtor_within': 'ISCXTor2016 within', 'cicids_within': 'CICIDS2017 within (σ=0.6)', 'cicddos_within': 'CICDDoS2019 within', 'forward_cross': 'IDS2017→DDoS2019 forward', 'reverse_cross': 'DDoS2019→IDS2017 reverse'} + +def _load(protocol, seed): + p = ROOT / f'{protocol}_seed{seed}.json' + if not p.exists(): + return None + return json.loads(p.read_text()) + +def _ms(vals): + arr = np.asarray([v for v in vals if v is not None and (not np.isnan(v))], dtype=np.float64) + if len(arr) == 0: + return (float('nan'), float('nan')) + return (float(arr.mean()), float(arr.std(ddof=1)) if len(arr) > 1 else 0.0) + +def main(): + rows = [] + per_class_collect = {p: {} for p in PROTOCOLS} + full = {'protocols': {}} + for protocol in PROTOCOLS: + agg_aurocs = {agg: [] for agg in AGGS} + agg_auprcs = {agg: [] for agg in AGGS} + seeds_run = [] + for s in SEEDS: + r = _load(protocol, s) + if r is None: + continue + seeds_run.append(s) + for agg in AGGS: + ov = r['overall_by_agg'][agg] + agg_aurocs[agg].append(ov['auroc']) + agg_auprcs[agg].append(ov['auprc']) + for (cls, info) in r.get('per_class_by_agg', {}).get('mean', {}).items(): + per_class_collect[protocol].setdefault(cls, {'n': int(info['_n']), 'aurocs': []}) + per_class_collect[protocol][cls]['aurocs'].append(info['auroc']) + agg_summary = {} + for agg in AGGS: + (m, sd) = _ms(agg_aurocs[agg]) + (ma, sda) = _ms(agg_auprcs[agg]) + agg_summary[agg] = {'auroc_mean': m, 'auroc_std': sd, 'auprc_mean': ma, 'auprc_std': sda} + full['protocols'][protocol] = {'seeds': seeds_run, 'by_agg': agg_summary} + best_agg = max(agg_summary, key=lambda a: agg_summary[a]['auroc_mean']) + rows.append({'protocol': protocol, 'n_seeds': len(seeds_run), 'best_agg': best_agg, 'auroc_mean': agg_summary[best_agg]['auroc_mean'], 'auroc_std': agg_summary[best_agg]['auroc_std'], 'all_aggs': agg_summary}) + lines = ['# Kitsune (Path B) Baseline — On Our 5-Protocol Layout', '', 'Date: 2026-04-29', '', 'Method: KitNET ensemble autoencoder (the ML core of Kitsune).', "**Path B**: feeds our **z-scored 9-d packet features** directly through `KitNET.process()` for the FM+AD grace, then `KitNET.execute()` per packet during eval. **AfterImage's 100-d host/session statistics are skipped** (they require sequential pcap streams which our (B,T,9) tensor abstraction discards). This keeps data usage unified with `eval_new_scores.py`.", 'Train: 5000 source-benign flows → ~75-320k packets (≥ FM+AD=55k grace).', 'Score: per-flow aggregate of per-packet RMSE (mean / max / median / p90).', 'Sampling: same seeds & stratification as `eval_new_scores.py`.', '', '## Headline AUROC (best aggregator per protocol, 3-seed mean ± std)', '', '| Protocol | terminal_norm | Kitsune paper (Shafir reproduction) | **Kitsune Path B (ours)** | best agg | Δ vs paper | Δ vs terminal |', '|---|---:|---:|---:|---|---:|---:|'] + for row in rows: + p = row['protocol'] + (tn_m, tn_sd) = TERMINAL_NORM[p] + (kp_m, _) = KITSUNE_PAPER[p] + (m, sd) = (row['auroc_mean'], row['auroc_std']) + if np.isnan(m): + lines.append(f'| {PRETTY[p]} | {tn_m:.4f} | — | (no runs) | — | — | — |') + continue + tn_str = f'{tn_m:.4f} ± {tn_sd:.4f}' if tn_sd is not None else f'{tn_m:.4f}' + kp_str = f'{kp_m:.4f}' if kp_m is not None else '—' + d_terminal = m - tn_m + d_paper = m - kp_m if kp_m is not None else None + d_paper_str = f'{d_paper:+.4f}' if d_paper is not None else '—' + lines.append(f"| {PRETTY[p]} | {tn_str} | {kp_str} | **{m:.4f} ± {sd:.4f}** | `{row['best_agg']}` | {d_paper_str} | {d_terminal:+.4f} |") + lines.append('') + lines.append('## All aggregators (3-seed mean ± std)') + lines.append('') + lines.append('| Protocol | mean | max | median | p90 |') + lines.append('|---|---:|---:|---:|---:|') + for row in rows: + cells = [PRETTY[row['protocol']]] + for agg in AGGS: + a = row['all_aggs'][agg] + m = a['auroc_mean'] + if np.isnan(m): + cells.append('—') + else: + cells.append(f"{m:.4f} ± {a['auroc_std']:.4f}") + lines.append('| ' + ' | '.join(cells) + ' |') + lines.append('') + lines.append('## Per-attack (forward + reverse, mean aggregator)') + for protocol in ('forward_cross', 'reverse_cross'): + lines.append(f'\n### {PRETTY[protocol]}') + d = per_class_collect[protocol] + if not d: + lines.append('(no runs)') + continue + lines.append('| attack | n | Kitsune AUROC mean ± std |') + lines.append('|---|---:|---:|') + for cls in sorted(d): + n = d[cls]['n'] + (m, sd) = _ms(d[cls]['aurocs']) + lines.append(f'| `{cls}` | {n} | {m:.4f} ± {sd:.4f} |') + out = ROOT / 'summary.md' + out.write_text('\n'.join(lines)) + summary_json = {'rows': rows, 'per_class': per_class_collect, 'baselines': {'terminal_norm': TERMINAL_NORM, 'kitsune_paper': KITSUNE_PAPER}} + (ROOT / 'summary.json').write_text(json.dumps(summary_json, indent=2)) + print(f'[saved] {out}') + print(f"[saved] {ROOT / 'summary.json'}") + print() + for row in rows: + if not np.isnan(row['auroc_mean']): + print(f" {PRETTY[row['protocol']]:<34s} best={row['best_agg']:<6s} {row['auroc_mean']:.4f} ± {row['auroc_std']:.4f}") +if __name__ == '__main__': + main() diff --git a/scripts/baselines/aggregate_shafir_nf.py b/scripts/baselines/aggregate_shafir_nf.py new file mode 100644 index 0000000..61b216b --- /dev/null +++ b/scripts/baselines/aggregate_shafir_nf.py @@ -0,0 +1,93 @@ +from __future__ import annotations +import json +from pathlib import Path +import numpy as np +REPO = Path(__file__).resolve().parents[2] +ROOT = REPO / 'artifacts/baselines/shafir_nf_2026_04_29' +PROTOCOLS = ('iscxtor_within', 'cicids_within', 'cicddos_within', 'forward_cross', 'reverse_cross') +SEEDS = (42, 43, 44) +TERMINAL_NORM = {'iscxtor_within': (0.9945, 0.0011), 'cicids_within': (0.9858, 0.0021), 'cicddos_within': (0.996, 0.001), 'forward_cross': (0.9109, 0.0032), 'reverse_cross': (0.5999, None)} +SHAFIR_PAPER = {'iscxtor_within': (0.8731, None), 'cicids_within': (0.9303, None), 'cicddos_within': (0.93, None), 'forward_cross': (0.89, None), 'reverse_cross': (0.93, None)} +PRETTY = {'iscxtor_within': 'ISCXTor2016 within', 'cicids_within': 'CICIDS2017 within (σ=0.6)', 'cicddos_within': 'CICDDoS2019 within', 'forward_cross': 'IDS2017→DDoS2019 forward', 'reverse_cross': 'DDoS2019→IDS2017 reverse'} + +def _load(protocol, seed): + p = ROOT / f'{protocol}_seed{seed}.json' + if not p.exists(): + return None + return json.loads(p.read_text()) + +def _ms(vals): + arr = np.asarray([v for v in vals if v is not None and (not np.isnan(v))], dtype=np.float64) + if len(arr) == 0: + return (float('nan'), float('nan')) + return (float(arr.mean()), float(arr.std(ddof=1)) if len(arr) > 1 else 0.0) + +def main(): + rows = [] + per_class_collect = {p: {} for p in PROTOCOLS} + for protocol in PROTOCOLS: + (aurocs, auprcs, t_train) = ([], [], []) + for s in SEEDS: + r = _load(protocol, s) + if r is None: + continue + aurocs.append(r['overall']['neg_log_prob']['auroc']) + auprcs.append(r['overall']['neg_log_prob']['auprc']) + t_train.append(r.get('t_train_sec', 0.0)) + for (cls, info) in r.get('per_class', {}).items(): + per_class_collect[protocol].setdefault(cls, {'n': int(info['_n']), 'aurocs': []}) + per_class_collect[protocol][cls]['aurocs'].append(info['auroc']) + (m, sd) = _ms(aurocs) + (ma, sda) = _ms(auprcs) + (tt, _) = _ms(t_train) + rows.append({'protocol': protocol, 'n_seeds': len(aurocs), 'auroc_mean': m, 'auroc_std': sd, 'auprc_mean': ma, 'auprc_std': sda, 't_train_sec_mean': tt}) + lines = ['# Shafir 2026 NF Baseline — On Our 5-Protocol Layout', '', 'Date: 2026-04-29', '', "Method: Shafir's official `pzflow.Flow` (single basic NF).", 'Features: our **20-d canonical packet-derived flow features** (`common.data_contract.CANONICAL_FLOW_FEATURE_NAMES`), z-scored with the **same source training stats** that the Unified_CFM checkpoint uses.', 'Train cap: 10,000 source-benign samples (Shafir paper protocol).', 'Optimizer: SGD lr=1e-3, 100 epochs (Shafir paper defaults).', 'Sampling: same seeds & stratification as `eval_new_scores.py`.', '', '## Headline AUROC (3-seed mean ± std)', '', '| Protocol | terminal_norm (ours) | Shafir NF — paper | **Shafir NF — our features** | Δ vs paper | Δ vs terminal_norm |', '|---|---:|---:|---:|---:|---:|'] + for row in rows: + p = row['protocol'] + (tn_m, tn_sd) = TERMINAL_NORM[p] + (sp_m, _) = SHAFIR_PAPER[p] + (m, sd) = (row['auroc_mean'], row['auroc_std']) + if np.isnan(m): + lines.append(f'| {PRETTY[p]} | {tn_m:.4f} | {sp_m:.4f} | (no runs yet) | — | — |') + continue + d_paper = m - sp_m + d_terminal = m - tn_m + tn_str = f'{tn_m:.4f} ± {tn_sd:.4f}' if tn_sd is not None else f'{tn_m:.4f}' + lines.append(f'| {PRETTY[p]} | {tn_str} | {sp_m:.4f} | **{m:.4f} ± {sd:.4f}** | {d_paper:+.4f} | {d_terminal:+.4f} |') + lines.append('') + lines.append('## Per-protocol stats') + lines.append('') + lines.append('| Protocol | n_seeds | AUPRC mean ± std | Train time (s, mean) |') + lines.append('|---|---:|---:|---:|') + for row in rows: + p = row['protocol'] + (m, sd) = (row['auprc_mean'], row['auprc_std']) + if np.isnan(m): + continue + lines.append(f"| {PRETTY[p]} | {row['n_seeds']} | {m:.4f} ± {sd:.4f} | {row['t_train_sec_mean']:.1f} |") + lines.append('') + lines.append('## Per-attack (forward + reverse)') + for protocol in ('forward_cross', 'reverse_cross'): + lines.append(f'\n### {PRETTY[protocol]}') + d = per_class_collect[protocol] + if not d: + lines.append('(no runs)') + continue + lines.append('| attack | n | Shafir NF AUROC mean ± std |') + lines.append('|---|---:|---:|') + for cls in sorted(d): + n = d[cls]['n'] + (m, sd) = _ms(d[cls]['aurocs']) + lines.append(f'| `{cls}` | {n} | {m:.4f} ± {sd:.4f} |') + out = ROOT / 'summary.md' + out.write_text('\n'.join(lines)) + summary_json = {'rows': rows, 'per_class': {p: {cls: {'n': v['n'], **dict(zip(['mean', 'std'], _ms(v['aurocs'])))} for (cls, v) in dd.items()} for (p, dd) in per_class_collect.items()}, 'baselines': {'terminal_norm': TERMINAL_NORM, 'shafir_paper': SHAFIR_PAPER}} + (ROOT / 'summary.json').write_text(json.dumps(summary_json, indent=2)) + print(f'[saved] {out}') + print(f"[saved] {ROOT / 'summary.json'}") + print() + for row in rows: + if not np.isnan(row['auroc_mean']): + print(f" {PRETTY[row['protocol']]:<34s} {row['auroc_mean']:.4f} ± {row['auroc_std']:.4f}") +if __name__ == '__main__': + main() diff --git a/scripts/baselines/run_anomaly_transformer.py b/scripts/baselines/run_anomaly_transformer.py new file mode 100644 index 0000000..abeb6fa --- /dev/null +++ b/scripts/baselines/run_anomaly_transformer.py @@ -0,0 +1,267 @@ +from __future__ import annotations +import argparse +import json +import sys +import time +from pathlib import Path +from typing import Any +import numpy as np +import pandas as pd +import torch +import torch.nn as nn +import yaml +from sklearn.metrics import average_precision_score, roc_auc_score +REPO = Path(__file__).resolve().parents[2] +sys.path.insert(0, str(REPO / 'Packet_CFM')) +sys.path.insert(0, str(REPO / 'Unified_CFM')) +sys.path.insert(0, str(REPO / 'baselines/Anomaly-Transformer')) +from data import _apply_mixed_dequant, _zscore, load_unified_data +from packet_store import PacketShardStore +from model.AnomalyTransformer import AnomalyTransformer +WITHIN_DIRS = {'iscxtor_within': ('phase25_multiseed_2026_04_25/iscxtor2016_lambda0p3_seed{seed}', {'n_val': 10000, 'n_atk': None}), 'cicids_within': ('phase25_sigma06_multiseed_2026_04_25/cicids2017_lambda0p3_sigma0p6_seed{seed}', {'n_val': 10000, 'n_atk': 30000}), 'cicddos_within': ('phase25_multiseed_2026_04_25/cicddos2019_lambda0p3_seed{seed}', {'n_val': 10000, 'n_atk': 20000})} +CROSS_DIRS = {'forward_cross': {'model_template': 'phase25_sigma06_multiseed_2026_04_25/cicids2017_lambda0p3_sigma0p6_seed{seed}', 'target_store': 'datasets/cicddos2019/processed/full_store', 'target_flows': 'datasets/cicddos2019/processed/flows.parquet', 'n_benign': 10000, 'n_attack': 10000}, 'reverse_cross': {'model_template': 'phase25_multiseed_2026_04_25/cicddos2019_lambda0p3_seed{seed}', 'target_store': 'datasets/cicids2017/processed/full_store', 'target_flows': 'datasets/cicids2017/processed/flows.parquet', 'n_benign': 10000, 'n_attack': 10000}} + +def _load_within(model_dir, n_val, n_atk, n_train_cap, seed): + cfg = yaml.safe_load((model_dir / 'config.yaml').read_text()) + data = load_unified_data(packets_npz=Path(cfg['packets_npz']) if cfg.get('packets_npz') else None, source_store=Path(cfg['source_store']) if cfg.get('source_store') else None, flows_parquet=Path(cfg['flows_parquet']), flow_features_path=Path(cfg['flow_features_path']) if cfg.get('flow_features_path') else None, flow_feature_columns=cfg.get('flow_feature_columns'), flow_features_align=str(cfg.get('flow_features_align', 'auto')), T=int(cfg['T']), split_seed=int(cfg.get('data_seed', cfg.get('seed', 42))), train_ratio=float(cfg.get('train_ratio', 0.8)), benign_label=str(cfg.get('benign_label', 'normal')), min_len=int(cfg.get('min_len', 2)), packet_preprocess=str(cfg.get('packet_preprocess', 'mixed_dequant')), attack_cap=int(cfg['attack_cap']) if cfg.get('attack_cap') else n_atk, val_cap=int(cfg['val_cap']) if cfg.get('val_cap') else n_val) + rng = np.random.default_rng(seed) + (train_packets, train_len) = (data.train_packets, data.train_len) + if len(train_packets) > n_train_cap: + idx = np.sort(rng.choice(len(train_packets), size=n_train_cap, replace=False)) + (train_packets, train_len) = (train_packets[idx], train_len[idx]) + (val_packets, val_len) = (data.val_packets, data.val_len) + (atk_packets, atk_len, atk_labels) = (data.attack_packets, data.attack_len, data.attack_labels) + if n_val is not None and len(val_packets) > n_val: + idx = np.sort(rng.choice(len(val_packets), size=n_val, replace=False)) + (val_packets, val_len) = (val_packets[idx], val_len[idx]) + if n_atk is not None and len(atk_packets) > n_atk: + idx = np.sort(rng.choice(len(atk_packets), size=n_atk, replace=False)) + (atk_packets, atk_len, atk_labels) = (atk_packets[idx], atk_len[idx], atk_labels[idx]) + return {'train_packets': train_packets, 'train_len': train_len, 'val_packets': val_packets, 'val_len': val_len, 'atk_packets': atk_packets, 'atk_len': atk_len, 'atk_labels': atk_labels} + +def _load_cross(spec, ckpt, seed, n_train_cap, T): + packet_mean = np.asarray(ckpt['packet_mean'], dtype=np.float32) + packet_std = np.asarray(ckpt['packet_std'], dtype=np.float32) + packet_preprocess = str(ckpt.get('packet_preprocess', 'mixed_dequant')) + src_cfg_path = REPO / 'artifacts' / spec['model_template'].format(seed=seed) / 'config.yaml' + src_cfg = yaml.safe_load(src_cfg_path.read_text()) + src_data = load_unified_data(packets_npz=Path(src_cfg['packets_npz']) if src_cfg.get('packets_npz') else None, source_store=Path(src_cfg['source_store']) if src_cfg.get('source_store') else None, flows_parquet=Path(src_cfg['flows_parquet']), flow_features_path=Path(src_cfg['flow_features_path']) if src_cfg.get('flow_features_path') else None, flow_feature_columns=src_cfg.get('flow_feature_columns'), flow_features_align=str(src_cfg.get('flow_features_align', 'auto')), T=int(src_cfg['T']), split_seed=int(src_cfg.get('data_seed', src_cfg.get('seed', 42))), train_ratio=float(src_cfg.get('train_ratio', 0.8)), benign_label=str(src_cfg.get('benign_label', 'normal')), min_len=int(src_cfg.get('min_len', 2)), packet_preprocess=packet_preprocess, attack_cap=None, val_cap=None) + rng = np.random.default_rng(seed + 1000) + (train_packets, train_len) = (src_data.train_packets, src_data.train_len) + if len(train_packets) > n_train_cap: + idx = np.sort(rng.choice(len(train_packets), size=n_train_cap, replace=False)) + (train_packets, train_len) = (train_packets[idx], train_len[idx]) + target_store = REPO / spec['target_store'] + target_flows = REPO / spec['target_flows'] + (n_benign, n_attack) = (int(spec['n_benign']), int(spec['n_attack'])) + flows = pd.read_parquet(target_flows, columns=['flow_id', 'label']) + labels = flows['label'].astype(str).to_numpy() + rng2 = np.random.default_rng(seed) + benign_idx = np.flatnonzero(labels == 'normal') + attack_idx = np.flatnonzero(labels != 'normal') + b_sel = np.sort(rng2.choice(benign_idx, size=n_benign, replace=False)) + atk_classes = sorted(set(labels[attack_idx])) + per_class = max(1, n_attack // len(atk_classes)) + chunks = [] + for cls in atk_classes: + pool = attack_idx[labels[attack_idx] == cls] + k = min(per_class, len(pool)) + if k: + chunks.append(rng2.choice(pool, size=k, replace=False)) + a_sel = np.sort(np.concatenate(chunks)) + if len(a_sel) > n_attack: + a_sel = np.sort(rng2.choice(a_sel, size=n_attack, replace=False)) + store = PacketShardStore.open(target_store) + + def _materialize(idx): + (tok, ll) = store.read_packets(idx, T=T) + ll = np.minimum(ll, T).astype(np.int32) + return (tok.astype(np.float32), ll) + (b_tok, b_len) = _materialize(b_sel) + (a_tok, a_len) = _materialize(a_sel) + if packet_preprocess == 'mixed_dequant': + val_packets = _apply_mixed_dequant(b_tok, b_len, packet_mean, packet_std, split_tag='val', seed=seed) + atk_packets = _apply_mixed_dequant(a_tok, a_len, packet_mean, packet_std, split_tag='attack', seed=seed) + else: + val_packets = _zscore(b_tok, packet_mean, packet_std) + atk_packets = _zscore(a_tok, packet_mean, packet_std) + msk_b = np.arange(T)[None, :] < b_len[:, None] + msk_a = np.arange(T)[None, :] < a_len[:, None] + val_packets = (val_packets * msk_b[:, :, None]).astype(np.float32) + atk_packets = (atk_packets * msk_a[:, :, None]).astype(np.float32) + return {'train_packets': train_packets, 'train_len': train_len, 'val_packets': val_packets, 'val_len': b_len, 'atk_packets': atk_packets, 'atk_len': a_len, 'atk_labels': labels[a_sel]} + +def _kl(p, q): + return torch.sum(p * (torch.log(p + 0.0001) - torch.log(q + 0.0001)), dim=-1) + +def _norm_prior(prior, win_size: int) -> torch.Tensor: + return prior / torch.unsqueeze(torch.sum(prior, dim=-1), dim=-1).repeat(1, 1, 1, win_size) + +def _train(model: AnomalyTransformer, train_packets: np.ndarray, train_len: np.ndarray, *, batch_size: int, epochs: int, lr: float, k_disc: float, win_size: int, device: torch.device) -> dict: + optimizer = torch.optim.Adam(model.parameters(), lr=lr) + criterion = nn.MSELoss() + n = len(train_packets) + losses_log = [] + t0 = time.time() + for epoch in range(epochs): + model.train() + rng = np.random.default_rng(epoch) + perm = rng.permutation(n) + epoch_losses = [] + for s in range(0, n, batch_size): + idx = perm[s:s + batch_size] + x = torch.from_numpy(train_packets[idx]).float().to(device) + optimizer.zero_grad() + (output, series, prior, _) = model(x) + series_loss = 0.0 + prior_loss = 0.0 + for u in range(len(prior)): + norm_p = _norm_prior(prior[u], win_size) + series_loss += torch.mean(_kl(series[u], norm_p.detach())) + torch.mean(_kl(norm_p.detach(), series[u])) + prior_loss += torch.mean(_kl(norm_p, series[u].detach())) + torch.mean(_kl(series[u].detach(), norm_p)) + series_loss /= len(prior) + prior_loss /= len(prior) + rec_loss = criterion(output, x) + loss1 = rec_loss - k_disc * series_loss + loss2 = rec_loss + k_disc * prior_loss + loss1.backward(retain_graph=True) + loss2.backward() + optimizer.step() + epoch_losses.append(rec_loss.item()) + losses_log.append(float(np.mean(epoch_losses))) + if (epoch + 1) % 5 == 0 or epoch == epochs - 1: + print(f' [epoch {epoch + 1}/{epochs}] rec_loss={losses_log[-1]:.4f} ({time.time() - t0:.1f}s elapsed)', flush=True) + return {'losses': losses_log, 't_train_sec': time.time() - t0} + +@torch.no_grad() +def _score(model: AnomalyTransformer, packets: np.ndarray, lens: np.ndarray, *, batch_size: int, win_size: int, temperature: float, device: torch.device) -> dict[str, np.ndarray]: + model.eval() + n = len(packets) + means = np.zeros(n, dtype=np.float32) + maxes = np.zeros(n, dtype=np.float32) + medians = np.zeros(n, dtype=np.float32) + p90s = np.zeros(n, dtype=np.float32) + crit = nn.MSELoss(reduction='none') + for s in range(0, n, batch_size): + x = torch.from_numpy(packets[s:s + batch_size]).float().to(device) + L = torch.from_numpy(lens[s:s + batch_size]).long().to(device) + (output, series, prior, _) = model(x) + rec = crit(output, x).mean(dim=-1) + series_loss = 0.0 + prior_loss = 0.0 + for u in range(len(prior)): + norm_p = _norm_prior(prior[u], win_size) + kl1 = _kl(series[u], norm_p.detach()) + kl2 = _kl(norm_p.detach(), series[u]) + series_loss = series_loss + (kl1 + kl2) + if isinstance(series_loss, torch.Tensor): + sl = series_loss.mean(dim=1) + metric = torch.softmax(-sl * temperature, dim=-1) * rec + else: + metric = rec + T_eff = x.shape[1] + arange = torch.arange(T_eff, device=device).unsqueeze(0).expand_as(metric) + mask = arange < L.unsqueeze(1) + for i in range(metric.shape[0]): + li = int(L[i].item()) + if li == 0: + continue + row = metric[i, :li].cpu().numpy() + means[s + i] = row.mean() + maxes[s + i] = row.max() + medians[s + i] = float(np.median(row)) + p90s[s + i] = float(np.percentile(row, 90)) + return {'mean': means, 'max': maxes, 'median': medians, 'p90': p90s} + +def _safe_metric(fn, y, s) -> float: + s = np.nan_to_num(s, nan=0.0, posinf=1000000000000.0, neginf=-1000000000000.0) + try: + return float(fn(y, s)) + except ValueError: + return float('nan') + +def _per_class(val_score, atk_score, atk_labels): + out = {} + for cls in sorted(set(atk_labels)): + m = atk_labels == cls + n_c = int(m.sum()) + v_c = atk_score[m] + y = np.r_[np.zeros(len(val_score)), np.ones(len(v_c))] + s = np.r_[val_score, v_c] + out[cls] = {'_n': float(n_c), 'auroc': _safe_metric(roc_auc_score, y, s)} + return out + +def main(): + p = argparse.ArgumentParser() + p.add_argument('--protocol', required=True, choices=list(WITHIN_DIRS) + list(CROSS_DIRS)) + p.add_argument('--seed', type=int, required=True, choices=[42, 43, 44]) + p.add_argument('--out-dir', type=Path, required=True) + p.add_argument('--n-train-cap', type=int, default=10000) + p.add_argument('--epochs', type=int, default=10) + p.add_argument('--lr', type=float, default=0.0001) + p.add_argument('--k-disc', type=float, default=3.0, help='weight on association-discrepancy KL term') + p.add_argument('--temperature', type=float, default=50.0) + p.add_argument('--batch-size', type=int, default=64) + p.add_argument('--d-model', type=int, default=128) + p.add_argument('--n-heads', type=int, default=4) + p.add_argument('--e-layers', type=int, default=3) + p.add_argument('--T', type=int, default=64) + p.add_argument('--device', type=str, default='auto') + args = p.parse_args() + args.out_dir.mkdir(parents=True, exist_ok=True) + device = torch.device('cuda' if args.device == 'auto' and torch.cuda.is_available() else args.device if args.device != 'auto' else 'cpu') + is_within = args.protocol in WITHIN_DIRS + if is_within: + (template, caps) = WITHIN_DIRS[args.protocol] + model_dir = REPO / 'artifacts' / template.format(seed=args.seed) + else: + spec = CROSS_DIRS[args.protocol] + model_dir = REPO / 'artifacts' / spec['model_template'].format(seed=args.seed) + print(f'[run] anomaly_transformer protocol={args.protocol} seed={args.seed}') + ckpt = torch.load(model_dir / 'model.pt', map_location='cpu', weights_only=False) + if is_within: + arrays = _load_within(model_dir, n_val=caps['n_val'], n_atk=caps['n_atk'], n_train_cap=args.n_train_cap, seed=args.seed) + else: + arrays = _load_cross(spec, ckpt, args.seed, args.n_train_cap, args.T) + n_train = len(arrays['train_packets']) + n_val = len(arrays['val_packets']) + n_atk = len(arrays['atk_packets']) + D = arrays['train_packets'].shape[-1] + print(f'[data] train_flows={n_train:,} val={n_val:,} attack={n_atk:,} D={D} device={device}') + torch.manual_seed(args.seed) + model = AnomalyTransformer(win_size=args.T, enc_in=D, c_out=D, d_model=args.d_model, n_heads=args.n_heads, e_layers=args.e_layers, d_ff=args.d_model, dropout=0.0, output_attention=True).to(device) + n_params = sum((p.numel() for p in model.parameters())) + print(f'[model] params={n_params:,}') + train_meta = _train(model, arrays['train_packets'], arrays['train_len'], batch_size=args.batch_size, epochs=args.epochs, lr=args.lr, k_disc=args.k_disc, win_size=args.T, device=device) + print(f"[train] {train_meta['t_train_sec']:.1f}s, final rec_loss={train_meta['losses'][-1]:.4f}") + t0 = time.time() + val_aggs = _score(model, arrays['val_packets'], arrays['val_len'], batch_size=args.batch_size, win_size=args.T, temperature=args.temperature, device=device) + print(f'[score] benign in {time.time() - t0:.1f}s') + t0 = time.time() + atk_aggs = _score(model, arrays['atk_packets'], arrays['atk_len'], batch_size=args.batch_size, win_size=args.T, temperature=args.temperature, device=device) + print(f'[score] attack in {time.time() - t0:.1f}s') + overall = {} + per_class_by_agg = {} + for agg in ('mean', 'max', 'median', 'p90'): + v = val_aggs[agg] + a = atk_aggs[agg] + y = np.r_[np.zeros(len(v)), np.ones(len(a))] + s = np.r_[v, a] + overall[agg] = {'auroc': _safe_metric(roc_auc_score, y, s), 'auprc': _safe_metric(average_precision_score, y, s)} + per_class_by_agg[agg] = _per_class(v, a, np.asarray(arrays['atk_labels']).astype(str)) + out = {'method': 'anomaly_transformer', 'protocol': args.protocol, 'seed': args.seed, 'model_dir': str(model_dir), 'n_train': n_train, 'n_val': n_val, 'n_atk': n_atk, 'D': int(D), 'epochs': args.epochs, 'lr': args.lr, 'k_disc': args.k_disc, 'temperature': args.temperature, 'd_model': args.d_model, 't_train_sec': round(train_meta['t_train_sec'], 2), 'loss_first_last': [train_meta['losses'][0], train_meta['losses'][-1]], 'overall_by_agg': overall, 'per_class_by_agg': per_class_by_agg} + out_json = args.out_dir / f'{args.protocol}_seed{args.seed}.json' + out_json.write_text(json.dumps(out, indent=2)) + npz_path = out_json.with_suffix('.npz') + save = {'a_labels': np.asarray(arrays['atk_labels']).astype(str)} + for agg in ('mean', 'max', 'median', 'p90'): + save[f'b_{agg}'] = val_aggs[agg].astype(np.float32) + save[f'a_{agg}'] = atk_aggs[agg].astype(np.float32) + np.savez_compressed(npz_path, **save) + print(f'[saved] {out_json}') + best = max(overall, key=lambda k: overall[k]['auroc']) + print(f"[best agg={best}] AUROC={overall[best]['auroc']:.4f} AUPRC={overall[best]['auprc']:.4f}") + for k in sorted(overall, key=lambda kk: -overall[kk]['auroc']): + print(f" {k:<8s} AUROC={overall[k]['auroc']:.4f} AUPRC={overall[k]['auprc']:.4f}") +if __name__ == '__main__': + main() diff --git a/scripts/baselines/run_anomaly_transformer_all.sh b/scripts/baselines/run_anomaly_transformer_all.sh new file mode 100755 index 0000000..d25ff2e --- /dev/null +++ b/scripts/baselines/run_anomaly_transformer_all.sh @@ -0,0 +1,37 @@ +#!/usr/bin/env bash +set -euo pipefail +REPO=$(cd "$(dirname "$0")/../.." && pwd) +cd "$REPO" + +OUT_DIR="artifacts/baselines/anomaly_transformer_2026_04_29" +mkdir -p "$OUT_DIR" +LOG="$OUT_DIR/master.log" +: > "$LOG" + +PROTOCOLS_DEFAULT="iscxtor_within cicids_within cicddos_within forward_cross reverse_cross" +SEEDS_DEFAULT="42 43 44" +PROTOCOLS="${PROTOCOLS:-$PROTOCOLS_DEFAULT}" +SEEDS="${SEEDS:-$SEEDS_DEFAULT}" +EPOCHS="${EPOCHS:-15}" +BATCH="${BATCH:-128}" +D_MODEL="${D_MODEL:-128}" + +for protocol in $PROTOCOLS; do + for seed in $SEEDS; do + out_json="$OUT_DIR/${protocol}_seed${seed}.json" + if [[ -f "$out_json" ]]; then + echo "[skip] $out_json exists" | tee -a "$LOG" + continue + fi + echo "=== protocol=$protocol seed=$seed epochs=$EPOCHS batch=$BATCH ===" | tee -a "$LOG" + ts=$(date +%s) + uv run --no-sync python scripts/baselines/run_anomaly_transformer.py \ + --protocol "$protocol" --seed "$seed" \ + --out-dir "$OUT_DIR" \ + --epochs "$EPOCHS" --batch-size "$BATCH" --d-model "$D_MODEL" \ + 2>&1 | tee -a "$LOG" + te=$(date +%s) + echo "[done] elapsed=$((te-ts))s $out_json" | tee -a "$LOG" + done +done +echo "ALL DONE" diff --git a/scripts/baselines/run_kitsune.py b/scripts/baselines/run_kitsune.py new file mode 100644 index 0000000..18e8968 --- /dev/null +++ b/scripts/baselines/run_kitsune.py @@ -0,0 +1,223 @@ +from __future__ import annotations +import argparse +import json +import os +import sys +import time +from pathlib import Path +from typing import Any +import numpy as np +import pandas as pd +import torch +import yaml +from sklearn.metrics import average_precision_score, roc_auc_score +REPO = Path(__file__).resolve().parents[2] +sys.path.insert(0, str(REPO / 'Packet_CFM')) +sys.path.insert(0, str(REPO / 'Unified_CFM')) +sys.path.insert(0, str(REPO / 'baselines/Kitsune-py')) +from data import _apply_mixed_dequant, _zscore, load_unified_data +from packet_store import PacketShardStore +from KitNET.KitNET import KitNET +WITHIN_DIRS = {'iscxtor_within': ('phase25_multiseed_2026_04_25/iscxtor2016_lambda0p3_seed{seed}', {'n_val': 10000, 'n_atk': None}), 'cicids_within': ('phase25_sigma06_multiseed_2026_04_25/cicids2017_lambda0p3_sigma0p6_seed{seed}', {'n_val': 10000, 'n_atk': 30000}), 'cicddos_within': ('phase25_multiseed_2026_04_25/cicddos2019_lambda0p3_seed{seed}', {'n_val': 10000, 'n_atk': 20000})} +CROSS_DIRS = {'forward_cross': {'model_template': 'phase25_sigma06_multiseed_2026_04_25/cicids2017_lambda0p3_sigma0p6_seed{seed}', 'target_store': 'datasets/cicddos2019/processed/full_store', 'target_flows': 'datasets/cicddos2019/processed/flows.parquet', 'target_flow_features': 'datasets/cicddos2019/processed/flow_features.parquet', 'n_benign': 10000, 'n_attack': 10000}, 'reverse_cross': {'model_template': 'phase25_multiseed_2026_04_25/cicddos2019_lambda0p3_seed{seed}', 'target_store': 'datasets/cicids2017/processed/full_store', 'target_flows': 'datasets/cicids2017/processed/flows.parquet', 'target_flow_features': 'datasets/cicids2017/processed/flow_features.parquet', 'n_benign': 10000, 'n_attack': 10000}} + +def _safe_metric(fn, y, s) -> float: + s = np.nan_to_num(s, nan=0.0, posinf=1000000000000.0, neginf=-1000000000000.0) + try: + return float(fn(y, s)) + except ValueError: + return float('nan') + +def _load_within(model_dir: Path, n_val, n_atk, n_train_cap, seed): + cfg = yaml.safe_load((model_dir / 'config.yaml').read_text()) + data = load_unified_data(packets_npz=Path(cfg['packets_npz']) if cfg.get('packets_npz') else None, source_store=Path(cfg['source_store']) if cfg.get('source_store') else None, flows_parquet=Path(cfg['flows_parquet']), flow_features_path=Path(cfg['flow_features_path']) if cfg.get('flow_features_path') else None, flow_feature_columns=cfg.get('flow_feature_columns'), flow_features_align=str(cfg.get('flow_features_align', 'auto')), T=int(cfg['T']), split_seed=int(cfg.get('data_seed', cfg.get('seed', 42))), train_ratio=float(cfg.get('train_ratio', 0.8)), benign_label=str(cfg.get('benign_label', 'normal')), min_len=int(cfg.get('min_len', 2)), packet_preprocess=str(cfg.get('packet_preprocess', 'mixed_dequant')), attack_cap=int(cfg['attack_cap']) if cfg.get('attack_cap') else n_atk, val_cap=int(cfg['val_cap']) if cfg.get('val_cap') else n_val) + rng = np.random.default_rng(seed) + (train_packets, train_len) = (data.train_packets, data.train_len) + if len(train_packets) > n_train_cap: + idx = np.sort(rng.choice(len(train_packets), size=n_train_cap, replace=False)) + (train_packets, train_len) = (train_packets[idx], train_len[idx]) + (val_packets, val_len) = (data.val_packets, data.val_len) + (atk_packets, atk_len, atk_labels) = (data.attack_packets, data.attack_len, data.attack_labels) + if n_val is not None and len(val_packets) > n_val: + idx = np.sort(rng.choice(len(val_packets), size=n_val, replace=False)) + (val_packets, val_len) = (val_packets[idx], val_len[idx]) + if n_atk is not None and len(atk_packets) > n_atk: + idx = np.sort(rng.choice(len(atk_packets), size=n_atk, replace=False)) + (atk_packets, atk_len, atk_labels) = (atk_packets[idx], atk_len[idx], atk_labels[idx]) + return {'train_packets': train_packets, 'train_len': train_len, 'val_packets': val_packets, 'val_len': val_len, 'atk_packets': atk_packets, 'atk_len': atk_len, 'atk_labels': atk_labels} + +def _load_cross(spec, ckpt, seed, n_train_cap, T): + packet_mean = np.asarray(ckpt['packet_mean'], dtype=np.float32) + packet_std = np.asarray(ckpt['packet_std'], dtype=np.float32) + packet_preprocess = str(ckpt.get('packet_preprocess', 'mixed_dequant')) + src_cfg_path = REPO / 'artifacts' / spec['model_template'].format(seed=seed) / 'config.yaml' + src_cfg = yaml.safe_load(src_cfg_path.read_text()) + src_data = load_unified_data(packets_npz=Path(src_cfg['packets_npz']) if src_cfg.get('packets_npz') else None, source_store=Path(src_cfg['source_store']) if src_cfg.get('source_store') else None, flows_parquet=Path(src_cfg['flows_parquet']), flow_features_path=Path(src_cfg['flow_features_path']) if src_cfg.get('flow_features_path') else None, flow_feature_columns=src_cfg.get('flow_feature_columns'), flow_features_align=str(src_cfg.get('flow_features_align', 'auto')), T=int(src_cfg['T']), split_seed=int(src_cfg.get('data_seed', src_cfg.get('seed', 42))), train_ratio=float(src_cfg.get('train_ratio', 0.8)), benign_label=str(src_cfg.get('benign_label', 'normal')), min_len=int(src_cfg.get('min_len', 2)), packet_preprocess=packet_preprocess, attack_cap=None, val_cap=None) + rng = np.random.default_rng(seed + 1000) + (train_packets, train_len) = (src_data.train_packets, src_data.train_len) + if len(train_packets) > n_train_cap: + idx = np.sort(rng.choice(len(train_packets), size=n_train_cap, replace=False)) + (train_packets, train_len) = (train_packets[idx], train_len[idx]) + target_store = REPO / spec['target_store'] + target_flows = REPO / spec['target_flows'] + (n_benign, n_attack) = (int(spec['n_benign']), int(spec['n_attack'])) + flows = pd.read_parquet(target_flows, columns=['flow_id', 'label']) + labels = flows['label'].astype(str).to_numpy() + rng2 = np.random.default_rng(seed) + benign_idx = np.flatnonzero(labels == 'normal') + attack_idx = np.flatnonzero(labels != 'normal') + b_sel = np.sort(rng2.choice(benign_idx, size=n_benign, replace=False)) + atk_classes = sorted(set(labels[attack_idx])) + per_class = max(1, n_attack // len(atk_classes)) + chunks = [] + for cls in atk_classes: + pool = attack_idx[labels[attack_idx] == cls] + k = min(per_class, len(pool)) + if k: + chunks.append(rng2.choice(pool, size=k, replace=False)) + a_sel = np.sort(np.concatenate(chunks)) + if len(a_sel) > n_attack: + a_sel = np.sort(rng2.choice(a_sel, size=n_attack, replace=False)) + store = PacketShardStore.open(target_store) + + def _materialize(idx): + (tok, ll) = store.read_packets(idx, T=T) + ll = np.minimum(ll, T).astype(np.int32) + return (tok.astype(np.float32), ll) + (b_tok, b_len) = _materialize(b_sel) + (a_tok, a_len) = _materialize(a_sel) + if packet_preprocess == 'mixed_dequant': + val_packets = _apply_mixed_dequant(b_tok, b_len, packet_mean, packet_std, split_tag='val', seed=seed) + atk_packets = _apply_mixed_dequant(a_tok, a_len, packet_mean, packet_std, split_tag='attack', seed=seed) + else: + val_packets = _zscore(b_tok, packet_mean, packet_std) + atk_packets = _zscore(a_tok, packet_mean, packet_std) + msk_b = np.arange(T)[None, :] < b_len[:, None] + msk_a = np.arange(T)[None, :] < a_len[:, None] + val_packets = (val_packets * msk_b[:, :, None]).astype(np.float32) + atk_packets = (atk_packets * msk_a[:, :, None]).astype(np.float32) + return {'train_packets': train_packets, 'train_len': train_len, 'val_packets': val_packets, 'val_len': b_len, 'atk_packets': atk_packets, 'atk_len': a_len, 'atk_labels': labels[a_sel]} + +def _flatten_packets(packets: np.ndarray, lens: np.ndarray) -> np.ndarray: + out_chunks = [] + for i in range(len(packets)): + L = int(lens[i]) + if L > 0: + out_chunks.append(packets[i, :L]) + if not out_chunks: + return np.empty((0, packets.shape[-1]), dtype=np.float32) + return np.concatenate(out_chunks, axis=0).astype(np.float32) + +def _train_kitnet(kit: KitNET, train_flat: np.ndarray) -> dict[str, float]: + t0 = time.time() + last_rmse = 0.0 + for i in range(len(train_flat)): + last_rmse = kit.process(train_flat[i]) + if (i + 1) % 50000 == 0: + print(f' [train] processed {i + 1:,}/{len(train_flat):,} last_rmse={last_rmse:.4f}', flush=True) + return {'t_train_sec': round(time.time() - t0, 2), 'n_trained_packets': len(train_flat)} + +def _score_flows(kit: KitNET, packets: np.ndarray, lens: np.ndarray) -> dict[str, np.ndarray]: + N = len(packets) + means = np.zeros(N, dtype=np.float32) + maxes = np.zeros(N, dtype=np.float32) + medians = np.zeros(N, dtype=np.float32) + p90s = np.zeros(N, dtype=np.float32) + for i in range(N): + L = int(lens[i]) + if L == 0: + continue + rmses = np.zeros(L, dtype=np.float32) + for t in range(L): + rmses[t] = kit.execute(packets[i, t]) + means[i] = rmses.mean() + maxes[i] = rmses.max() + medians[i] = np.median(rmses) + p90s[i] = np.percentile(rmses, 90) + return {'mean': means, 'max': maxes, 'median': medians, 'p90': p90s} + +def _per_class(val_score: np.ndarray, atk_score: np.ndarray, atk_labels: np.ndarray): + out = {} + for cls in sorted(set(atk_labels)): + m = atk_labels == cls + n_c = int(m.sum()) + v_c = atk_score[m] + y = np.r_[np.zeros(len(val_score)), np.ones(len(v_c))] + s = np.r_[val_score, v_c] + out[cls] = {'_n': float(n_c), 'auroc': _safe_metric(roc_auc_score, y, s)} + return out + +def main(): + p = argparse.ArgumentParser() + p.add_argument('--protocol', required=True, choices=list(WITHIN_DIRS) + list(CROSS_DIRS)) + p.add_argument('--seed', type=int, required=True, choices=[42, 43, 44]) + p.add_argument('--out-dir', type=Path, required=True) + p.add_argument('--n-train-cap', type=int, default=2000, help='Cap source-benign train flows (each contributes ~T packets).') + p.add_argument('--fm-grace', type=int, default=2000, help='Kitsune feature-mapper grace period (packets).') + p.add_argument('--ad-grace', type=int, default=20000, help='Kitsune anomaly-detector grace period (packets).') + p.add_argument('--max-ae-size', type=int, default=10) + p.add_argument('--lr', type=float, default=0.1) + p.add_argument('--hidden-ratio', type=float, default=0.75) + p.add_argument('--T', type=int, default=64) + args = p.parse_args() + args.out_dir.mkdir(parents=True, exist_ok=True) + is_within = args.protocol in WITHIN_DIRS + if is_within: + (template, caps) = WITHIN_DIRS[args.protocol] + model_dir = REPO / 'artifacts' / template.format(seed=args.seed) + else: + spec = CROSS_DIRS[args.protocol] + model_dir = REPO / 'artifacts' / spec['model_template'].format(seed=args.seed) + print(f'[run] kitsune protocol={args.protocol} seed={args.seed}') + print(f'[run] using packet stats from {model_dir}/model.pt') + ckpt = torch.load(model_dir / 'model.pt', map_location='cpu', weights_only=False) + if is_within: + arrays = _load_within(model_dir, n_val=caps['n_val'], n_atk=caps['n_atk'], n_train_cap=args.n_train_cap, seed=args.seed) + else: + arrays = _load_cross(spec, ckpt, args.seed, args.n_train_cap, args.T) + n_train = len(arrays['train_packets']) + n_val = len(arrays['val_packets']) + n_atk = len(arrays['atk_packets']) + D = arrays['train_packets'].shape[-1] + print(f'[data] train_flows={n_train:,} val={n_val:,} attack={n_atk:,} D={D}') + train_flat = _flatten_packets(arrays['train_packets'], arrays['train_len']) + print(f'[data] train_flat packets={len(train_flat):,} FM_grace={args.fm_grace} AD_grace={args.ad_grace}') + if len(train_flat) < args.fm_grace + args.ad_grace: + raise ValueError(f'Need at least FM+AD={args.fm_grace + args.ad_grace} packets, have {len(train_flat)} (try increasing --n-train-cap).') + kit = KitNET(n=D, max_autoencoder_size=args.max_ae_size, FM_grace_period=args.fm_grace, AD_grace_period=args.ad_grace, learning_rate=args.lr, hidden_ratio=args.hidden_ratio) + train_meta = _train_kitnet(kit, train_flat) + print(f'[train] {train_meta}') + t0 = time.time() + val_aggs = _score_flows(kit, arrays['val_packets'], arrays['val_len']) + print(f'[score] benign in {time.time() - t0:.1f}s') + t0 = time.time() + atk_aggs = _score_flows(kit, arrays['atk_packets'], arrays['atk_len']) + print(f'[score] attack in {time.time() - t0:.1f}s') + overall = {} + per_class_by_agg = {} + for agg in ('mean', 'max', 'median', 'p90'): + v = val_aggs[agg] + a = atk_aggs[agg] + y = np.r_[np.zeros(len(v)), np.ones(len(a))] + s = np.r_[v, a] + overall[agg] = {'auroc': _safe_metric(roc_auc_score, y, s), 'auprc': _safe_metric(average_precision_score, y, s)} + per_class_by_agg[agg] = _per_class(v, a, np.asarray(arrays['atk_labels']).astype(str)) + out = {'method': 'kitsune_path_b', 'protocol': args.protocol, 'seed': args.seed, 'model_dir': str(model_dir), 'n_train_flows': n_train, 'n_train_packets': int(len(train_flat)), 'n_val': n_val, 'n_atk': n_atk, 'D': int(D), 'fm_grace': args.fm_grace, 'ad_grace': args.ad_grace, 'max_ae_size': args.max_ae_size, 't_train_sec': train_meta['t_train_sec'], 'overall_by_agg': overall, 'per_class_by_agg': per_class_by_agg} + out_json = args.out_dir / f'{args.protocol}_seed{args.seed}.json' + out_json.write_text(json.dumps(out, indent=2)) + npz_path = out_json.with_suffix('.npz') + save = {'a_labels': np.asarray(arrays['atk_labels']).astype(str)} + for agg in ('mean', 'max', 'median', 'p90'): + save[f'b_{agg}'] = val_aggs[agg].astype(np.float32) + save[f'a_{agg}'] = atk_aggs[agg].astype(np.float32) + np.savez_compressed(npz_path, **save) + print(f'[saved] {out_json}') + print(f'[saved] {npz_path}') + best = max(overall, key=lambda k: overall[k]['auroc']) + print(f"[best agg={best}] AUROC={overall[best]['auroc']:.4f} AUPRC={overall[best]['auprc']:.4f}") + print() + print('=== overall AUROC by aggregator ===') + for k in sorted(overall, key=lambda kk: -overall[kk]['auroc']): + print(f" {k:<8s} AUROC={overall[k]['auroc']:.4f} AUPRC={overall[k]['auprc']:.4f}") +if __name__ == '__main__': + main() diff --git a/scripts/baselines/run_kitsune_all.sh b/scripts/baselines/run_kitsune_all.sh new file mode 100755 index 0000000..a685962 --- /dev/null +++ b/scripts/baselines/run_kitsune_all.sh @@ -0,0 +1,35 @@ +#!/usr/bin/env bash +set -euo pipefail +REPO=$(cd "$(dirname "$0")/../.." && pwd) +cd "$REPO" + +OUT_DIR="artifacts/baselines/kitsune_2026_04_29" +mkdir -p "$OUT_DIR" +LOG="$OUT_DIR/master.log" +: > "$LOG" + +PROTOCOLS_DEFAULT="iscxtor_within cicids_within cicddos_within forward_cross reverse_cross" +SEEDS_DEFAULT="42 43 44" +PROTOCOLS="${PROTOCOLS:-$PROTOCOLS_DEFAULT}" +SEEDS="${SEEDS:-$SEEDS_DEFAULT}" +N_TRAIN_CAP="${N_TRAIN_CAP:-5000}" + +for protocol in $PROTOCOLS; do + for seed in $SEEDS; do + out_json="$OUT_DIR/${protocol}_seed${seed}.json" + if [[ -f "$out_json" ]]; then + echo "[skip] $out_json exists" | tee -a "$LOG" + continue + fi + echo "=== protocol=$protocol seed=$seed n_train_cap=$N_TRAIN_CAP ===" | tee -a "$LOG" + ts=$(date +%s) + uv run --no-sync python scripts/baselines/run_kitsune.py \ + --protocol "$protocol" --seed "$seed" \ + --out-dir "$OUT_DIR" \ + --n-train-cap "$N_TRAIN_CAP" \ + 2>&1 | tee -a "$LOG" + te=$(date +%s) + echo "[done] elapsed=$((te-ts))s $out_json" | tee -a "$LOG" + done +done +echo "ALL DONE" diff --git a/scripts/baselines/run_kitsune_path_a.py b/scripts/baselines/run_kitsune_path_a.py new file mode 100644 index 0000000..bd926f8 --- /dev/null +++ b/scripts/baselines/run_kitsune_path_a.py @@ -0,0 +1,211 @@ +from __future__ import annotations +import argparse +import json +import sys +import time +from collections import defaultdict +from pathlib import Path +import numpy as np +import pandas as pd +import yaml +if not hasattr(np, 'Inf'): + np.Inf = np.inf +from sklearn.metrics import average_precision_score, roc_auc_score +REPO = Path(__file__).resolve().parents[2] +sys.path.insert(0, str(REPO / 'baselines/Kitsune-py')) +sys.path.insert(0, str(REPO / 'Unified_CFM')) +from FeatureExtractor import FE +from KitNET.KitNET import KitNET +from data import load_unified_data +PCAP_GLOBS = {'iscxtor': str(REPO / 'datasets/iscxtor2016/raw/pcap_extracted/**/*.pcap'), 'cicids2017': str(REPO / 'datasets/cicids2017/raw/pcap/*.pcap'), 'cicddos2019': str(REPO / 'datasets/cicddos2019/raw/pcap/*')} +WITHIN_DIRS = {'iscxtor_within': ('phase25_multiseed_2026_04_25/iscxtor2016_lambda0p3_seed{seed}', 'iscxtor', {'n_val': 10000, 'n_atk': None}), 'cicids_within': ('phase25_sigma06_multiseed_2026_04_25/cicids2017_lambda0p3_sigma0p6_seed{seed}', 'cicids2017', {'n_val': 10000, 'n_atk': 30000}), 'cicddos_within': ('phase25_multiseed_2026_04_25/cicddos2019_lambda0p3_seed{seed}', 'cicddos2019', {'n_val': 10000, 'n_atk': 20000})} + +def _canonical_key(src_ip, dst_ip, src_port, dst_port, protocol) -> tuple: + a = (src_ip, src_port) + b = (dst_ip, dst_port) + if a <= b: + return (a[0], b[0], a[1], b[1], int(protocol)) + return (b[0], a[0], b[1], a[1], int(protocol)) + +def _proto_from_kitsune(srcproto: str, dstproto: str) -> int: + if srcproto == 'icmp': + return 1 + if srcproto == 'arp': + return 0 + return -1 + +class FEWithMeta(FE): + + def __init__(self, path, limit=np.inf): + super().__init__(path, limit) + self._last_ts = None + self._last_5tuple = None + self._last_framelen = None + + def get_next_vector(self): + if self.curPacketIndx == self.limit: + if self.parse_type == 'tsv': + self.tsvinf.close() + return [] + if self.parse_type == 'tsv': + row = self.tsvin.__next__() + IPtype = np.nan + timestamp = row[0] + framelen = row[1] + srcIP = '' + dstIP = '' + if row[4] != '': + (srcIP, dstIP, IPtype) = (row[4], row[5], 0) + elif row[17] != '': + (srcIP, dstIP, IPtype) = (row[17], row[18], 1) + srcproto = row[6] + row[8] + dstproto = row[7] + row[9] + (srcMAC, dstMAC) = (row[2], row[3]) + if srcproto == '': + if row[12] != '': + (srcproto, dstproto) = ('arp', 'arp') + (srcIP, dstIP, IPtype) = (row[14], row[16], 0) + elif row[10] != '': + (srcproto, dstproto, IPtype) = ('icmp', 'icmp', 0) + elif srcIP + srcproto + dstIP + dstproto == '': + (srcIP, dstIP) = (row[2], row[3]) + else: + return [] + try: + sp = int(srcproto) if srcproto.isdigit() else 0 + dp = int(dstproto) if dstproto.isdigit() else 0 + except Exception: + (sp, dp) = (0, 0) + try: + self._last_ts = float(timestamp) + except Exception: + self._last_ts = np.nan + self._last_5tuple = (srcIP, dstIP, sp, dp) + try: + self._last_framelen = int(framelen) + except Exception: + self._last_framelen = 0 + self.curPacketIndx += 1 + try: + return self.nstat.updateGetStats(IPtype, srcMAC, dstMAC, srcIP, srcproto, dstIP, dstproto, int(framelen), float(timestamp)) + except Exception as e: + print(f' [warn] netStat error: {e}') + return [] + +def _stream_pcap_kitsune(pcap_path: Path, *, kit: KitNET, fm_grace: int, ad_grace: int, packet_limit: int, fivetuple_to_rmses: dict, n_packets_total: list) -> None: + print(f' [stream] {pcap_path.name}', flush=True) + fe = FEWithMeta(str(pcap_path), limit=packet_limit) + t0 = time.time() + n_local = 0 + while True: + x = fe.get_next_vector() + if len(x) == 0: + break + n_local += 1 + n_packets_total[0] += 1 + rmse = kit.process(x) + if rmse is None or rmse == 0: + continue + if fe._last_5tuple is None: + continue + (srcIP, dstIP, sp, dp) = fe._last_5tuple + key = (srcIP, dstIP, sp, dp) if (srcIP, sp) <= (dstIP, dp) else (dstIP, srcIP, dp, sp) + fivetuple_to_rmses[key].append(rmse) + if n_local % 200000 == 0: + print(f' [{n_local:,}] elapsed {time.time() - t0:.0f}s ({n_local / max(time.time() - t0, 0.001):.0f} pkt/s)', flush=True) + print(f' [stream] {pcap_path.name} done: {n_local:,} packets in {time.time() - t0:.0f}s', flush=True) + +def _flows_to_key(flows_df: pd.DataFrame) -> np.ndarray: + keys = [] + for (src_ip, dst_ip, sp, dp) in zip(flows_df['src_ip'], flows_df['dst_ip'], flows_df['src_port'], flows_df['dst_port']): + if (str(src_ip), int(sp)) <= (str(dst_ip), int(dp)): + k = (str(src_ip), str(dst_ip), int(sp), int(dp)) + else: + k = (str(dst_ip), str(src_ip), int(dp), int(sp)) + keys.append(k) + return np.asarray(keys, dtype=object) + +def _safe_metric(fn, y, s) -> float: + s = np.nan_to_num(s, nan=0.0, posinf=1000000000000.0, neginf=-1000000000000.0) + try: + return float(fn(y, s)) + except ValueError: + return float('nan') + +def main(): + p = argparse.ArgumentParser() + p.add_argument('--protocol', required=True, choices=list(WITHIN_DIRS)) + p.add_argument('--seed', type=int, required=True) + p.add_argument('--out-dir', type=Path, required=True) + p.add_argument('--fm-grace', type=int, default=5000) + p.add_argument('--ad-grace', type=int, default=50000) + p.add_argument('--max-ae-size', type=int, default=10) + p.add_argument('--lr', type=float, default=0.1) + p.add_argument('--hidden-ratio', type=float, default=0.75) + p.add_argument('--packet-limit-per-pcap', type=int, default=2000000, help='Cap per-pcap packets to keep runtime tractable. None = full.') + p.add_argument('--max-pcaps', type=int, default=None, help='Cap number of pcap files processed (default: all).') + args = p.parse_args() + args.out_dir.mkdir(parents=True, exist_ok=True) + (template, ds_name, caps) = WITHIN_DIRS[args.protocol] + model_dir = REPO / 'artifacts' / template.format(seed=args.seed) + print(f'[run] kitsune_path_a protocol={args.protocol} seed={args.seed}') + print(f'[run] dataset={ds_name} model_dir={model_dir}') + cfg = yaml.safe_load((model_dir / 'config.yaml').read_text()) + data = load_unified_data(packets_npz=Path(cfg['packets_npz']) if cfg.get('packets_npz') else None, source_store=Path(cfg['source_store']) if cfg.get('source_store') else None, flows_parquet=Path(cfg['flows_parquet']), flow_features_path=Path(cfg['flow_features_path']) if cfg.get('flow_features_path') else None, flow_feature_columns=cfg.get('flow_feature_columns'), flow_features_align=str(cfg.get('flow_features_align', 'auto')), T=int(cfg['T']), split_seed=int(cfg.get('data_seed', cfg.get('seed', 42))), train_ratio=float(cfg.get('train_ratio', 0.8)), benign_label=str(cfg.get('benign_label', 'normal')), min_len=int(cfg.get('min_len', 2)), packet_preprocess=str(cfg.get('packet_preprocess', 'mixed_dequant')), attack_cap=int(cfg['attack_cap']) if cfg.get('attack_cap') else caps['n_atk'], val_cap=int(cfg['val_cap']) if cfg.get('val_cap') else caps['n_val']) + flows_full = pd.read_parquet(cfg['flows_parquet']) + print(f'[data] flows.parquet rows: {len(flows_full):,}; val={len(data.val_flow):,} attack={len(data.attack_flow):,}') + from glob import glob + pcaps = sorted(glob(PCAP_GLOBS[ds_name], recursive=True)) + pcaps = [Path(p) for p in pcaps] + if args.max_pcaps is not None: + pcaps = pcaps[:args.max_pcaps] + print(f'[pcap] discovered {len(pcaps)} pcap(s)') + for p in pcaps[:5]: + print(f' {p}') + if len(pcaps) > 5: + print(f' ...({len(pcaps) - 5} more)') + kit = KitNET(n=100, max_autoencoder_size=args.max_ae_size, FM_grace_period=args.fm_grace, AD_grace_period=args.ad_grace, learning_rate=args.lr, hidden_ratio=args.hidden_ratio) + fivetuple_to_rmses: dict = defaultdict(list) + n_total = [0] + t0 = time.time() + for p in pcaps: + _stream_pcap_kitsune(p, kit=kit, fm_grace=args.fm_grace, ad_grace=args.ad_grace, packet_limit=args.packet_limit_per_pcap, fivetuple_to_rmses=fivetuple_to_rmses, n_packets_total=n_total) + elapsed = time.time() - t0 + print(f'[stream] total {n_total[0]:,} packets in {elapsed:.0f}s ({n_total[0] / max(elapsed, 0.001):.0f} pkt/s)') + print(f'[stream] unique 5-tuples seen: {len(fivetuple_to_rmses):,}') + keys_full = _flows_to_key(flows_full) + print(f'[match] keying {len(keys_full):,} flows to 5-tuples') + flow_score_mean = np.full(len(flows_full), np.nan, dtype=np.float64) + flow_score_max = np.full(len(flows_full), np.nan, dtype=np.float64) + flow_score_median = np.full(len(flows_full), np.nan, dtype=np.float64) + n_matched = 0 + for (i, k) in enumerate(keys_full): + rl = fivetuple_to_rmses.get(tuple(k)) + if rl: + flow_score_mean[i] = float(np.mean(rl)) + flow_score_max[i] = float(np.max(rl)) + flow_score_median[i] = float(np.median(rl)) + n_matched += 1 + print(f'[match] flows with RMSE coverage: {n_matched:,}/{len(flows_full):,} ({100 * n_matched / max(len(flows_full), 1):.1f}%)') + val_flow_ids = set((int(x) for x in data.val_flow_ids)) if hasattr(data, 'val_flow_ids') else None + bin_labels = (flows_full['label'].astype(str) != cfg.get('benign_label', 'normal')).astype(int).to_numpy() + keys = ['mean', 'max', 'median'] + score_arrs = {'mean': flow_score_mean, 'max': flow_score_max, 'median': flow_score_median} + overall = {} + for k in keys: + s = score_arrs[k] + valid = ~np.isnan(s) + if valid.sum() < 10: + overall[k] = {'auroc': float('nan'), 'auprc': float('nan'), 'n_valid': int(valid.sum())} + continue + y = bin_labels[valid] + sv = s[valid] + overall[k] = {'auroc': _safe_metric(roc_auc_score, y, sv), 'auprc': _safe_metric(average_precision_score, y, sv), 'n_valid': int(valid.sum())} + print(f" [{k}] AUROC={overall[k]['auroc']:.4f} AUPRC={overall[k]['auprc']:.4f} (n_valid={overall[k]['n_valid']:,})") + out_json = args.out_dir / f'{args.protocol}_seed{args.seed}.json' + out = {'method': 'kitsune_path_a', 'protocol': args.protocol, 'seed': args.seed, 'dataset': ds_name, 'n_pcaps': len(pcaps), 'n_total_packets': int(n_total[0]), 'n_unique_5tuples': int(len(fivetuple_to_rmses)), 'n_flows_total': int(len(flows_full)), 'n_flows_matched': int(n_matched), 'fm_grace': args.fm_grace, 'ad_grace': args.ad_grace, 'packet_limit_per_pcap': args.packet_limit_per_pcap, 'elapsed_sec': round(elapsed, 1), 'overall_by_agg': overall} + out_json.write_text(json.dumps(out, indent=2)) + np.savez_compressed(out_json.with_suffix('.npz'), flow_score_mean=flow_score_mean.astype(np.float32), flow_score_max=flow_score_max.astype(np.float32), flow_score_median=flow_score_median.astype(np.float32), binary_label=bin_labels.astype(np.int8)) + print(f'[saved] {out_json}') +if __name__ == '__main__': + main() diff --git a/scripts/baselines/run_shafir_nf.py b/scripts/baselines/run_shafir_nf.py new file mode 100644 index 0000000..3891dc2 --- /dev/null +++ b/scripts/baselines/run_shafir_nf.py @@ -0,0 +1,227 @@ +from __future__ import annotations +import argparse +import json +import os +import sys +import time +from pathlib import Path +from typing import Any +import numpy as np +import pandas as pd +import torch +import yaml +os.environ.setdefault('JAX_PLATFORMS', 'cpu') +import optax +from pzflow import Flow +from sklearn.metrics import average_precision_score, roc_auc_score +REPO = Path(__file__).resolve().parents[2] +sys.path.insert(0, str(REPO / 'Packet_CFM')) +sys.path.insert(0, str(REPO / 'Unified_CFM')) +from data import _apply_mixed_dequant, _zscore, load_unified_data +from model import UnifiedCFMConfig, UnifiedTokenCFM +from packet_store import PacketShardStore +WITHIN_DIRS = {'iscxtor_within': ('phase25_multiseed_2026_04_25/iscxtor2016_lambda0p3_seed{seed}', {'n_val': 10000, 'n_atk': None}), 'cicids_within': ('phase25_sigma06_multiseed_2026_04_25/cicids2017_lambda0p3_sigma0p6_seed{seed}', {'n_val': 10000, 'n_atk': 30000}), 'cicddos_within': ('phase25_multiseed_2026_04_25/cicddos2019_lambda0p3_seed{seed}', {'n_val': 10000, 'n_atk': 20000}), 'ciciot_within': ('runs/unified_cfm_ciciot2023_shafir5_2026_04_29', {'n_val': 10000, 'n_atk': 30000})} +CROSS_DIRS = {'forward_cross': {'model_template': 'phase25_sigma06_multiseed_2026_04_25/cicids2017_lambda0p3_sigma0p6_seed{seed}', 'target_store': 'datasets/cicddos2019/processed/full_store', 'target_flows': 'datasets/cicddos2019/processed/flows.parquet', 'target_flow_features': 'datasets/cicddos2019/processed/flow_features.parquet', 'n_benign': 10000, 'n_attack': 10000}, 'reverse_cross': {'model_template': 'phase25_multiseed_2026_04_25/cicddos2019_lambda0p3_seed{seed}', 'target_store': 'datasets/cicids2017/processed/full_store', 'target_flows': 'datasets/cicids2017/processed/flows.parquet', 'target_flow_features': 'datasets/cicids2017/processed/flow_features.parquet', 'n_benign': 10000, 'n_attack': 10000}} + +def _load_within(model_dir: Path, n_val: int | None, n_atk: int | None, n_train_cap: int, seed: int) -> dict[str, Any]: + cfg = yaml.safe_load((model_dir / 'config.yaml').read_text()) + data = load_unified_data(packets_npz=Path(cfg['packets_npz']) if cfg.get('packets_npz') else None, source_store=Path(cfg['source_store']) if cfg.get('source_store') else None, flows_parquet=Path(cfg['flows_parquet']), flow_features_path=Path(cfg['flow_features_path']) if cfg.get('flow_features_path') else None, flow_feature_columns=cfg.get('flow_feature_columns'), flow_features_align=str(cfg.get('flow_features_align', 'auto')), T=int(cfg['T']), split_seed=int(cfg.get('data_seed', cfg.get('seed', 42))), train_ratio=float(cfg.get('train_ratio', 0.8)), benign_label=str(cfg.get('benign_label', 'normal')), min_len=int(cfg.get('min_len', 2)), packet_preprocess=str(cfg.get('packet_preprocess', 'mixed_dequant')), attack_cap=int(cfg['attack_cap']) if cfg.get('attack_cap') else n_atk, val_cap=int(cfg['val_cap']) if cfg.get('val_cap') else n_val) + rng = np.random.default_rng(seed) + train_flow = data.train_flow + if len(train_flow) > n_train_cap: + idx = np.sort(rng.choice(len(train_flow), size=n_train_cap, replace=False)) + train_flow = train_flow[idx] + val_flow = data.val_flow + (atk_flow, atk_labels) = (data.attack_flow, data.attack_labels) + if n_val is not None and len(val_flow) > n_val: + idx = np.sort(rng.choice(len(val_flow), size=n_val, replace=False)) + val_flow = val_flow[idx] + if n_atk is not None and len(atk_flow) > n_atk: + idx = np.sort(rng.choice(len(atk_flow), size=n_atk, replace=False)) + atk_flow = atk_flow[idx] + atk_labels = atk_labels[idx] + return {'train_flow': train_flow, 'val_flow': val_flow, 'atk_flow': atk_flow, 'atk_labels': atk_labels} + +def _load_cross(spec: dict[str, Any], ckpt_dict: dict[str, Any], seed: int, T: int, n_train_cap: int) -> dict[str, Any]: + flow_mean = np.asarray(ckpt_dict['flow_mean'], dtype=np.float32) + flow_std = np.asarray(ckpt_dict['flow_std'], dtype=np.float32) + flow_names = [str(n) for n in ckpt_dict['flow_feature_names']] + target_store = REPO / spec['target_store'] + target_flows = REPO / spec['target_flows'] + target_flow_features = REPO / spec['target_flow_features'] + (n_benign, n_attack) = (int(spec['n_benign']), int(spec['n_attack'])) + flows = pd.read_parquet(target_flows, columns=['flow_id', 'label']) + ff = pd.read_parquet(target_flow_features) + if not np.array_equal(flows['flow_id'].to_numpy(dtype=np.uint64), ff['flow_id'].to_numpy(dtype=np.uint64)): + raise ValueError('target flows and flow_features not row-aligned') + labels = flows['label'].astype(str).to_numpy() + rng = np.random.default_rng(seed) + benign_idx = np.flatnonzero(labels == 'normal') + attack_idx = np.flatnonzero(labels != 'normal') + b_sel = np.sort(rng.choice(benign_idx, size=n_benign, replace=False)) + atk_classes = sorted(set(labels[attack_idx])) + per_class = max(1, n_attack // len(atk_classes)) + a_sel_chunks = [] + for cls in atk_classes: + pool = attack_idx[labels[attack_idx] == cls] + k = min(per_class, len(pool)) + if k: + a_sel_chunks.append(rng.choice(pool, size=k, replace=False)) + a_sel = np.sort(np.concatenate(a_sel_chunks)) + if len(a_sel) > n_attack: + a_sel = np.sort(rng.choice(a_sel, size=n_attack, replace=False)) + + def _flow_only(idx): + f = ff.iloc[idx][flow_names].to_numpy(dtype=np.float64) + f = np.nan_to_num(f, nan=0.0, posinf=0.0, neginf=0.0).astype(np.float32) + return ((f - flow_mean) / np.maximum(flow_std, 1e-06)).astype(np.float32) + val_flow = _flow_only(b_sel) + atk_flow = _flow_only(a_sel) + atk_labels = labels[a_sel] + src_flows = pd.read_parquet(REPO / ckpt_dict_paths(ckpt_dict)['flows'], columns=['flow_id', 'label']) + src_ff = pd.read_parquet(REPO / ckpt_dict_paths(ckpt_dict)['flow_features']) + if not np.array_equal(src_flows['flow_id'].to_numpy(dtype=np.uint64), src_ff['flow_id'].to_numpy(dtype=np.uint64)): + raise ValueError('source flows and flow_features not row-aligned') + src_labels = src_flows['label'].astype(str).to_numpy() + src_benign_idx = np.flatnonzero(src_labels == 'normal') + rng2 = np.random.default_rng(seed + 1000) + if len(src_benign_idx) > n_train_cap: + src_benign_idx = np.sort(rng2.choice(src_benign_idx, size=n_train_cap, replace=False)) + src_train = src_ff.iloc[src_benign_idx][flow_names].to_numpy(dtype=np.float64) + src_train = np.nan_to_num(src_train, nan=0.0, posinf=0.0, neginf=0.0).astype(np.float32) + train_flow = ((src_train - flow_mean) / np.maximum(flow_std, 1e-06)).astype(np.float32) + return {'train_flow': train_flow, 'val_flow': val_flow, 'atk_flow': atk_flow, 'atk_labels': atk_labels, 'flow_names': flow_names} + +def ckpt_dict_paths(ckpt: dict[str, Any]) -> dict[str, str]: + raise NotImplementedError('paths must be passed via main()') + +def _train_and_score(train_flow: np.ndarray, val_flow: np.ndarray, atk_flow: np.ndarray, *, epochs: int, lr: float, optimizer: str, verbose: bool): + cols = [f'x{i}' for i in range(train_flow.shape[1])] + df_train = pd.DataFrame(train_flow.astype(np.float32), columns=cols) + df_val = pd.DataFrame(val_flow.astype(np.float32), columns=cols) + df_atk = pd.DataFrame(atk_flow.astype(np.float32), columns=cols) + if optimizer == 'sgd': + opt = optax.sgd(learning_rate=lr) + elif optimizer == 'adam': + opt = optax.adam(learning_rate=lr) + else: + raise ValueError(f'unknown optimizer {optimizer!r}') + flow = Flow(df_train.columns.tolist()) + t0 = time.time() + losses = flow.train(df_train, optimizer=opt, epochs=epochs, verbose=verbose) + t_train = time.time() - t0 + t0 = time.time() + lp_val = np.asarray(flow.log_prob(df_val)) + lp_atk = np.asarray(flow.log_prob(df_atk)) + t_score = time.time() - t0 + return {'score_val': (-lp_val).astype(np.float32), 'score_atk': (-lp_atk).astype(np.float32), 'losses': np.asarray(losses, dtype=np.float64), 't_train': t_train, 't_score': t_score} + +def _safe_metric(fn, y, s) -> float: + s = np.nan_to_num(s, nan=0.0, posinf=1000000000000.0, neginf=-1000000000000.0) + try: + return float(fn(y, s)) + except ValueError: + return float('nan') + +def _per_class(val_score: np.ndarray, atk_score: np.ndarray, atk_labels: np.ndarray): + out = {} + for cls in sorted(set(atk_labels)): + m = atk_labels == cls + n_c = int(m.sum()) + v_c = atk_score[m] + y = np.r_[np.zeros(len(val_score)), np.ones(len(v_c))] + s = np.r_[val_score, v_c] + out[cls] = {'_n': float(n_c), 'auroc': _safe_metric(roc_auc_score, y, s)} + return out + +def main() -> None: + p = argparse.ArgumentParser() + p.add_argument('--protocol', required=True, choices=list(WITHIN_DIRS) + list(CROSS_DIRS)) + p.add_argument('--seed', type=int, required=True, choices=[42, 43, 44]) + p.add_argument('--out-dir', type=Path, required=True) + p.add_argument('--n-train-cap', type=int, default=10000, help='Cap benign train (default 10k mirrors Shafir).') + p.add_argument('--epochs', type=int, default=100) + p.add_argument('--lr', type=float, default=0.001) + p.add_argument('--optimizer', choices=['sgd', 'adam'], default='sgd') + p.add_argument('--T', type=int, default=64) + p.add_argument('--verbose', action='store_true') + args = p.parse_args() + args.out_dir.mkdir(parents=True, exist_ok=True) + is_within = args.protocol in WITHIN_DIRS + if is_within: + (template, caps) = WITHIN_DIRS[args.protocol] + model_dir = REPO / 'artifacts' / template.format(seed=args.seed) + else: + spec = CROSS_DIRS[args.protocol] + model_dir = REPO / 'artifacts' / spec['model_template'].format(seed=args.seed) + print(f'[run] shafir_nf protocol={args.protocol} seed={args.seed}') + print(f'[run] using normalization stats from {model_dir}/model.pt (source ckpt)') + ckpt = torch.load(model_dir / 'model.pt', map_location='cpu', weights_only=False) + if is_within: + arrays = _load_within(model_dir, n_val=caps['n_val'], n_atk=caps['n_atk'], n_train_cap=args.n_train_cap, seed=args.seed) + else: + cfg = yaml.safe_load((model_dir / 'config.yaml').read_text()) + flows_parquet = Path(cfg['flows_parquet']) + flow_features_path = Path(cfg['flow_features_path']) + flow_mean = np.asarray(ckpt['flow_mean'], dtype=np.float32) + flow_std = np.asarray(ckpt['flow_std'], dtype=np.float32) + flow_names = [str(n) for n in ckpt['flow_feature_names']] + src_flows = pd.read_parquet(flows_parquet, columns=['flow_id', 'label']) + src_ff = pd.read_parquet(flow_features_path) + if not np.array_equal(src_flows['flow_id'].to_numpy(dtype=np.uint64), src_ff['flow_id'].to_numpy(dtype=np.uint64)): + raise ValueError('source flows and flow_features not row-aligned') + src_labels = src_flows['label'].astype(str).to_numpy() + src_benign_idx = np.flatnonzero(src_labels == 'normal') + rng2 = np.random.default_rng(args.seed + 1000) + if len(src_benign_idx) > args.n_train_cap: + src_benign_idx = np.sort(rng2.choice(src_benign_idx, size=args.n_train_cap, replace=False)) + src_train = src_ff.iloc[src_benign_idx][flow_names].to_numpy(dtype=np.float64) + src_train = np.nan_to_num(src_train, nan=0.0, posinf=0.0, neginf=0.0).astype(np.float32) + train_flow = ((src_train - flow_mean) / np.maximum(flow_std, 1e-06)).astype(np.float32) + target_store = REPO / spec['target_store'] + target_flows = REPO / spec['target_flows'] + target_flow_features = REPO / spec['target_flow_features'] + (n_benign, n_attack) = (int(spec['n_benign']), int(spec['n_attack'])) + flows = pd.read_parquet(target_flows, columns=['flow_id', 'label']) + ff = pd.read_parquet(target_flow_features) + labels = flows['label'].astype(str).to_numpy() + rng = np.random.default_rng(args.seed) + b_sel = np.sort(rng.choice(np.flatnonzero(labels == 'normal'), size=n_benign, replace=False)) + atk_idx = np.flatnonzero(labels != 'normal') + atk_classes = sorted(set(labels[atk_idx])) + per_class_n = max(1, n_attack // len(atk_classes)) + chunks = [] + for cls in atk_classes: + pool = atk_idx[labels[atk_idx] == cls] + k = min(per_class_n, len(pool)) + if k: + chunks.append(rng.choice(pool, size=k, replace=False)) + a_sel = np.sort(np.concatenate(chunks)) + if len(a_sel) > n_attack: + a_sel = np.sort(rng.choice(a_sel, size=n_attack, replace=False)) + + def _flow_only(idx): + f = ff.iloc[idx][flow_names].to_numpy(dtype=np.float64) + f = np.nan_to_num(f, nan=0.0, posinf=0.0, neginf=0.0).astype(np.float32) + return ((f - flow_mean) / np.maximum(flow_std, 1e-06)).astype(np.float32) + val_flow = _flow_only(b_sel) + atk_flow = _flow_only(a_sel) + atk_labels = labels[a_sel] + arrays = {'train_flow': train_flow, 'val_flow': val_flow, 'atk_flow': atk_flow, 'atk_labels': atk_labels} + print(f"[data] train={len(arrays['train_flow']):,} val={len(arrays['val_flow']):,} attack={len(arrays['atk_flow']):,} D={arrays['train_flow'].shape[1]}") + res = _train_and_score(arrays['train_flow'], arrays['val_flow'], arrays['atk_flow'], epochs=args.epochs, lr=args.lr, optimizer=args.optimizer, verbose=args.verbose) + (val_score, atk_score) = (res['score_val'], res['score_atk']) + y = np.r_[np.zeros(len(val_score)), np.ones(len(atk_score))] + s = np.r_[val_score, atk_score] + overall = {'neg_log_prob': {'auroc': _safe_metric(roc_auc_score, y, s), 'auprc': _safe_metric(average_precision_score, y, s)}} + per_cls = _per_class(val_score, atk_score, np.asarray(arrays['atk_labels']).astype(str)) + out = {'method': 'shafir_nf', 'protocol': args.protocol, 'seed': args.seed, 'model_dir': str(model_dir), 'n_train': int(len(arrays['train_flow'])), 'n_val': int(len(arrays['val_flow'])), 'n_atk': int(len(arrays['atk_flow'])), 'epochs': args.epochs, 'lr': args.lr, 'optimizer': args.optimizer, 't_train_sec': round(res['t_train'], 2), 't_score_sec': round(res['t_score'], 2), 'loss_first_last': [float(res['losses'][0]), float(res['losses'][-1])], 'overall': overall, 'per_class': per_cls} + out_json = args.out_dir / f'{args.protocol}_seed{args.seed}.json' + out_json.write_text(json.dumps(out, indent=2)) + npz_path = out_json.with_suffix('.npz') + np.savez_compressed(npz_path, b_neg_log_prob=val_score, a_neg_log_prob=atk_score, a_labels=np.asarray(arrays['atk_labels']).astype(str), losses=res['losses']) + print(f'[saved] {out_json}') + print(f'[saved] {npz_path}') + print(f"[result] AUROC={overall['neg_log_prob']['auroc']:.4f} AUPRC={overall['neg_log_prob']['auprc']:.4f} train={res['t_train']:.1f}s score={res['t_score']:.1f}s") +if __name__ == '__main__': + main() diff --git a/scripts/baselines/run_shafir_nf_all.sh b/scripts/baselines/run_shafir_nf_all.sh new file mode 100755 index 0000000..fd0a727 --- /dev/null +++ b/scripts/baselines/run_shafir_nf_all.sh @@ -0,0 +1,38 @@ +#!/usr/bin/env bash +set -euo pipefail +REPO=$(cd "$(dirname "$0")/../.." && pwd) +cd "$REPO" + +OUT_DIR="artifacts/baselines/shafir_nf_2026_04_29" +mkdir -p "$OUT_DIR" +LOG="$OUT_DIR/master.log" +: > "$LOG" + +PROTOCOLS_DEFAULT="iscxtor_within cicids_within cicddos_within forward_cross reverse_cross" +SEEDS_DEFAULT="42 43 44" +PROTOCOLS="${PROTOCOLS:-$PROTOCOLS_DEFAULT}" +SEEDS="${SEEDS:-$SEEDS_DEFAULT}" +EPOCHS="${EPOCHS:-100}" +LR="${LR:-0.001}" +OPTIMIZER="${OPTIMIZER:-sgd}" + +for protocol in $PROTOCOLS; do + for seed in $SEEDS; do + out_json="$OUT_DIR/${protocol}_seed${seed}.json" + if [[ -f "$out_json" ]]; then + echo "[skip] $out_json exists" | tee -a "$LOG" + continue + fi + echo "=== protocol=$protocol seed=$seed epochs=$EPOCHS opt=$OPTIMIZER lr=$LR ===" | tee -a "$LOG" + ts=$(date +%s) + uv run --no-sync python scripts/baselines/run_shafir_nf.py \ + --protocol "$protocol" --seed "$seed" \ + --out-dir "$OUT_DIR" \ + --epochs "$EPOCHS" --lr "$LR" --optimizer "$OPTIMIZER" \ + 2>&1 | tee -a "$LOG" + te=$(date +%s) + echo "[done] elapsed=$((te-ts))s $out_json" | tee -a "$LOG" + done +done + +echo "ALL DONE" diff --git a/scripts/baselines/run_shafir_nf_csv.py b/scripts/baselines/run_shafir_nf_csv.py new file mode 100644 index 0000000..3dfed2b --- /dev/null +++ b/scripts/baselines/run_shafir_nf_csv.py @@ -0,0 +1,265 @@ +from __future__ import annotations +import argparse +import json +import os +import sys +import time +import warnings +from glob import glob +from pathlib import Path +import numpy as np +import pandas as pd +os.environ.setdefault('JAX_PLATFORMS', 'cpu') +warnings.filterwarnings('ignore') +import optax +from pzflow import Flow +from sklearn.metrics import average_precision_score, roc_auc_score +from sklearn.preprocessing import StandardScaler +REPO = Path(__file__).resolve().parents[2] +IDS2017_FEATURES = ['Flow Duration', 'Total Fwd Packets', 'Total Backward Packets', 'Total Length of Fwd Packets', 'Total Length of Bwd Packets', 'Fwd Packet Length Min', 'Fwd Packet Length Mean', 'Fwd Packet Length Std', 'Bwd Packet Length Max', 'Bwd Packet Length Min', 'Bwd Packet Length Mean', 'Flow IAT Mean', 'Flow IAT Std', 'Flow IAT Max', 'Flow IAT Min', 'Fwd IAT Total', 'Fwd IAT Mean', 'Fwd IAT Std', 'Fwd IAT Max', 'Fwd IAT Min', 'Bwd IAT Total', 'Bwd IAT Mean', 'Bwd IAT Std', 'Bwd IAT Max', 'Bwd IAT Min', 'Fwd Header Length', 'Bwd Header Length', 'Fwd Packets/s', 'Bwd Packets/s', 'Min Packet Length', 'Max Packet Length', 'Packet Length Mean', 'Packet Length Std', 'Packet Length Variance', 'SYN Flag Count', 'PSH Flag Count', 'ACK Flag Count', 'URG Flag Count', 'Down/Up Ratio', 'Average Packet Size', 'Avg Fwd Segment Size', 'Avg Bwd Segment Size', 'Subflow Fwd Packets', 'Subflow Fwd Bytes', 'Subflow Bwd Packets', 'Subflow Bwd Bytes', 'Init_Win_bytes_forward', 'Init_Win_bytes_backward', 'act_data_pkt_fwd', 'min_seg_size_forward', 'Active Mean', 'Active Std', 'Active Max', 'Active Min', 'Idle Mean', 'Idle Std', 'Idle Max', 'Idle Min'] +TOR2016_FEATURES = ['Protocol', 'Flow Duration', 'Flow Bytes/s', 'Flow Packets/s', 'Flow IAT Mean', 'Flow IAT Std', 'Flow IAT Max', 'Flow IAT Min', 'Fwd IAT Mean', 'Fwd IAT Std', 'Fwd IAT Max', 'Fwd IAT Min', 'Bwd IAT Mean', 'Bwd IAT Std', 'Bwd IAT Max', 'Bwd IAT Min', 'Active Mean', 'Active Std', 'Active Max', 'Active Min', 'Idle Mean', 'Idle Std', 'Idle Max', 'Idle Min'] +CICIOT5_FEATURES = ['HTTPS', 'Protocol Type', 'Magnitude', 'Variance', 'fin_count'] +CICIDS_BEST5_FEATURES = ['Bwd Packet Length Mean', 'Fwd Packets/s', 'ACK Flag Count', 'Total Length of Bwd Packets', 'Flow Duration'] +TOR_BEST4_FEATURES = ['Flow IAT Std', 'Flow Bytes/s', 'Flow Packets/s', 'Bwd IAT Max'] +COLUMN_ALIASES = {'Total Fwd Packets': ['Total Fwd Packet'], 'Total Backward Packets': ['Total Bwd packets'], 'Total Length of Fwd Packets': ['Total Length of Fwd Packet'], 'Total Length of Bwd Packets': ['Total Length of Bwd Packet'], 'Fwd Header Length': ['Fwd Header Length.1'], 'Init_Win_bytes_forward': ['FWD Init Win Bytes', 'Init Win Bytes Fwd'], 'Init_Win_bytes_backward': ['Bwd Init Win Bytes', 'Init Win Bytes Bwd'], 'act_data_pkt_fwd': ['Fwd Act Data Pkts'], 'min_seg_size_forward': ['Fwd Seg Size Min'], 'Avg Fwd Segment Size': ['Fwd Segment Size Avg'], 'Avg Bwd Segment Size': ['Bwd Segment Size Avg'], 'Min Packet Length': ['Packet Length Min'], 'Max Packet Length': ['Packet Length Max']} +DATASETS = {'iscxtor': {'csv_glob': str(REPO / 'datasets/iscxtor2016/raw/csv/Scenario-*-merged_5s.csv'), 'label_col': 'label', 'benign_values': ['nonTOR'], 'drop_patterns': [], 'feature_set': TOR_BEST4_FEATURES}, 'cicids2017': {'csv_glob': str(REPO / 'datasets/cicids2017/raw/csv/*.csv'), 'label_col': 'Label', 'benign_values': ['BENIGN', 'Benign', 'benign'], 'drop_patterns': [' - Attempted', '- Attempted'], 'feature_set': CICIDS_BEST5_FEATURES}, 'cicddos2019': {'csv_glob': str(REPO / 'datasets/cicddos2019/raw/csv/**/*.csv'), 'label_col': 'Label', 'benign_values': ['BENIGN', 'Benign', 'benign'], 'drop_patterns': [], 'feature_set': CICIDS_BEST5_FEATURES}, 'ciciot2023': {'csv_glob': str(REPO / 'datasets/ciciot2023/raw/csv/CSV/*/*.pcap.csv'), 'label_col': None, 'benign_folder': 'Benign_Final', 'drop_patterns': [], 'feature_set': CICIOT5_FEATURES}} +PROTOCOL_CONFIG = {'iscxtor_within': ('iscxtor', 'iscxtor', {'n_train': 10000, 'n_val': 10000, 'n_attack': None}), 'cicids_within': ('cicids2017', 'cicids2017', {'n_train': 10000, 'n_val': 10000, 'n_attack': 30000}), 'cicddos_within': ('cicddos2019', 'cicddos2019', {'n_train': 10000, 'n_val': 10000, 'n_attack': 20000}), 'ciciot_within': ('ciciot2023', 'ciciot2023', {'n_train': 10000, 'n_val': 10000, 'n_attack': 30000}), 'forward_cross': ('cicids2017', 'cicddos2019', {'n_train': 10000, 'n_val': 10000, 'n_attack': 10000}), 'reverse_cross': ('cicddos2019', 'cicids2017', {'n_train': 10000, 'n_val': 10000, 'n_attack': 10000})} + +def _resolve_columns(df: pd.DataFrame, names: list[str]) -> tuple[list[str], list[str]]: + df.columns = [c.strip() if isinstance(c, str) else c for c in df.columns] + (resolved, missing) = ([], []) + for n in names: + if n in df.columns: + resolved.append(n) + continue + found = None + for alias in COLUMN_ALIASES.get(n, []): + if alias in df.columns: + found = alias + break + if found is None: + low = {c.lower(): c for c in df.columns} + if n.lower() in low: + found = low[n.lower()] + if found is None: + missing.append(n) + else: + resolved.append(found) + return (resolved, missing) + +def _load_csvs(dataset_name: str, return_paths: bool=False): + cfg = DATASETS[dataset_name] + paths = sorted(glob(cfg['csv_glob'], recursive=True)) + if not paths: + raise FileNotFoundError(f"no CSVs match {cfg['csv_glob']}") + print(f' [csv] {dataset_name}: {len(paths)} files') + return paths if return_paths else paths + +def _attach_labels(df: pd.DataFrame, dataset_name: str, source_path: str | None=None) -> pd.DataFrame: + cfg = DATASETS[dataset_name] + if cfg.get('label_col') is None: + folder = Path(source_path).parent.name + df = df.copy() + df['cls_label'] = folder + df['binary_label'] = 0 if folder == cfg['benign_folder'] else 1 + else: + lbl_col = cfg['label_col'].strip() + match = None + for c in df.columns: + if isinstance(c, str) and c.strip() == lbl_col: + match = c + break + if match is None: + raise KeyError(f'label column {lbl_col!r} not found in {source_path}') + df = df.copy() + df['cls_label'] = df[match].astype(str).str.strip() + for pat in cfg['drop_patterns']: + df = df[~df['cls_label'].str.contains(pat, na=False, regex=False)] + df['binary_label'] = df['cls_label'].apply(lambda x: 0 if x in cfg['benign_values'] else 1) + return df + +def _load_dataset(dataset_name: str, feature_set: list[str]) -> pd.DataFrame: + cfg = DATASETS[dataset_name] + paths = _load_csvs(dataset_name) + dfs = [] + for p in paths: + try: + df = pd.read_csv(p, low_memory=False) + except Exception as e: + print(f' [csv-warn] skip {p}: {e}') + continue + df = _attach_labels(df, dataset_name, source_path=p) + (resolved, missing) = _resolve_columns(df, feature_set) + if missing: + if not hasattr(_load_dataset, '_warned'): + _load_dataset._warned = set() + key = (dataset_name, tuple(missing)) + if key not in _load_dataset._warned: + _load_dataset._warned.add(key) + print(f' [warn] {Path(p).name}: missing {missing}') + sub = df[resolved + ['binary_label', 'cls_label']].copy() + rename = {r: n for (r, n) in zip(resolved, [f for f in feature_set if f not in missing])} + sub = sub.rename(columns=rename) + dfs.append(sub) + if not dfs: + raise RuntimeError(f'no usable CSVs for {dataset_name}') + full = pd.concat(dfs, axis=0, ignore_index=True) + for c in [c for c in feature_set if c in full.columns]: + full[c] = pd.to_numeric(full[c], errors='coerce') + full = full.replace([np.inf, -np.inf], np.nan) + feat_cols = [c for c in feature_set if c in full.columns] + full = full.dropna(subset=feat_cols).reset_index(drop=True) + print(f' [csv] {dataset_name} concat: {len(full):,} rows benign={int((full.binary_label == 0).sum()):,} attack={int((full.binary_label == 1).sum()):,} features_kept={len(feat_cols)}') + return (full, feat_cols) + +def _sample_within(df: pd.DataFrame, caps: dict, seed: int): + rng = np.random.default_rng(seed) + benign = df[df.binary_label == 0] + attack = df[df.binary_label == 1] + n_train = caps['n_train'] + n_val = caps['n_val'] + n_atk = caps['n_attack'] + needed_b = n_train + n_val + if len(benign) < needed_b: + raise RuntimeError(f'only {len(benign)} benign rows, need {needed_b}') + b_idx = rng.permutation(len(benign)) + train = benign.iloc[b_idx[:n_train]] + val = benign.iloc[b_idx[n_train:n_train + n_val]] + if n_atk is None: + atk = attack + else: + atk_classes = sorted(attack['cls_label'].unique()) + per = max(1, n_atk // len(atk_classes)) + chunks = [] + for cls in atk_classes: + pool = attack[attack['cls_label'] == cls] + k = min(per, len(pool)) + if k: + chunks.append(pool.sample(n=k, random_state=seed)) + atk = pd.concat(chunks, axis=0, ignore_index=True) + if len(atk) > n_atk: + atk = atk.sample(n=n_atk, random_state=seed) + return (train, val, atk) + +def _sample_cross(src_df, tgt_df, caps, seed): + rng = np.random.default_rng(seed + 1000) + src_benign = src_df[src_df.binary_label == 0] + if len(src_benign) < caps['n_train']: + raise RuntimeError(f"src benign only {len(src_benign)}, need {caps['n_train']}") + sb_idx = rng.permutation(len(src_benign)) + train = src_benign.iloc[sb_idx[:caps['n_train']]] + rng2 = np.random.default_rng(seed) + tgt_benign = tgt_df[tgt_df.binary_label == 0] + tgt_attack = tgt_df[tgt_df.binary_label == 1] + if len(tgt_benign) < caps['n_val']: + raise RuntimeError(f'tgt benign only {len(tgt_benign)}') + tb_idx = rng2.permutation(len(tgt_benign)) + val = tgt_benign.iloc[tb_idx[:caps['n_val']]] + atk_classes = sorted(tgt_attack['cls_label'].unique()) + per = max(1, caps['n_attack'] // len(atk_classes)) + chunks = [] + for cls in atk_classes: + pool = tgt_attack[tgt_attack['cls_label'] == cls] + k = min(per, len(pool)) + if k: + chunks.append(pool.sample(n=k, random_state=seed)) + atk = pd.concat(chunks, axis=0, ignore_index=True) + if len(atk) > caps['n_attack']: + atk = atk.sample(n=caps['n_attack'], random_state=seed) + return (train, val, atk) + +def _safe_metric(fn, y, s) -> float: + s = np.nan_to_num(s, nan=0.0, posinf=1000000000000.0, neginf=-1000000000000.0) + try: + return float(fn(y, s)) + except ValueError: + return float('nan') + +def _train_and_score(train, val, atk, feat_cols, *, epochs, lr, optimizer): + raw_train = train[feat_cols].astype(np.float64).values + keep = raw_train.std(axis=0) > 0 + if not keep.all(): + dropped = [c for (c, k) in zip(feat_cols, keep) if not k] + print(f' [train] dropping {len(dropped)} zero-variance cols: {dropped}') + feat_cols = [c for (c, k) in zip(feat_cols, keep) if k] + raw_train = raw_train[:, keep] + raw_val = val[feat_cols].astype(np.float64).values + raw_atk = atk[feat_cols].astype(np.float64).values + scaler = StandardScaler() + X_train = scaler.fit_transform(raw_train) + X_val = scaler.transform(raw_val) + X_atk = scaler.transform(raw_atk) + clip_lim = 30.0 + X_train = np.clip(X_train, -clip_lim, clip_lim) + X_val = np.clip(X_val, -clip_lim, clip_lim) + X_atk = np.clip(X_atk, -clip_lim, clip_lim) + df_train = pd.DataFrame(X_train.astype(np.float32), columns=[f'x{i}' for i in range(len(feat_cols))]) + df_val = pd.DataFrame(X_val.astype(np.float32), columns=df_train.columns) + df_atk = pd.DataFrame(X_atk.astype(np.float32), columns=df_train.columns) + if optimizer == 'sgd': + opt = optax.sgd(learning_rate=lr) + else: + opt = optax.adam(learning_rate=lr) + flow = Flow(df_train.columns.tolist()) + t0 = time.time() + losses = flow.train(df_train, optimizer=opt, epochs=epochs, verbose=False) + t_train = time.time() - t0 + t0 = time.time() + lp_val = np.asarray(flow.log_prob(df_val)) + lp_atk = np.asarray(flow.log_prob(df_atk)) + t_score = time.time() - t0 + return {'score_val': (-lp_val).astype(np.float32), 'score_atk': (-lp_atk).astype(np.float32), 'losses': np.asarray(losses, dtype=np.float64), 't_train': t_train, 't_score': t_score} + +def _per_class(val_score, atk_score, atk_labels): + out = {} + for cls in sorted(set(atk_labels)): + m = atk_labels == cls + n_c = int(m.sum()) + v_c = atk_score[m] + y = np.r_[np.zeros(len(val_score)), np.ones(len(v_c))] + s = np.r_[val_score, v_c] + out[cls] = {'_n': float(n_c), 'auroc': _safe_metric(roc_auc_score, y, s)} + return out + +def main(): + p = argparse.ArgumentParser() + p.add_argument('--protocol', required=True, choices=list(PROTOCOL_CONFIG)) + p.add_argument('--seed', type=int, required=True, choices=[42, 43, 44]) + p.add_argument('--out-dir', type=Path, required=True) + p.add_argument('--epochs', type=int, default=100) + p.add_argument('--lr', type=float, default=0.001) + p.add_argument('--optimizer', choices=['sgd', 'adam'], default='sgd') + args = p.parse_args() + args.out_dir.mkdir(parents=True, exist_ok=True) + (src_name, tgt_name, caps) = PROTOCOL_CONFIG[args.protocol] + cross = src_name != tgt_name + print(f'[run] shafir_nf_csv protocol={args.protocol} seed={args.seed}') + print(f' src={src_name} tgt={tgt_name} cross={cross}') + feat_set = DATASETS[src_name]['feature_set'] + (src_df, src_feat_cols) = _load_dataset(src_name, feat_set) + if cross: + (tgt_df, tgt_feat_cols) = _load_dataset(tgt_name, feat_set) + feat_cols = [c for c in feat_set if c in src_feat_cols and c in tgt_feat_cols] + print(f' [features] cross intersection: {len(feat_cols)} cols') + (train, val, atk) = _sample_cross(src_df, tgt_df, caps, args.seed) + else: + feat_cols = src_feat_cols + print(f' [features] within: {len(feat_cols)} cols') + (train, val, atk) = _sample_within(src_df, caps, args.seed) + print(f' [data] train={len(train):,} val={len(val):,} attack={len(atk):,} D={len(feat_cols)}') + res = _train_and_score(train, val, atk, feat_cols, epochs=args.epochs, lr=args.lr, optimizer=args.optimizer) + (val_score, atk_score) = (res['score_val'], res['score_atk']) + y = np.r_[np.zeros(len(val_score)), np.ones(len(atk_score))] + s = np.r_[val_score, atk_score] + overall = {'neg_log_prob': {'auroc': _safe_metric(roc_auc_score, y, s), 'auprc': _safe_metric(average_precision_score, y, s)}} + a_labels = atk['cls_label'].astype(str).to_numpy() + per_cls = _per_class(val_score, atk_score, a_labels) + out = {'method': 'shafir_nf_csv', 'protocol': args.protocol, 'seed': args.seed, 'src_dataset': src_name, 'tgt_dataset': tgt_name, 'feature_set': feat_cols, 'n_features': len(feat_cols), 'n_train': len(train), 'n_val': len(val), 'n_atk': len(atk), 'epochs': args.epochs, 'lr': args.lr, 'optimizer': args.optimizer, 't_train_sec': round(res['t_train'], 2), 't_score_sec': round(res['t_score'], 2), 'loss_first_last': [float(res['losses'][0]), float(res['losses'][-1])], 'overall': overall, 'per_class': per_cls} + out_json = args.out_dir / f'{args.protocol}_seed{args.seed}.json' + out_json.write_text(json.dumps(out, indent=2)) + npz_path = out_json.with_suffix('.npz') + np.savez_compressed(npz_path, b_neg_log_prob=val_score, a_neg_log_prob=atk_score, a_labels=a_labels.astype(str), losses=res['losses']) + print(f'[saved] {out_json}') + print(f"[result] AUROC={overall['neg_log_prob']['auroc']:.4f} AUPRC={overall['neg_log_prob']['auprc']:.4f} train={res['t_train']:.1f}s") +if __name__ == '__main__': + main() diff --git a/scripts/baselines/run_shafir_nf_csv_all.sh b/scripts/baselines/run_shafir_nf_csv_all.sh new file mode 100755 index 0000000..9b7132e --- /dev/null +++ b/scripts/baselines/run_shafir_nf_csv_all.sh @@ -0,0 +1,37 @@ +#!/usr/bin/env bash +set -euo pipefail +REPO=$(cd "$(dirname "$0")/../.." && pwd) +cd "$REPO" + +OUT_DIR="artifacts/baselines/shafir_nf_csv_2026_04_29" +mkdir -p "$OUT_DIR" +LOG="$OUT_DIR/master.log" +: > "$LOG" + +PROTOCOLS_DEFAULT="iscxtor_within cicids_within cicddos_within ciciot_within forward_cross reverse_cross" +SEEDS_DEFAULT="42 43 44" +PROTOCOLS="${PROTOCOLS:-$PROTOCOLS_DEFAULT}" +SEEDS="${SEEDS:-$SEEDS_DEFAULT}" +EPOCHS="${EPOCHS:-100}" +LR="${LR:-0.001}" +OPTIMIZER="${OPTIMIZER:-sgd}" + +for protocol in $PROTOCOLS; do + for seed in $SEEDS; do + out_json="$OUT_DIR/${protocol}_seed${seed}.json" + if [[ -f "$out_json" ]]; then + echo "[skip] $out_json exists" | tee -a "$LOG" + continue + fi + echo "=== protocol=$protocol seed=$seed epochs=$EPOCHS opt=$OPTIMIZER lr=$LR ===" | tee -a "$LOG" + ts=$(date +%s) + uv run --no-sync python scripts/baselines/run_shafir_nf_csv.py \ + --protocol "$protocol" --seed "$seed" \ + --out-dir "$OUT_DIR" \ + --epochs "$EPOCHS" --lr "$LR" --optimizer "$OPTIMIZER" \ + 2>&1 | tee -a "$LOG" + te=$(date +%s) + echo "[done] elapsed=$((te-ts))s $out_json" | tee -a "$LOG" + done +done +echo "ALL DONE" diff --git a/scripts/compute_shafir5_features.py b/scripts/compute_shafir5_features.py new file mode 100644 index 0000000..f195de8 --- /dev/null +++ b/scripts/compute_shafir5_features.py @@ -0,0 +1,87 @@ +from __future__ import annotations +import argparse +import sys +import time +from pathlib import Path +import numpy as np +import pandas as pd +REPO = Path(__file__).resolve().parents[1] +sys.path.insert(0, str(REPO / 'Packet_CFM')) +from packet_store import PacketShardStore +SHAFIR5_FEATURE_NAMES = ('HTTPS', 'Protocol_Type', 'Magnitude', 'Variance', 'fin_count') + +def _compute_batch(tokens: np.ndarray, lens: np.ndarray, dst_ports: np.ndarray, protocols: np.ndarray) -> np.ndarray: + (B, T, _) = tokens.shape + out = np.zeros((B, 5), dtype=np.float32) + arange = np.arange(T)[None, :] + mask = arange < lens[:, None] + log_size = tokens[:, :, 0] + sizes = np.expm1(np.maximum(log_size, 0.0)) + sizes = np.where(mask, sizes, 0.0) + n = lens.astype(np.float32) + n_safe = np.maximum(n, 1.0) + sum_sq = (sizes * sizes).sum(axis=1) + mean = sizes.sum(axis=1) / n_safe + mean_sq = sum_sq / n_safe + magnitude = np.sqrt(np.maximum(mean_sq, 0.0)) + variance = np.maximum(mean_sq - mean * mean, 0.0) + fin_flags = tokens[:, :, 4] + fin_flags = np.where(mask, fin_flags, 0.0) + fin_count = fin_flags.sum(axis=1) + https = (dst_ports == 443).astype(np.float32) + proto_type = protocols.astype(np.float32) + out[:, 0] = https + out[:, 1] = proto_type + out[:, 2] = magnitude + out[:, 3] = variance + out[:, 4] = fin_count + return out + +def main(): + p = argparse.ArgumentParser() + p.add_argument('--source-store', type=Path, required=True) + p.add_argument('--flows-parquet', type=Path, required=True) + p.add_argument('--out', type=Path, required=True) + p.add_argument('--T', type=int, default=None, help='Truncate to first T packets (default = stored).') + p.add_argument('--batch', type=int, default=100000) + args = p.parse_args() + print(f'[read] {args.flows_parquet}') + flows = pd.read_parquet(args.flows_parquet, columns=['flow_id', 'label', 'dst_port', 'protocol']) + flow_id = flows['flow_id'].to_numpy(dtype=np.uint64) + labels = flows['label'].astype(str).to_numpy() + dst_ports = flows['dst_port'].to_numpy(dtype=np.uint32) + protocols = flows['protocol'].to_numpy(dtype=np.uint8) + store = PacketShardStore.open(args.source_store) + store_fid = store.read_flows(columns=['flow_id'])['flow_id'].to_numpy(dtype=np.uint64) + if len(store_fid) != len(flow_id) or not np.array_equal(store_fid, flow_id): + raise ValueError('store flow_id ordering differs from flows.parquet') + T_stored = int(store.manifest['packet_length'].max()) + T = args.T if args.T is not None else T_stored + n = len(flows) + feats = np.zeros((n, 5), dtype=np.float32) + print(f'[stream] {n:,} flows × T={T} (stored {T_stored}), batch={args.batch}') + t0 = time.time() + for start in range(0, n, args.batch): + end = min(start + args.batch, n) + idx = np.arange(start, end, dtype=np.int64) + (tok, ll) = store.read_packets(idx, T=T) + ll = np.minimum(ll, T).astype(np.int32) + feats[start:end] = _compute_batch(tok.astype(np.float32), ll, dst_ports[start:end], protocols[start:end]) + if start // args.batch % 20 == 0 or end == n: + dt = time.time() - t0 + rate = end / max(dt, 1e-06) + eta = (n - end) / max(rate, 1.0) + print(f'[stream] {end:,}/{n:,} dt={dt:.1f}s rate={rate:.0f} flows/s ETA={eta:.0f}s', flush=True) + args.out.parent.mkdir(parents=True, exist_ok=True) + df = pd.DataFrame({'flow_id': flow_id, 'label': labels}) + for (i, name) in enumerate(SHAFIR5_FEATURE_NAMES): + df[name] = feats[:, i] + df.to_parquet(args.out, compression='snappy', index=False) + print(f'[write] {args.out} rows={len(df):,} cols={list(df.columns)}') + print(f'[stats] HTTPS=1 fraction: {(feats[:, 0] > 0).mean():.4f}') + print(f'[stats] Protocol_Type unique values: {np.unique(feats[:, 1].astype(int))[:10]}') + print(f'[stats] Magnitude mean={feats[:, 2].mean():.1f} median={np.median(feats[:, 2]):.1f}') + print(f'[stats] Variance mean={feats[:, 3].mean():.1f}') + print(f'[stats] fin_count mean={feats[:, 4].mean():.3f}') +if __name__ == '__main__': + main() diff --git a/scripts/convert_npz_splits_to_store.py b/scripts/convert_npz_splits_to_store.py new file mode 100644 index 0000000..8fbbe1b --- /dev/null +++ b/scripts/convert_npz_splits_to_store.py @@ -0,0 +1,119 @@ +from __future__ import annotations +import argparse +import sys +import zipfile +from pathlib import Path +from typing import BinaryIO +import numpy as np +import pandas as pd +from numpy.lib import format as npy_format +sys.path.insert(0, str(Path(__file__).resolve().parents[1])) +from packet_store import PacketShardStore, PacketShardWriter + +def _read_npy_header(fp: BinaryIO) -> tuple[tuple[int, ...], np.dtype, bool]: + version = npy_format.read_magic(fp) + if version == (1, 0): + (shape, fortran_order, dtype) = npy_format.read_array_header_1_0(fp) + elif version == (2, 0): + (shape, fortran_order, dtype) = npy_format.read_array_header_2_0(fp) + else: + raise ValueError(f'unsupported npy version {version}') + return (tuple((int(v) for v in shape)), np.dtype(dtype), bool(fortran_order)) + +def _read_exact(fp: BinaryIO, n_bytes: int) -> bytes: + chunks: list[bytes] = [] + remaining = int(n_bytes) + while remaining: + chunk = fp.read(remaining) + if not chunk: + raise EOFError(f'expected {n_bytes} bytes, missing {remaining}') + chunks.append(chunk) + remaining -= len(chunk) + return b''.join(chunks) + +def _open_member(zf: zipfile.ZipFile, name: str) -> tuple[BinaryIO, tuple[int, ...], np.dtype]: + fp = zf.open(name) + (shape, dtype, fortran_order) = _read_npy_header(fp) + if fortran_order: + fp.close() + raise ValueError(f'{name} uses Fortran order, expected C order') + return (fp, shape, dtype) + +def _iter_npz_rows(npz_path: Path, rows: int, chunk_rows: int): + with zipfile.ZipFile(npz_path) as zf: + (token_fp, token_shape, token_dtype) = _open_member(zf, 'packet_tokens.npy') + (length_fp, length_shape, length_dtype) = _open_member(zf, 'packet_lengths.npy') + try: + if len(token_shape) != 3: + raise ValueError(f'packet_tokens.npy must be 3-D, got {token_shape}') + if length_shape != (token_shape[0],): + raise ValueError(f'packet_lengths.npy shape {length_shape} does not match tokens {token_shape}') + if rows > token_shape[0]: + raise ValueError(f'requested {rows} rows, but {npz_path} has {token_shape[0]}') + row_values = int(np.prod(token_shape[1:], dtype=np.int64)) + token_row_bytes = row_values * token_dtype.itemsize + length_row_bytes = length_dtype.itemsize + emitted = 0 + while emitted < rows: + take = min(int(chunk_rows), rows - emitted) + token_bytes = _read_exact(token_fp, take * token_row_bytes) + length_bytes = _read_exact(length_fp, take * length_row_bytes) + tokens = np.frombuffer(token_bytes, dtype=token_dtype).reshape(take, token_shape[1], token_shape[2]) + lengths = np.frombuffer(length_bytes, dtype=length_dtype).reshape(take) + yield (emitted, tokens, lengths) + emitted += take + finally: + token_fp.close() + length_fp.close() + +def _npz_token_shape(npz_path: Path) -> tuple[int, int, int]: + with zipfile.ZipFile(npz_path) as zf: + (fp, shape, _dtype) = _open_member(zf, 'packet_tokens.npy') + fp.close() + if len(shape) != 3: + raise ValueError(f'packet_tokens.npy must be 3-D, got {shape}') + return shape + +def convert(args: argparse.Namespace) -> None: + pairs = list(zip(args.packets_npz, args.flows_parquet, strict=True)) + first_shape = _npz_token_shape(pairs[0][0]) + total_rows = 0 + with PacketShardWriter(args.out_store, shard_size=args.shard_size, T_full=first_shape[1], D=first_shape[2], overwrite=args.overwrite) as writer: + for (split_id, (npz_path, flows_path)) in enumerate(pairs): + token_shape = _npz_token_shape(npz_path) + if token_shape[1:] != first_shape[1:]: + raise ValueError(f'{npz_path} shape {token_shape} does not match {first_shape}') + flows = pd.read_parquet(flows_path) + rows = min(len(flows), token_shape[0]) + if args.max_rows_per_split > 0: + rows = min(rows, args.max_rows_per_split) + if len(flows) != token_shape[0]: + raise ValueError(f'{flows_path} has {len(flows)} rows but {npz_path} has {token_shape[0]}') + print(f'[split {split_id}] npz={npz_path} flows={flows_path} rows={rows:,} shape={token_shape}', flush=True) + for (start, tokens, lengths) in _iter_npz_rows(npz_path, rows, args.chunk_rows): + end = start + len(lengths) + writer.add_batch(tokens, lengths, flows.iloc[start:end].reset_index(drop=True)) + total_rows += len(lengths) + if total_rows % args.report_every < len(lengths) or end == rows: + print(f'[split {split_id}] emitted={end:,}/{rows:,} total={total_rows:,}', flush=True) + store = PacketShardStore.open(args.out_store) + flows = store.read_flows(columns=['label']) + print(f"[done] store={args.out_store} rows={store.n_flows:,} shards={store.metadata['n_shards']}") + print(flows['label'].value_counts().to_string()) + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument('--packets-npz', type=Path, nargs='+', required=True) + parser.add_argument('--flows-parquet', type=Path, nargs='+', required=True) + parser.add_argument('--out-store', type=Path, required=True) + parser.add_argument('--shard-size', type=int, default=50000) + parser.add_argument('--chunk-rows', type=int, default=10000) + parser.add_argument('--report-every', type=int, default=250000) + parser.add_argument('--max-rows-per-split', type=int, default=0) + parser.add_argument('--overwrite', action='store_true') + args = parser.parse_args() + if len(args.packets_npz) != len(args.flows_parquet): + raise SystemExit('--packets-npz and --flows-parquet must have the same count') + convert(args) +if __name__ == '__main__': + main() diff --git a/scripts/csv_adapter.py b/scripts/csv_adapter.py new file mode 100644 index 0000000..9f9bc05 --- /dev/null +++ b/scripts/csv_adapter.py @@ -0,0 +1,114 @@ +from __future__ import annotations +import csv +import sys +from dataclasses import dataclass, field +from datetime import datetime +from pathlib import Path +from typing import Callable +import numpy as np +sys.path.insert(0, str(Path(__file__).resolve().parent)) +from extract_lib import _canonical_key + +@dataclass(frozen=True) +class CsvFlowAdapter: + join_cols: dict[str, str] + label_col: str + timestamp_formats: tuple[str, ...] + benign_aliases: frozenset[str] + benign_token: str = 'normal' + drop_label_patterns: tuple[str, ...] = () + label_aliases: dict[str, str] = field(default_factory=dict) + label_normalizer: Callable[[str], str] | None = None + + def normalize_label(self, raw: str) -> str: + if self.label_normalizer is not None: + return self.label_normalizer(raw) + s = raw.strip() + if s in self.benign_aliases: + return self.benign_token + return self.label_aliases.get(s, s) + + def parse_timestamp(self, raw: str) -> float | None: + s = raw.strip() + if not s: + return None + for fmt in self.timestamp_formats: + try: + return datetime.strptime(s, fmt).timestamp() + except ValueError: + continue + return None + +def parse_csv_rows(*, csv_path: Path, row_idx_start: int, time_offset_seconds: float, adapter: CsvFlowAdapter, max_per_class: int | None=None, max_benign: int | None=None, rng: np.random.Generator | None=None) -> tuple[dict[tuple, list[tuple[int, float]]], list[str], int, int, dict[str, int]]: + if (max_per_class is not None or max_benign is not None) and rng is None: + rng = np.random.default_rng(42) + parsed: list[tuple[tuple, float, str]] = [] + n_skip = 0 + with open(csv_path, 'r', newline='') as f: + reader = csv.reader(f) + header = [h.strip() for h in next(reader)] + h2i = {h: i for (i, h) in enumerate(header)} + needed = list(adapter.join_cols.values()) + [adapter.label_col] + for col in needed: + if col not in h2i: + raise KeyError(f'{csv_path.name}: missing column {col!r}') + i_src_ip = h2i[adapter.join_cols['src_ip']] + i_src_port = h2i[adapter.join_cols['src_port']] + i_dst_ip = h2i[adapter.join_cols['dst_ip']] + i_dst_port = h2i[adapter.join_cols['dst_port']] + i_proto = h2i[adapter.join_cols['protocol']] + i_ts = h2i[adapter.join_cols['timestamp']] + i_label = h2i[adapter.label_col] + for row in reader: + if not row: + continue + try: + raw_label = row[i_label] + except IndexError: + n_skip += 1 + continue + if any((pat in raw_label for pat in adapter.drop_label_patterns)): + n_skip += 1 + continue + try: + sp = int(float(row[i_src_port])) if row[i_src_port].strip() else 0 + dp = int(float(row[i_dst_port])) if row[i_dst_port].strip() else 0 + proto = int(float(row[i_proto])) if row[i_proto].strip() else 0 + except (ValueError, IndexError): + n_skip += 1 + continue + sip = row[i_src_ip].strip() + dip = row[i_dst_ip].strip() + ck = _canonical_key(sip, dip, sp, dp, proto) + ts_parsed = adapter.parse_timestamp(row[i_ts]) + ts_epoch = float('nan') if ts_parsed is None else ts_parsed + time_offset_seconds + parsed.append((ck, ts_epoch, adapter.normalize_label(raw_label))) + keep_idx = _select_indices(labels=[p[2] for p in parsed], benign_token=adapter.benign_token, max_per_class=max_per_class, max_benign=max_benign, rng=rng) + rows_by_key: dict[tuple, list[tuple[int, float]]] = {} + labels_out: list[str] = [] + class_counts: dict[str, int] = {} + row_idx = row_idx_start + for i in keep_idx: + (ck, ts_epoch, label) = parsed[i] + rows_by_key.setdefault(ck, []).append((row_idx, ts_epoch)) + labels_out.append(label) + class_counts[label] = class_counts.get(label, 0) + 1 + row_idx += 1 + return (rows_by_key, labels_out, row_idx - row_idx_start, n_skip, class_counts) + +def _select_indices(*, labels: list[str], benign_token: str, max_per_class: int | None, max_benign: int | None, rng: np.random.Generator | None) -> list[int]: + if max_per_class is None and max_benign is None: + return list(range(len(labels))) + assert rng is not None + buckets: dict[str, list[int]] = {} + for (i, label) in enumerate(labels): + buckets.setdefault(label, []).append(i) + keep: list[int] = [] + for (label, idxs) in buckets.items(): + cap = max_benign if label == benign_token else max_per_class + if cap is not None and len(idxs) > cap: + pick = rng.choice(len(idxs), size=cap, replace=False) + idxs = [idxs[j] for j in sorted(pick)] + keep.extend(idxs) + keep.sort() + return keep diff --git a/scripts/download/README.md b/scripts/download/README.md new file mode 100644 index 0000000..fb0a5ea --- /dev/null +++ b/scripts/download/README.md @@ -0,0 +1,112 @@ +# Dataset download scripts + +Target layout (mirrors `datasets/cicids2017/`): + +``` +datasets/ + ciciot2023/raw/{pcap,csv} + iscxtor2016/raw/{pcap,csv} + cicapt_iiot2024/raw/{pcap,csv} + ustc_tfc2016/raw/pcap + datacon2020/raw/pcap +``` + +## CICIoT2023 / ISCXTor2016 (automated) + +UNB/CIC gates downloads behind a consent form. After submission the site issues +a `Token` cookie (domain `.cicresearch.ca`) that unlocks two endpoints: + +- `browse.php?p=` — HTML directory listing +- `download.php?file=` — raw file bytes + +`cic_download.py` is a stdlib-only recursive crawler that walks `browse.php` +and fetches each leaf via `download.php`. Already-downloaded files are +skipped (presence-based; the PHP endpoint does not advertise sizes). + +### Workflow + +1. Open the dataset page in a browser, fill and submit the form: + - CICIoT2023 : + - ISCXTor2016: +2. After submit, click through to `cicresearch.ca/.../browse.php`. The page + must load successfully in your browser — this proves the Token is set. +3. Export the cookie in **Netscape format** (tab-separated). One line is + sufficient: + + ``` + # Netscape HTTP Cookie File + .cicresearch.ca TRUE / TRUE Token + ``` + + Save as: + - `scripts/download/cookies_ciciot2023.txt` + - `scripts/download/cookies_iscxtor2016.txt` + + Tokens are per-dataset — a CICIoT2023 cookie will not work for ISCXTor. +4. Run: + + ```bash + bash scripts/download/download_ciciot2023.sh + bash scripts/download/download_iscxtor2016.sh + ``` + + Env vars: `WHAT=pcap|csv|both`, `DEST=`, `COOKIES=`, `DRY_RUN=1`, `LIMIT=N`. + For ISCXTor, if the remote subdir names differ from the defaults + (`Pcaps` / `CSVs`), set `PCAP_ROOT=` / `CSV_ROOT=`. + +### Known remote tree sizes + +- **CICIoT2023** — `CSV/` 328 files (includes `CSV.zip`, `MERGED_CSV.zip`, + `MERGED_CSV/`, and per-attack CSVs), `PCAP/` 311 files across 36 attack + categories. Full dataset is ~12 GB. + +### Quick commands + +```bash +# Dry-run (enumerate only, no downloads) +DRY_RUN=1 bash scripts/download/download_ciciot2023.sh + +# Download first 5 files as a smoke test +LIMIT=5 WHAT=csv bash scripts/download/download_ciciot2023.sh + +# Full download +bash scripts/download/download_ciciot2023.sh +``` + +## CICAPT-IIoT2024 (automated) + +Same UNB/CIC pipeline as CICIoT2023, but crawled in a single pass — the +entire `CICAPT-IIoT Dataset/` top-level folder is mirrored (pcap, csv, and +anything else) under `datasets/cicapt_iiot2024/raw/`. + +Cookie file: `scripts/download/cookies_cicapt_iiot2024.txt` (Token for +`.cicresearch.ca`). + +```bash +# Smoke test first +DRY_RUN=1 LIMIT=5 bash scripts/download/download_cicapt_iiot2024.sh + +# Full download +bash scripts/download/download_cicapt_iiot2024.sh + +# Skip heavy archives if they duplicate a per-file tree +SKIP_EXT=zip,7z bash scripts/download/download_cicapt_iiot2024.sh +``` + +Reference URL (browser, with Token cookie live): + + +## USTC-TFC2016 (manual) + +```bash +cd datasets/ustc_tfc2016/raw/pcap +git clone --depth=1 https://github.com/yungshenglu/USTC-TFC2016.git . +``` + +No official CSV — extract features yourself (CICFlowMeter, USTC-TK2016). + +## DataCon2020 (manual) + +Register at and place +the `black/` `white/` `test/` pcap bundles under +`datasets/datacon2020/raw/pcap/`. No official CSV. diff --git a/scripts/download/_run_ciciot2023_pcap_loop.sh b/scripts/download/_run_ciciot2023_pcap_loop.sh new file mode 100755 index 0000000..12678e5 --- /dev/null +++ b/scripts/download/_run_ciciot2023_pcap_loop.sh @@ -0,0 +1,54 @@ +#!/usr/bin/env bash +# Background wrapper: retry CICIoT2023 PCAP download until it reports +# a clean "Done." with n_files > 0. Each attempt is delimited in the log +# so the monitor can grep for progress. +# +# Invoked detached (nohup ... &). The inner script is resumable via +# the .part-file convention in cic_download.py. + +set -uo pipefail + +SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" +REPO_ROOT="$(cd "${SCRIPT_DIR}/../.." && pwd)" +LOG="${REPO_ROOT}/logs/ciciot2023_pcap.log" + +# nohup strips the interactive PATH; re-expose the project venv so +# `python` resolves inside download_ciciot2023.sh. +if [[ -x "${REPO_ROOT}/.venv/bin/python" ]]; then + export PATH="${REPO_ROOT}/.venv/bin:${PATH:-/usr/local/bin:/usr/bin:/bin}" +fi + +# Route through the local proxy; detached bash does not inherit the +# interactive shell's proxy env, and cicresearch.ca's WAF rate-limits +# bare-IP traffic much more aggressively than the proxy exit. +export HTTP_PROXY="http://127.0.0.1:7093" +export HTTPS_PROXY="http://127.0.0.1:7093" +export ALL_PROXY="socks5h://127.0.0.1:7093" +export NO_PROXY="localhost,127.0.0.1,::1" +export http_proxy="${HTTP_PROXY}" +export https_proxy="${HTTPS_PROXY}" +export all_proxy="${ALL_PROXY}" +export no_proxy="${NO_PROXY}" + +i=0 +while :; do + i=$((i + 1)) + ts=$(date +%F\ %T) + printf '\n=== attempt %d %s ===\n' "$i" "$ts" >>"$LOG" + # Skip bundle zips (e.g. PCAP.zip) — we want per-attack-class .pcap files, + # not the whole dataset as one archive. + WHAT=pcap SKIP_EXT="zip,7z" bash "${SCRIPT_DIR}/download_ciciot2023.sh" >>"$LOG" 2>&1 + rc=$? + # If inner script exited with 0 AND last "Done." line reports >0 files, + # we consider the listing+walk to have succeeded at least once. Otherwise + # keep retrying on network/SSL failures. + last_done=$(grep -E '^Done\. [0-9]+ files processed' "$LOG" | tail -1 || true) + n=$(printf '%s' "$last_done" | awk '{print $2}') + if [[ "$rc" -eq 0 && -n "$n" && "$n" -gt 0 ]]; then + printf '=== loop finished clean %s (files=%s) ===\n' "$(date +%F\ %T)" "$n" >>"$LOG" + break + fi + printf '=== attempt %d ended rc=%s last_done=%q; sleep 60 ===\n' \ + "$i" "$rc" "$last_done" >>"$LOG" + sleep 60 +done diff --git a/scripts/download/cic_download.py b/scripts/download/cic_download.py new file mode 100644 index 0000000..2a5281f --- /dev/null +++ b/scripts/download/cic_download.py @@ -0,0 +1,185 @@ +from __future__ import annotations +import argparse +import http.cookiejar +import re +import sys +import time +import urllib.error +import urllib.parse +import urllib.request +from pathlib import Path +UA = 'Mozilla/5.0 (cic-downloader)' +LINK_RE = re.compile('href="(browse\\.php\\?p=[^"]+|download\\.php\\?file=[^"]+)"') + +def build_opener(cookies_path: Path) -> urllib.request.OpenerDirector: + jar = http.cookiejar.MozillaCookieJar() + jar.load(str(cookies_path), ignore_discard=True, ignore_expires=True) + return urllib.request.build_opener(urllib.request.HTTPCookieProcessor(jar)) + +def http_get(opener, url: str, timeout: int=60, retries: int=5) -> bytes: + last: Exception | None = None + for attempt in range(retries): + try: + req = urllib.request.Request(url, headers={'User-Agent': UA}) + with opener.open(req, timeout=timeout) as resp: + final = resp.geturl() + if 'unb.ca/cic/datasets' in final: + raise RuntimeError(f'Got redirected to UNB form page ({final}). Token cookie is missing/expired or wrong dataset scope.') + return resp.read() + except RuntimeError: + raise + except Exception as e: + last = e + wait = min(30, 2 ** attempt) + print(f' WARN GET {url} failed ({e!r}); retry in {wait}s ({attempt + 1}/{retries})', file=sys.stderr) + time.sleep(wait) + raise RuntimeError(f'GET {url} failed after {retries} attempts: {last!r}') + +def list_dir(opener, base: str, p: str) -> list[tuple[str, str]]: + url = urllib.parse.urljoin(base, 'browse.php') + '?p=' + urllib.parse.quote(p, safe='/') + html = http_get(opener, url).decode('utf-8', 'replace') + out: list[tuple[str, str]] = [] + for m in LINK_RE.finditer(html): + href = m.group(1) + qs = urllib.parse.parse_qs(urllib.parse.urlparse(href).query) + if href.startswith('browse.php'): + out.append(('dir', qs['p'][0])) + else: + out.append(('file', qs['file'][0])) + return out + +def walk(opener, base: str, root: str): + stack = [root] + seen: set[str] = set() + while stack: + p = stack.pop() + if p in seen: + continue + seen.add(p) + try: + entries = list_dir(opener, base, p) + except Exception as e: + print(f' WARN list_dir({p}) failed permanently: {e!r}', file=sys.stderr) + continue + for (kind, val) in sorted(entries): + if kind == 'dir': + stack.append(val) + else: + yield val + +def download_file(opener, base: str, remote: str, dest_root: Path, *, root_prefix: str) -> None: + url = urllib.parse.urljoin(base, 'download.php') + '?file=' + urllib.parse.quote(remote, safe='') + rel = remote[len(root_prefix):].lstrip('/') if remote.startswith(root_prefix) else remote + local = dest_root / rel + local.parent.mkdir(parents=True, exist_ok=True) + if local.exists() and local.stat().st_size > 0: + print(f' SKIP {rel} ({local.stat().st_size} bytes, already present)') + return + tmp = local.with_suffix(local.suffix + '.part') + last: Exception | None = None + for attempt in range(5): + resume_from = tmp.stat().st_size if tmp.exists() else 0 + try: + headers = {'User-Agent': UA} + if resume_from > 0: + headers['Range'] = f'bytes={resume_from}-' + req = urllib.request.Request(url, headers=headers) + t0 = time.monotonic() + bytes_read = 0 + with opener.open(req, timeout=1800) as resp: + final = resp.geturl() + if 'unb.ca/cic/datasets' in final: + raise RuntimeError('Token cookie invalid mid-download.') + status = getattr(resp, 'status', None) + mode = 'ab' + if resume_from <= 0: + mode = 'wb' + elif status != 206: + print(f' INFO {rel} resume request ignored (status={status}); restarting from zero') + resume_from = 0 + mode = 'wb' + with open(tmp, mode) as fh: + while True: + buf = resp.read(1 << 20) + if not buf: + break + fh.write(buf) + bytes_read += len(buf) + tmp.replace(local) + dt = time.monotonic() - t0 + total_bytes = local.stat().st_size + mb = total_bytes / (1 << 20) + delta_mb = bytes_read / (1 << 20) + rate = mb / dt if dt > 0 else 0 + if resume_from > 0: + resumed_mb = resume_from / (1 << 20) + rate = delta_mb / dt if dt > 0 else 0 + print(f' GOT {rel} {mb:.1f} MB +{delta_mb:.1f} MB from {resumed_mb:.1f} MB {rate:.1f} MB/s') + else: + print(f' GOT {rel} {mb:.1f} MB {rate:.1f} MB/s') + return + except urllib.error.HTTPError as e: + last = e + if e.code == 416 and resume_from > 0: + print(f' WARN {rel} resume rejected with 416; restarting from zero', file=sys.stderr) + try: + tmp.unlink(missing_ok=True) + except OSError: + pass + time.sleep(1) + continue + wait = min(30, 2 ** attempt) + print(f' WARN {rel} failed ({e!r}); retry in {wait}s ({attempt + 1}/5)', file=sys.stderr) + time.sleep(wait) + except RuntimeError: + raise + except Exception as e: + last = e + wait = min(30, 2 ** attempt) + print(f' WARN {rel} failed ({e!r}); retry in {wait}s ({attempt + 1}/5)', file=sys.stderr) + time.sleep(wait) + raise RuntimeError(f'download failed after 5 attempts: {last!r}') + +def main() -> int: + ap = argparse.ArgumentParser(description=__doc__) + ap.add_argument('--cookies', required=True, type=Path) + ap.add_argument('--base', required=True, help='dataset URL ending with /, e.g. https://cicresearch.ca/IOTDataset/CIC_IOT_Dataset2023/') + ap.add_argument('--root', required=True, help='sub-path to crawl (e.g. PCAP or CSV)') + ap.add_argument('--dest', required=True, type=Path, help='local directory to mirror into') + ap.add_argument('--dry-run', action='store_true', help='enumerate only; do not download') + ap.add_argument('--limit', type=int, default=0, help='stop after N files (0 = no limit)') + ap.add_argument('--skip-ext', default='', help="comma-separated file extensions to skip (e.g. 'zip,7z'); case-insensitive, no dots") + args = ap.parse_args() + skip_exts = {e.strip().lower().lstrip('.') for e in args.skip_ext.split(',') if e.strip()} + if not args.cookies.is_file(): + print(f'ERROR: cookies file not found: {args.cookies}', file=sys.stderr) + return 2 + opener = build_opener(args.cookies) + args.dest.mkdir(parents=True, exist_ok=True) + print(f'Base : {args.base}') + print(f'Root : {args.root}') + print(f'Dest : {args.dest}') + print(f'Walking tree...') + n_files = 0 + n_skipped = 0 + for remote in walk(opener, args.base, args.root): + ext = remote.rsplit('.', 1)[-1].lower() if '.' in remote else '' + if ext in skip_exts: + n_skipped += 1 + print(f" SKIP {remote} (extension '.{ext}' excluded)") + continue + n_files += 1 + if args.dry_run: + print(f' FILE {remote}') + else: + try: + download_file(opener, args.base, remote, args.dest, root_prefix=args.root.rstrip('/')) + except Exception as e: + print(f' FAIL {remote}: {e}', file=sys.stderr) + if args.limit and n_files >= args.limit: + print(f'-- stopped after {args.limit} (--limit) --') + break + print(f'Done. {n_files} files processed, {n_skipped} skipped by --skip-ext.') + return 0 +if __name__ == '__main__': + sys.exit(main()) diff --git a/scripts/download/cookies_cicapt_iiot2024.txt b/scripts/download/cookies_cicapt_iiot2024.txt new file mode 100644 index 0000000..74284f1 --- /dev/null +++ b/scripts/download/cookies_cicapt_iiot2024.txt @@ -0,0 +1,5 @@ +# Netscape HTTP Cookie File +# https://curl.haxx.se/rfc/cookie_spec.html +# This is a generated file! Do not edit. + +.cicresearch.ca TRUE / TRUE 1777047525 Token ef8ooumh5qdh42r0k410mjoq0c diff --git a/scripts/download/cookies_cicddos2019.txt b/scripts/download/cookies_cicddos2019.txt new file mode 100644 index 0000000..f257ad7 --- /dev/null +++ b/scripts/download/cookies_cicddos2019.txt @@ -0,0 +1,4 @@ +# Netscape HTTP Cookie File +# https://curl.haxx.se/rfc/cookie_spec.html + +.cicresearch.ca TRUE / TRUE 1776910223 Token 8kfh51fj8u46lum8kvu6safonr diff --git a/scripts/download/cookies_ciciot2023.txt b/scripts/download/cookies_ciciot2023.txt new file mode 100644 index 0000000..73220c4 --- /dev/null +++ b/scripts/download/cookies_ciciot2023.txt @@ -0,0 +1,5 @@ +# Netscape HTTP Cookie File +# https://curl.haxx.se/rfc/cookie_spec.html +# This is a generated file! Do not edit. + +.cicresearch.ca TRUE / TRUE 1777518468 Token qn181atofvua6sn8ouv1hlcoo8 diff --git a/scripts/download/cookies_iscxtor2016.txt b/scripts/download/cookies_iscxtor2016.txt new file mode 100644 index 0000000..12ef638 --- /dev/null +++ b/scripts/download/cookies_iscxtor2016.txt @@ -0,0 +1,5 @@ +# Netscape HTTP Cookie File +# https://curl.haxx.se/rfc/cookie_spec.html +# This is a generated file! Do not edit. + +.cicresearch.ca TRUE / TRUE 1776990463 Token t4sfffhk5mnttgkh300buhg0it diff --git a/scripts/download/download_cicapt_iiot2024.sh b/scripts/download/download_cicapt_iiot2024.sh new file mode 100755 index 0000000..c60972d --- /dev/null +++ b/scripts/download/download_cicapt_iiot2024.sh @@ -0,0 +1,38 @@ +#!/usr/bin/env bash +# Download CICAPT-IIoT2024 (entire dataset tree) from UNB CIC via cic_download.py. +# +# Prereq: Token cookie for .cicresearch.ca saved as +# scripts/download/cookies_cicapt_iiot2024.txt +# +# Remote tree is crawled in a single pass under ROOT="CICAPT-IIoT Dataset" +# (the top-level folder at +# https://cicresearch.ca/IOTDataset/CICAPT-IIoT-Dataset/browse.php?p=CICAPT-IIoT+Dataset ). +# Every leaf file — pcap, csv, whatever — is mirrored under +# datasets/cicapt_iiot2024/raw/ +# preserving the remote subdirectory layout. +# +# Usage: +# bash download_cicapt_iiot2024.sh # full download +# DRY_RUN=1 bash download_cicapt_iiot2024.sh # enumerate only +# LIMIT=5 bash download_cicapt_iiot2024.sh # smoke test (first 5 files) +# SKIP_EXT=zip,7z bash download_cicapt_iiot2024.sh # skip archives + +set -euo pipefail + +SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" +REPO_ROOT="$(cd "${SCRIPT_DIR}/../.." && pwd)" + +DEST_ROOT="${DEST:-${REPO_ROOT}/datasets/cicapt_iiot2024/raw}" +COOKIES="${COOKIES:-${SCRIPT_DIR}/cookies_cicapt_iiot2024.txt}" +BASE="${BASE:-https://cicresearch.ca/IOTDataset/CICAPT-IIoT-Dataset/}" +ROOT="${ROOT:-CICAPT-IIoT Dataset}" + +EXTRA=() +[[ "${DRY_RUN:-}" == "1" ]] && EXTRA+=(--dry-run) +[[ -n "${LIMIT:-}" ]] && EXTRA+=(--limit "${LIMIT}") +[[ -n "${SKIP_EXT:-}" ]] && EXTRA+=(--skip-ext "${SKIP_EXT}") + +echo "=== ${ROOT} -> ${DEST_ROOT} ===" +python3 -u "${SCRIPT_DIR}/cic_download.py" \ + --cookies "${COOKIES}" --base "${BASE}" \ + --root "${ROOT}" --dest "${DEST_ROOT}" "${EXTRA[@]}" diff --git a/scripts/download/download_cicddos2019.sh b/scripts/download/download_cicddos2019.sh new file mode 100755 index 0000000..5fefd93 --- /dev/null +++ b/scripts/download/download_cicddos2019.sh @@ -0,0 +1,58 @@ +#!/usr/bin/env bash +# Download CICDDoS2019 (CSV, optionally PCAP) from UNB CIC via cic_download.py. +# +# Prereq: submit the form at +# https://www.unb.ca/cic/datasets/ddos-2019.html +# in a browser, then save the issued Token cookie (Netscape format) as +# scripts/download/cookies_cicddos2019.txt +# Tokens are scoped per-dataset — the CICIoT2023 / ISCXTor cookies will NOT +# work here. +# +# PCAPs for this dataset are already downloaded (see datasets/cicddos2019/raw/ +# pcap/). Default WHAT=csv reflects that. Switch to WHAT=pcap or WHAT=both if +# you need to re-fetch. +# +# Usage: +# bash download_cicddos2019.sh # CSVs only (default) +# WHAT=pcap bash download_cicddos2019.sh # PCAPs only +# WHAT=both bash download_cicddos2019.sh # everything +# DRY_RUN=1 bash download_cicddos2019.sh # enumerate without downloading +# CSV_ROOT=CSV bash download_cicddos2019.sh # override root if server uses a different name +# +# First-time tip: run with DRY_RUN=1 to discover the exact remote root names. +# The CIC site is inconsistent across datasets (CSV / CSVs / CSV-01-12 ...). + +set -euo pipefail + +SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" +REPO_ROOT="$(cd "${SCRIPT_DIR}/../.." && pwd)" + +DEST_ROOT="${DEST:-${REPO_ROOT}/datasets/cicddos2019/raw}" +COOKIES="${COOKIES:-${SCRIPT_DIR}/cookies_cicddos2019.txt}" +BASE="https://cicresearch.ca/CICDataset/CICDDoS2019/" +WHAT="${WHAT:-csv}" + +# Default root names. Override via env if dry-run shows a different layout. +PCAP_ROOT="${PCAP_ROOT:-PCAPs}" +CSV_ROOT="${CSV_ROOT:-CSVs}" + +EXTRA=() +[[ "${DRY_RUN:-}" == "1" ]] && EXTRA+=(--dry-run) +[[ -n "${LIMIT:-}" ]] && EXTRA+=(--limit "${LIMIT}") +[[ -n "${SKIP_EXT:-}" ]] && EXTRA+=(--skip-ext "${SKIP_EXT}") + +run() { + local root="$1" dest="$2" + echo "=== ${root} -> ${dest} ===" + python -u "${SCRIPT_DIR}/cic_download.py" \ + --cookies "${COOKIES}" --base "${BASE}" \ + --root "${root}" --dest "${dest}" "${EXTRA[@]}" +} + +case "${WHAT}" in + pcap) run "${PCAP_ROOT}" "${DEST_ROOT}/pcap" ;; + csv) run "${CSV_ROOT}" "${DEST_ROOT}/csv" ;; + both) run "${PCAP_ROOT}" "${DEST_ROOT}/pcap" + run "${CSV_ROOT}" "${DEST_ROOT}/csv" ;; + *) echo "Unknown WHAT=${WHAT} (expected pcap|csv|both)" >&2; exit 1 ;; +esac diff --git a/scripts/download/download_ciciot2023.sh b/scripts/download/download_ciciot2023.sh new file mode 100755 index 0000000..15fc591 --- /dev/null +++ b/scripts/download/download_ciciot2023.sh @@ -0,0 +1,45 @@ +#!/usr/bin/env bash +# Download CICIoT2023 (PCAP + CSV) from UNB CIC via cic_download.py. +# +# Prereq: submit the form at +# https://www.unb.ca/cic/datasets/iotdataset-2023.html +# in a browser, then save the issued Token cookie in Netscape format as +# scripts/download/cookies_ciciot2023.txt +# The cookie domain must be .cicresearch.ca and the name must be "Token". +# +# Usage: +# bash download_ciciot2023.sh # both PCAP and CSV +# WHAT=pcap bash download_ciciot2023.sh # PCAP only +# WHAT=csv bash download_ciciot2023.sh # CSV only +# DRY_RUN=1 bash download_ciciot2023.sh # enumerate without downloading + +set -euo pipefail + +SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" +REPO_ROOT="$(cd "${SCRIPT_DIR}/../.." && pwd)" + +DEST_ROOT="${DEST:-${REPO_ROOT}/datasets/ciciot2023/raw}" +COOKIES="${COOKIES:-${SCRIPT_DIR}/cookies_ciciot2023.txt}" +BASE="https://cicresearch.ca/IOTDataset/CIC_IOT_Dataset2023/" +WHAT="${WHAT:-both}" + +EXTRA=() +[[ "${DRY_RUN:-}" == "1" ]] && EXTRA+=(--dry-run) +[[ -n "${LIMIT:-}" ]] && EXTRA+=(--limit "${LIMIT}") +[[ -n "${SKIP_EXT:-}" ]] && EXTRA+=(--skip-ext "${SKIP_EXT}") + +run() { + local root="$1" dest="$2" + echo "=== ${root} -> ${dest} ===" + python -u "${SCRIPT_DIR}/cic_download.py" \ + --cookies "${COOKIES}" --base "${BASE}" \ + --root "${root}" --dest "${dest}" "${EXTRA[@]}" +} + +case "${WHAT}" in + pcap) run PCAP "${DEST_ROOT}/pcap" ;; + csv) run CSV "${DEST_ROOT}/csv" ;; + both) run PCAP "${DEST_ROOT}/pcap" + run CSV "${DEST_ROOT}/csv" ;; + *) echo "Unknown WHAT=${WHAT} (expected pcap|csv|both)" >&2; exit 1 ;; +esac diff --git a/scripts/download/download_iscxtor2016.sh b/scripts/download/download_iscxtor2016.sh new file mode 100755 index 0000000..e8d1ce3 --- /dev/null +++ b/scripts/download/download_iscxtor2016.sh @@ -0,0 +1,75 @@ +#!/usr/bin/env bash +# Download ISCXTor2016 (PCAP + CSV) from UNB CIC via cic_download.py. +# +# Prereq: submit the form at +# https://www.unb.ca/cic/datasets/tor.html +# in a browser, then save the issued Token cookie (Netscape format) as +# scripts/download/cookies_iscxtor2016.txt +# Tokens are scoped per-dataset — the CICIoT2023 cookie will NOT work here. +# +# Usage: +# bash download_iscxtor2016.sh +# WHAT=pcap|csv|both DEST=... COOKIES=... DRY_RUN=1 LIMIT=N +# PCAP_ROOT=... CSV_ROOT=... SKIP_EXT=zip,7z +# +# Note: the remote sub-path names ("Pcaps" / "CSVs" or similar) are only +# visible after authenticating. Run with DRY_RUN=1 first to confirm the +# tree; if the roots differ, set PCAP_ROOT=... and/or CSV_ROOT=.... + +set -euo pipefail + +SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" +REPO_ROOT="$(cd "${SCRIPT_DIR}/../.." && pwd)" + +DEST_ROOT="${DEST:-${REPO_ROOT}/datasets/iscxtor2016/raw}" +COOKIES="${COOKIES:-${SCRIPT_DIR}/cookies_iscxtor2016.txt}" +BASE="https://cicresearch.ca/CICDataset/ISCX-Tor-NonTor-2017/" +WHAT="${WHAT:-both}" + +# Default root names (override via env if the server uses different casing) +PCAP_ROOT="${PCAP_ROOT:-PCAPs}" +CSV_ROOT="${CSV_ROOT:-CSVs}" + +EXTRA=() +[[ "${DRY_RUN:-}" == "1" ]] && EXTRA+=(--dry-run) +[[ -n "${LIMIT:-}" ]] && EXTRA+=(--limit "${LIMIT}") +[[ -n "${SKIP_EXT:-}" ]] && EXTRA+=(--skip-ext "${SKIP_EXT}") + +resolve_python() { + if [[ -n "${PYTHON:-}" ]]; then + printf '%s\n' "${PYTHON}" + return + fi + if [[ -x "${REPO_ROOT}/.venv/bin/python" ]]; then + printf '%s\n' "${REPO_ROOT}/.venv/bin/python" + return + fi + if command -v python >/dev/null 2>&1; then + command -v python + return + fi + if command -v python3 >/dev/null 2>&1; then + command -v python3 + return + fi + echo "ERROR: no Python interpreter found. Set PYTHON=/path/to/python." >&2 + exit 127 +} + +PYTHON_BIN="$(resolve_python)" + +run() { + local root="$1" dest="$2" + echo "=== ${root} -> ${dest} ===" + "${PYTHON_BIN}" -u "${SCRIPT_DIR}/cic_download.py" \ + --cookies "${COOKIES}" --base "${BASE}" \ + --root "${root}" --dest "${dest}" "${EXTRA[@]}" +} + +case "${WHAT}" in + pcap) run "${PCAP_ROOT}" "${DEST_ROOT}/pcap" ;; + csv) run "${CSV_ROOT}" "${DEST_ROOT}/csv" ;; + both) run "${PCAP_ROOT}" "${DEST_ROOT}/pcap" + run "${CSV_ROOT}" "${DEST_ROOT}/csv" ;; + *) echo "Unknown WHAT=${WHAT} (expected pcap|csv|both)" >&2; exit 1 ;; +esac diff --git a/scripts/eval_cross_dataset_protocol.py b/scripts/eval_cross_dataset_protocol.py new file mode 100644 index 0000000..238394a --- /dev/null +++ b/scripts/eval_cross_dataset_protocol.py @@ -0,0 +1,114 @@ +from __future__ import annotations +import argparse +import json +import sys +from pathlib import Path +import numpy as np +import torch +from sklearn.metrics import average_precision_score, roc_auc_score +sys.path.insert(0, str(Path(__file__).resolve().parents[1])) +from data import _preprocess_packet_batch +from detect import _load_model +from packet_store import PacketShardStore + +@torch.no_grad() +def _score_indices(*, store: PacketShardStore, indices: np.ndarray, model, device: torch.device, preprocess: str, mean: np.ndarray, std: np.ndarray, clip_lo: np.ndarray | None, clip_hi: np.ndarray | None, split_tag: str, split_seed: int, batch: int, materialize_batch: int, n_steps: int) -> dict[str, np.ndarray]: + out = {'terminal_norm': [], 'arc_length': [], 'kinetic_energy': [], 'velocity_score': []} + total = len(indices) + report_every = max(1, total // 4) + next_report = 0 + for start in range(0, total, materialize_batch): + idx = indices[start:start + materialize_batch] + (x_np, lens_np) = store.read_packets(idx, T=model.cfg.T) + x_np = _preprocess_packet_batch(x_np, lens_np, preprocess=preprocess, mean=mean, std=std, clip_lo=clip_lo, clip_hi=clip_hi, split_tag=split_tag, split_seed=split_seed, flow_ids=idx) + for pos in range(0, len(idx), batch): + bx = torch.from_numpy(x_np[pos:pos + batch]).float().to(device) + bl = torch.from_numpy(lens_np[pos:pos + batch]).long().to(device) + m = model.trajectory_metrics(bx, lens=bl, cond=None, n_steps=n_steps) + for key in ('terminal_norm', 'arc_length', 'kinetic_energy'): + out[key].append(m[key].cpu().numpy()) + vs = model.velocity_score(bx, lens=bl, cond=None, t_eval=(0.5, 0.75, 1.0)) + out['velocity_score'].append(vs.cpu().numpy()) + done = min(start + len(idx), total) + if done >= next_report or done == total: + print(f'[{split_tag}] {done:,}/{total:,}', flush=True) + next_report = done + report_every + return {key: np.concatenate(parts) for (key, parts) in out.items()} + +def run(args: argparse.Namespace) -> None: + device = torch.device('cuda' if args.device == 'auto' and torch.cuda.is_available() else 'cpu' if args.device == 'auto' else args.device) + save_dir = Path(args.save_dir) + ckpt = torch.load(save_dir / 'model.pt', map_location='cpu', weights_only=False) + preprocess = str(ckpt.get('preprocess', 'zscore')) + mean = np.asarray(ckpt['packet_mean'], dtype=np.float32) + std = np.asarray(ckpt['packet_std'], dtype=np.float32) + clip_lo = np.asarray(ckpt['clip_lo'], dtype=np.float32) if 'clip_lo' in ckpt else None + clip_hi = np.asarray(ckpt['clip_hi'], dtype=np.float32) if 'clip_hi' in ckpt else None + model = _load_model(save_dir, device) + store = PacketShardStore.open(Path(args.target_store)) + flows = store.read_flows(columns=['flow_id', 'label']) + labels = flows['label'].to_numpy().astype(str) + lens = store.manifest['packet_length'].to_numpy(dtype=np.int32) + keep = lens >= int(args.min_len) + benign_idx = flows.loc[keep & (labels == args.benign_label), 'flow_id'].to_numpy(dtype=np.int64) + attack_df = flows.loc[keep & (labels != args.benign_label), ['flow_id', 'label']] + attack_idx_all = attack_df['flow_id'].to_numpy(dtype=np.int64) + attack_labels_all = attack_df['label'].to_numpy().astype(str) + if len(benign_idx) < args.n_benign: + raise ValueError(f'target has only {len(benign_idx)} benign rows, need {args.n_benign}') + if len(attack_idx_all) < args.n_attack: + raise ValueError(f'target has only {len(attack_idx_all)} attack rows, need {args.n_attack}') + print(f'[target] store={args.target_store} benign_pool={len(benign_idx):,} attack_pool={len(attack_idx_all):,} T={model.cfg.T} preprocess={preprocess}', flush=True) + results: dict[str, object] = {'save_dir': str(save_dir), 'target_store': str(args.target_store), 'n_benign': int(args.n_benign), 'n_attack': int(args.n_attack), 'seeds': [], 'mean': {}, 'std': {}} + metrics = ('terminal_norm', 'arc_length', 'kinetic_energy', 'velocity_score') + per_metric_values = {f'{metric}_auroc': [] for metric in metrics} + per_metric_values.update({f'{metric}_auprc': [] for metric in metrics}) + for seed in args.seeds: + rng = np.random.default_rng(int(seed)) + b_idx = np.sort(rng.choice(benign_idx, args.n_benign, replace=False)) + a_pos = rng.choice(len(attack_idx_all), args.n_attack, replace=False) + a_pos.sort() + a_idx = attack_idx_all[a_pos] + a_labels = attack_labels_all[a_pos] + print(f'[seed={seed}] scoring benign={len(b_idx):,} attack={len(a_idx):,}', flush=True) + b_scores = _score_indices(store=store, indices=b_idx, model=model, device=device, preprocess=preprocess, mean=mean, std=std, clip_lo=clip_lo, clip_hi=clip_hi, split_tag='val', split_seed=int(seed), batch=args.batch, materialize_batch=args.materialize_batch, n_steps=args.n_steps) + a_scores = _score_indices(store=store, indices=a_idx, model=model, device=device, preprocess=preprocess, mean=mean, std=std, clip_lo=clip_lo, clip_hi=clip_hi, split_tag='attack', split_seed=int(seed), batch=args.batch, materialize_batch=args.materialize_batch, n_steps=args.n_steps) + seed_result: dict[str, object] = {'seed': int(seed), 'attack_label_counts': {str(k): int(v) for (k, v) in zip(*np.unique(a_labels, return_counts=True))}, 'metrics': {}} + for metric in metrics: + y = np.r_[np.zeros(len(b_scores[metric])), np.ones(len(a_scores[metric]))] + s = np.r_[b_scores[metric], a_scores[metric]] + s = np.nan_to_num(s, nan=0.0, posinf=1000000.0, neginf=-1000000.0) + auroc = float(roc_auc_score(y, s)) + auprc = float(average_precision_score(y, s)) + seed_result['metrics'][metric] = {'auroc': auroc, 'auprc': auprc} + per_metric_values[f'{metric}_auroc'].append(auroc) + per_metric_values[f'{metric}_auprc'].append(auprc) + print(f'[seed={seed}] {metric:<16s} AUROC={auroc:.4f} AUPRC={auprc:.4f}', flush=True) + results['seeds'].append(seed_result) + for (key, values) in per_metric_values.items(): + arr = np.asarray(values, dtype=np.float64) + results['mean'][key] = float(arr.mean()) + results['std'][key] = float(arr.std(ddof=0)) + Path(args.output).parent.mkdir(parents=True, exist_ok=True) + Path(args.output).write_text(json.dumps(results, indent=2, sort_keys=True) + '\n') + print(f'[saved] {args.output}', flush=True) + for metric in metrics: + print(f"[mean] {metric:<16s} AUROC={results['mean'][metric + '_auroc']:.4f}±{results['std'][metric + '_auroc']:.4f} AUPRC={results['mean'][metric + '_auprc']:.4f}±{results['std'][metric + '_auprc']:.4f}", flush=True) + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument('--save-dir', type=Path, required=True) + parser.add_argument('--target-store', type=Path, required=True) + parser.add_argument('--output', type=Path, required=True) + parser.add_argument('--n-benign', type=int, default=10000) + parser.add_argument('--n-attack', type=int, default=10000) + parser.add_argument('--seeds', type=int, nargs='+', default=[0, 1, 2, 3, 4]) + parser.add_argument('--benign-label', type=str, default='normal') + parser.add_argument('--min-len', type=int, default=2) + parser.add_argument('--n-steps', type=int, default=16) + parser.add_argument('--batch', type=int, default=4096) + parser.add_argument('--materialize-batch', type=int, default=32768) + parser.add_argument('--device', type=str, default='auto') + run(parser.parse_args()) +if __name__ == '__main__': + main() diff --git a/scripts/extract_cicddos2019.py b/scripts/extract_cicddos2019.py new file mode 100644 index 0000000..ea95336 --- /dev/null +++ b/scripts/extract_cicddos2019.py @@ -0,0 +1,132 @@ +from __future__ import annotations +import argparse +import csv +import sys +from datetime import datetime +from pathlib import Path +import numpy as np +sys.path.insert(0, str(Path(__file__).resolve().parent)) +from extract_lib import extract_dataset, _canonical_key +from csv_adapter import CsvFlowAdapter, parse_csv_rows +JOIN_COLS = {'src_ip': 'Source IP', 'src_port': 'Source Port', 'dst_ip': 'Destination IP', 'dst_port': 'Destination Port', 'protocol': 'Protocol', 'timestamp': 'Timestamp'} +LABEL_COL = 'Label' +TIMESTAMP_FORMATS = ('%Y-%m-%d %H:%M:%S.%f', '%Y-%m-%d %H:%M:%S') +BENIGN_ALIASES = {'BENIGN', 'Benign', 'benign'} +BENIGN_TOKEN = 'normal' +DROP_LABEL_PATTERNS: tuple[str, ...] = () +LABEL_ALIASES = {'UDP-lag': 'UDPLag'} +SHARDS = {'01-12': 'SAT-01-12-2018', '03-11': 'SAT-03-11-2018'} +SHARD_OFFSETS_DEFAULT = {'01-12': 43200.0, '03-11': 39600.0} +DEFAULT_CSV_DIR = Path('datasets/cicddos2019/raw/csv') +DEFAULT_PCAP_DIR = Path('datasets/cicddos2019/raw/pcap') +DEFAULT_OUT_PACKETS = Path('datasets/cicddos2019/processed/packets.npz') +DEFAULT_OUT_FLOWS = Path('datasets/cicddos2019/processed/flows.parquet') +CICDDOS2019_ADAPTER = CsvFlowAdapter(join_cols=JOIN_COLS, label_col=LABEL_COL, timestamp_formats=TIMESTAMP_FORMATS, benign_aliases=frozenset(BENIGN_ALIASES), benign_token=BENIGN_TOKEN, drop_label_patterns=DROP_LABEL_PATTERNS, label_aliases=LABEL_ALIASES) + +def _normalize_label(raw: str) -> str: + s = raw.strip() + if s in BENIGN_ALIASES: + return BENIGN_TOKEN + return LABEL_ALIASES.get(s, s) + +def _parse_timestamp(ts: str) -> float | None: + s = ts.strip() + if not s: + return None + for fmt in TIMESTAMP_FORMATS: + try: + return datetime.strptime(s, fmt).timestamp() + except ValueError: + continue + return None + +def _find_pcaps_for_shard(pcap_dir: Path, prefix: str) -> list[Path]: + found: list[Path] = [] + seen = set() + for pat in (f'{prefix}*', f'{prefix}*.pcap', f'{prefix}*.pcapng'): + for p in sorted(pcap_dir.glob(pat)): + if p.is_file() and p not in seen: + found.append(p) + seen.add(p) + return found + +def _parse_csv(csv_path: Path, row_idx_start: int, time_offset_seconds: float, max_per_class: int | None, max_benign: int | None, rng: np.random.Generator) -> tuple[dict[tuple, list[tuple[int, float]]], list[str], int, int, dict[str, int]]: + return parse_csv_rows(csv_path=csv_path, row_idx_start=row_idx_start, time_offset_seconds=time_offset_seconds, adapter=CICDDOS2019_ADAPTER, max_per_class=max_per_class, max_benign=max_benign, rng=rng) + +def main() -> None: + ap = argparse.ArgumentParser(description=__doc__) + ap.add_argument('--csv-dir', type=Path, default=DEFAULT_CSV_DIR) + ap.add_argument('--pcap-dir', type=Path, default=DEFAULT_PCAP_DIR) + ap.add_argument('--out-packets', type=Path, default=DEFAULT_OUT_PACKETS) + ap.add_argument('--out-flows', type=Path, default=DEFAULT_OUT_FLOWS) + ap.add_argument('--out-store', type=Path, default=None, help='Optional sharded packet store output. When set, writes store_root/{metadata,manifest,flows,packets/*} instead of the monolithic packets.npz/flows.parquet pair.') + ap.add_argument('--shard-size', type=int, default=100000, help='Rows per packet shard when --out-store is set.') + ap.add_argument('--worker-flush-size', type=int, default=10000, help='Matched flows per temporary worker chunk when --out-store is set.') + ap.add_argument('--spool-dir', type=Path, default=None, help='Optional temporary spool directory for worker chunks.') + ap.add_argument('--match-strategy', choices=('auto', 'hungarian', 'stream_nearest'), default='auto', help='CSV↔pcap matching strategy. auto uses stream_nearest for --out-store and hungarian for legacy npz output.') + ap.add_argument('--T-full', type=int, default=256) + ap.add_argument('--idle-timeout', type=float, default=120.0) + ap.add_argument('--time-tolerance', type=float, default=2.0) + ap.add_argument('--time-offset', type=float, default=0.0, help='Extra seconds added to per-shard SHARD_OFFSETS_DEFAULT. Default 0 assumes a UTC+8 host (matches the SHARD_OFFSETS_DEFAULT values: 03-11=39600, 01-12=43200). If the per-shard time-delta diagnostic shows a non-zero median, add that to this flag.') + ap.add_argument('--jobs', type=int, default=0, help='0=auto (min(n_shards, cpu_count)). 1=serial.') + ap.add_argument('--shards', type=str, nargs='*', default=None, choices=sorted(SHARDS.keys()), help='Subset of shards to process (default: all).') + ap.add_argument('--max-per-class', type=int, default=500000, help='Per-file, per-attack-class row cap (random subsample). Default 500k. Pass 0 to disable.') + ap.add_argument('--max-benign', type=int, default=None, help='Per-file benign row cap. Default: uncapped (keep all).') + ap.add_argument('--max-packets-per-pcap', type=int, default=None, help='Cap per-pcap packets (smoke only).') + ap.add_argument('--max-pcap-files-per-shard', type=int, default=None, help='Only process the first N pcap chunks per shard (smoke only).') + ap.add_argument('--sample-seed', type=int, default=42) + args = ap.parse_args() + max_per_class = args.max_per_class or None + max_benign = args.max_benign or None + rng = np.random.default_rng(args.sample_seed) + shards = args.shards or sorted(SHARDS.keys()) + csv_rows_by_day: dict[str, dict] = {} + all_labels: list[str] = [] + total_rows = 0 + total_skip = 0 + aggregate_counts: dict[str, int] = {} + print(f'=== parsing CSVs in {args.csv_dir} ===') + print(f' max_per_class={max_per_class} max_benign={max_benign}') + print(f' additive time_offset={args.time_offset}s (on top of per-shard defaults)') + for shard in shards: + shard_offset = SHARD_OFFSETS_DEFAULT.get(shard, 0.0) + args.time_offset + print(f'[{shard}] effective time_offset={shard_offset}s (= default {SHARD_OFFSETS_DEFAULT.get(shard, 0.0)} + CLI {args.time_offset})') + shard_dir = args.csv_dir / shard + if not shard_dir.is_dir(): + print(f'[{shard}] {shard_dir} not found — skipping') + continue + csvs = sorted(shard_dir.glob('*.csv')) + if not csvs: + print(f'[{shard}] no CSVs under {shard_dir}') + continue + shard_rows: dict[tuple, list[tuple[int, float]]] = {} + for csv_path in csvs: + (day_rows, labels, n_emit, n_skip, cls_counts) = _parse_csv(csv_path, row_idx_start=total_rows, time_offset_seconds=shard_offset, max_per_class=max_per_class, max_benign=max_benign, rng=rng) + for (ck, rs) in day_rows.items(): + shard_rows.setdefault(ck, []).extend(rs) + all_labels.extend(labels) + total_rows += n_emit + total_skip += n_skip + for (lbl, c) in cls_counts.items(): + aggregate_counts[lbl] = aggregate_counts.get(lbl, 0) + c + print(f'[{shard}/{csv_path.name}] emitted {n_emit:,} skipped {n_skip:,} cls={dict(sorted(cls_counts.items()))}') + csv_rows_by_day[shard] = shard_rows + print(f'[{shard}] shard total: {sum((len(v) for v in shard_rows.values())):,} canonical keys') + labels_by_row = np.asarray(all_labels, dtype=object) + print(f'\nTotal CSV rows emitted: {total_rows:,} skipped: {total_skip:,}') + print(f'Aggregate label distribution (post-subsample):') + for (lbl, cnt) in sorted(aggregate_counts.items(), key=lambda x: -x[1]): + print(f' {lbl:<40s} {cnt:>12,}') + print(f'\n=== locating pcap chunks in {args.pcap_dir} ===') + pcap_files_by_day: dict[str, list[Path]] = {} + for shard in shards: + prefix = SHARDS[shard] + files = _find_pcaps_for_shard(args.pcap_dir, prefix) + if args.max_pcap_files_per_shard is not None: + files = files[:args.max_pcap_files_per_shard] + pcap_files_by_day[shard] = files + print(f'[{shard}] prefix {prefix!r} → {len(files):,} pcap chunks') + print(f'\n=== extracting packet sequences ===') + extract_dataset(csv_rows_by_day=csv_rows_by_day, labels_by_row=labels_by_row, pcap_files_by_day=pcap_files_by_day, out_packets=args.out_packets, out_flows=args.out_flows, out_store=args.out_store, shard_size=args.shard_size, worker_flush_size=args.worker_flush_size, spool_dir=args.spool_dir, match_strategy=None if args.match_strategy == 'auto' else args.match_strategy, T_full=args.T_full, idle_timeout=args.idle_timeout, time_tolerance_seconds=args.time_tolerance, max_packets_per_pcap=args.max_packets_per_pcap, n_jobs=args.jobs) +if __name__ == '__main__': + main() diff --git a/scripts/extract_cicids2017.py b/scripts/extract_cicids2017.py new file mode 100644 index 0000000..e2a9713 --- /dev/null +++ b/scripts/extract_cicids2017.py @@ -0,0 +1,104 @@ +from __future__ import annotations +import argparse +import csv +import sys +from datetime import datetime +from pathlib import Path +import numpy as np +sys.path.insert(0, str(Path(__file__).resolve().parent)) +from extract_lib import extract_dataset, _canonical_key +from csv_adapter import CsvFlowAdapter, parse_csv_rows +JOIN_COLS = {'src_ip': 'Src IP', 'src_port': 'Src Port', 'dst_ip': 'Dst IP', 'dst_port': 'Dst Port', 'protocol': 'Protocol', 'timestamp': 'Timestamp'} +LABEL_COL = 'Label' +TIMESTAMP_FORMATS = ('%Y-%m-%d %H:%M:%S.%f', '%Y-%m-%d %H:%M:%S', '%d/%m/%Y %H:%M:%S', '%d/%m/%Y %H:%M') +BENIGN_ALIASES = {'BENIGN', 'Benign', 'benign'} +BENIGN_TOKEN = 'normal' +DROP_LABEL_PATTERNS = ('- Attempted',) +SHARDS = ('monday', 'tuesday', 'wednesday', 'thursday', 'friday') +DEFAULT_CSV_DIR = Path('datasets/cicids2017/raw/csv') +DEFAULT_PCAP_DIR = Path('datasets/cicids2017/raw/pcap') +DEFAULT_OUT_PACKETS = Path('datasets/cicids2017/processed/packets.npz') +DEFAULT_OUT_FLOWS = Path('datasets/cicids2017/processed/flows.parquet') +CICIDS2017_ADAPTER = CsvFlowAdapter(join_cols=JOIN_COLS, label_col=LABEL_COL, timestamp_formats=TIMESTAMP_FORMATS, benign_aliases=frozenset(BENIGN_ALIASES), benign_token=BENIGN_TOKEN, drop_label_patterns=DROP_LABEL_PATTERNS) + +def _normalize_label(raw: str) -> str: + s = raw.strip() + return BENIGN_TOKEN if s in BENIGN_ALIASES else s + +def _parse_timestamp(ts: str) -> float | None: + s = ts.strip() + if not s: + return None + for fmt in TIMESTAMP_FORMATS: + try: + return datetime.strptime(s, fmt).timestamp() + except ValueError: + continue + return None + +def _find_pcaps_for_day(pcap_dir: Path, day: str) -> list[Path]: + day_lc = day.lower() + day_cap = day.capitalize() + pats = [f'*{day_lc}*.pcap', f'*{day_lc}*.pcapng', f'*{day_cap}*.pcap', f'*{day_cap}*.pcapng'] + found: list[Path] = [] + seen = set() + for pat in pats: + for p in sorted(pcap_dir.glob(pat)): + if p not in seen: + found.append(p) + seen.add(p) + return found + +def _parse_day_csv(csv_path: Path, row_idx_start: int, time_offset_seconds: float) -> tuple[dict[tuple, list[tuple[int, float]]], list[str], int, int]: + (day_rows, labels, n_emit, n_skip, _) = parse_csv_rows(csv_path=csv_path, row_idx_start=row_idx_start, time_offset_seconds=time_offset_seconds, adapter=CICIDS2017_ADAPTER) + return (day_rows, labels, n_emit, n_skip) + +def main() -> None: + ap = argparse.ArgumentParser(description=__doc__) + ap.add_argument('--csv-dir', type=Path, default=DEFAULT_CSV_DIR) + ap.add_argument('--pcap-dir', type=Path, default=DEFAULT_PCAP_DIR) + ap.add_argument('--out-packets', type=Path, default=DEFAULT_OUT_PACKETS) + ap.add_argument('--out-flows', type=Path, default=DEFAULT_OUT_FLOWS) + ap.add_argument('--out-store', type=Path, default=None, help='Optional sharded packet store output. When set, writes store_root/{metadata,manifest,flows,packets/*} instead of the monolithic packets.npz/flows.parquet pair.') + ap.add_argument('--shard-size', type=int, default=100000, help='Rows per packet shard when --out-store is set.') + ap.add_argument('--worker-flush-size', type=int, default=10000, help='Matched flows per temporary worker chunk when --out-store is set.') + ap.add_argument('--spool-dir', type=Path, default=None, help='Optional temporary spool directory for worker chunks.') + ap.add_argument('--match-strategy', choices=('auto', 'hungarian', 'stream_nearest'), default='auto', help='CSV↔pcap matching strategy. auto uses stream_nearest for --out-store and hungarian for legacy npz output.') + ap.add_argument('--T-full', type=int, default=256) + ap.add_argument('--idle-timeout', type=float, default=120.0) + ap.add_argument('--time-tolerance', type=float, default=2.0, help='Max |t_csv - t_pcap| seconds for flow match.') + ap.add_argument('--time-offset', type=float, default=0.0, help='Seconds added to CSV timestamps before matching.') + ap.add_argument('--jobs', type=int, default=0, help='0 = auto (min(n_days, cpu_count)). 1 = serial.') + ap.add_argument('--days', type=str, nargs='*', default=None, help='Subset of shards to process (default: all 5).') + ap.add_argument('--max-packets-per-pcap', type=int, default=None, help='Cap per-pcap packets (smoke tests only).') + args = ap.parse_args() + days = tuple(args.days) if args.days else SHARDS + csv_rows_by_day: dict[str, dict] = {} + all_labels: list[str] = [] + total_rows = 0 + total_skip = 0 + print(f'=== parsing CSVs in {args.csv_dir} ===') + for day in days: + csv_path = args.csv_dir / f'{day}.csv' + if not csv_path.exists(): + print(f'[{day}] {csv_path} not found, skipping') + continue + (day_rows, labels, n_emit, n_skip) = _parse_day_csv(csv_path, row_idx_start=total_rows, time_offset_seconds=args.time_offset) + csv_rows_by_day[day] = day_rows + all_labels.extend(labels) + total_rows += n_emit + total_skip += n_skip + print(f'[{day}] emitted {n_emit:,} rows skipped {n_skip:,} canonical keys {len(day_rows):,}') + labels_by_row = np.asarray(all_labels, dtype=object) + print(f'Total CSV rows emitted: {total_rows:,} (skipped {total_skip:,})') + print(f'\n=== locating pcap files in {args.pcap_dir} ===') + pcap_files_by_day: dict[str, list[Path]] = {} + for day in days: + files = _find_pcaps_for_day(args.pcap_dir, day) + pcap_files_by_day[day] = files + names = [p.name for p in files] + print(f'[{day}] {len(files)} pcap(s): {names}') + print(f'\n=== extracting packet sequences ===') + extract_dataset(csv_rows_by_day=csv_rows_by_day, labels_by_row=labels_by_row, pcap_files_by_day=pcap_files_by_day, out_packets=args.out_packets, out_flows=args.out_flows, out_store=args.out_store, shard_size=args.shard_size, worker_flush_size=args.worker_flush_size, spool_dir=args.spool_dir, match_strategy=None if args.match_strategy == 'auto' else args.match_strategy, T_full=args.T_full, idle_timeout=args.idle_timeout, time_tolerance_seconds=args.time_tolerance, max_packets_per_pcap=args.max_packets_per_pcap, n_jobs=args.jobs) +if __name__ == '__main__': + main() diff --git a/scripts/extract_ciciot2023.py b/scripts/extract_ciciot2023.py new file mode 100644 index 0000000..271446a --- /dev/null +++ b/scripts/extract_ciciot2023.py @@ -0,0 +1,56 @@ +from __future__ import annotations +import argparse +import sys +from pathlib import Path +sys.path.insert(0, str(Path(__file__).resolve().parent)) +from extract_lib import extract_labeled_pcaps +DEFAULT_PCAP_ROOT = Path('datasets/ciciot2023/raw/pcap') +DEFAULT_OUT_PACKETS = Path('datasets/ciciot2023/processed/packets.npz') +DEFAULT_OUT_FLOWS = Path('datasets/ciciot2023/processed/flows.parquet') +BENIGN_FOLDER = 'Benign_Final' +BENIGN_LABEL = 'normal' + +def _label_for_folder(folder: str) -> str: + if folder == BENIGN_FOLDER: + return BENIGN_LABEL + return folder.lower() + +def _find_pcap_files(pcap_root: Path, *, max_pcaps_per_class: int | None) -> list[tuple[Path, str, dict]]: + triples: list[tuple[Path, str, dict]] = [] + for class_dir in sorted((p for p in pcap_root.iterdir() if p.is_dir())): + folder = class_dir.name + label = _label_for_folder(folder) + pcaps = sorted(class_dir.rglob('*.pcap')) + sorted(class_dir.rglob('*.pcapng')) + if max_pcaps_per_class is not None and len(pcaps) > max_pcaps_per_class: + pcaps = pcaps[:max_pcaps_per_class] + for p in pcaps: + triples.append((p, label, {'class_folder': folder})) + return triples + +def main() -> None: + ap = argparse.ArgumentParser(description=__doc__) + ap.add_argument('--pcap-root', type=Path, default=DEFAULT_PCAP_ROOT) + ap.add_argument('--out-packets', type=Path, default=DEFAULT_OUT_PACKETS) + ap.add_argument('--out-flows', type=Path, default=DEFAULT_OUT_FLOWS) + ap.add_argument('--out-store', type=Path, default=None, help='Sharded PacketShardStore output. Recommended for CICIoT2023 since the raw set is large.') + ap.add_argument('--shard-size', type=int, default=100000) + ap.add_argument('--worker-flush-size', type=int, default=10000) + ap.add_argument('--spool-dir', type=Path, default=None) + ap.add_argument('--T-full', type=int, default=256) + ap.add_argument('--idle-timeout', type=float, default=120.0) + ap.add_argument('--jobs', type=int, default=0) + ap.add_argument('--max-pcaps-per-class', type=int, default=1, help='Cap pcap files per class folder. Default 1 (single pcap per class) keeps extraction tractable.') + ap.add_argument('--max-packets-per-pcap', type=int, default=2000000, help='Cap packets per pcap to bound RAM/IO. Default 2M.') + args = ap.parse_args() + triples = _find_pcap_files(args.pcap_root, max_pcaps_per_class=args.max_pcaps_per_class) + if not triples: + raise RuntimeError(f'No pcap files found under {args.pcap_root}') + print(f'[discover] {len(triples)} pcap files across {len(set((t[1] for t in triples)))} labels') + by_label: dict[str, int] = {} + for (_, lbl, _) in triples: + by_label[lbl] = by_label.get(lbl, 0) + 1 + for (lbl, n) in sorted(by_label.items()): + print(f' {lbl:<28s} {n} pcap(s)') + extract_labeled_pcaps(pcap_files_with_labels=triples, out_packets=args.out_packets, out_flows=args.out_flows, out_store=args.out_store, shard_size=args.shard_size, worker_flush_size=args.worker_flush_size, spool_dir=args.spool_dir, T_full=args.T_full, idle_timeout=args.idle_timeout, max_packets_per_pcap=args.max_packets_per_pcap, n_jobs=args.jobs, extra_column_names=('class_folder',)) +if __name__ == '__main__': + main() diff --git a/scripts/extract_iscxtor2016.py b/scripts/extract_iscxtor2016.py new file mode 100644 index 0000000..0919b3d --- /dev/null +++ b/scripts/extract_iscxtor2016.py @@ -0,0 +1,96 @@ +from __future__ import annotations +import argparse +import re +import shutil +import subprocess +import sys +import time +from pathlib import Path +sys.path.insert(0, str(Path(__file__).resolve().parent)) +from extract_lib import extract_labeled_pcaps +DEFAULT_PCAP_ARCHIVE_DIR = Path('datasets/iscxtor2016/raw/pcap') +DEFAULT_DECOMPRESS_DIR = Path('datasets/iscxtor2016/raw/pcap_extracted') +DEFAULT_OUT_PACKETS = Path('datasets/iscxtor2016/processed/packets.npz') +DEFAULT_OUT_FLOWS = Path('datasets/iscxtor2016/processed/flows.parquet') +NONTOR_ARCHIVE = 'NonTor.tar.xz' +TOR_ARCHIVE = 'Tor.zip' +ACTIVITY_PATTERNS = (('mail', re.compile('mail|email|imap|pop_|smtp|thunderbird')), ('voip', re.compile('voip|voice|call|facebook_voice|hangouts_voice')), ('audio', re.compile('audio|spotify|skype_audio|hangout_audio|facebook_audio')), ('browsing', re.compile('browsing|browser|ssl_browsing|gate_ssl')), ('chat', re.compile('chat|aim|icq|skypechat')), ('file', re.compile('file[-_]?transfer|ftp|sftp|tftp')), ('p2p', re.compile('p2p|multispeed|multiple[sS]peed|bittor|utor')), ('video', re.compile('video|youtube|vimeo'))) + +def _infer_activity(pcap_name: str) -> str: + lower = pcap_name.lower() + for (act, pat) in ACTIVITY_PATTERNS: + if pat.search(lower): + return act + return 'other' + +def _decompress_archives(archive_dir: Path, out_dir: Path) -> None: + nontor_arc = archive_dir / NONTOR_ARCHIVE + tor_arc = archive_dir / TOR_ARCHIVE + out_nontor = out_dir / 'NonTor' + out_tor = out_dir / 'Tor' + if not out_nontor.exists(): + out_nontor.parent.mkdir(parents=True, exist_ok=True) + print(f'[decompress] {nontor_arc} → {out_dir}/ (tar xf)') + t0 = time.time() + subprocess.run(['tar', '-xf', str(nontor_arc), '-C', str(out_dir)], check=True) + print(f'[decompress] NonTor done in {time.time() - t0:.1f}s') + else: + print(f'[decompress] {out_nontor} already exists — skipping NonTor unpack') + if not out_tor.exists(): + print(f'[decompress] {tor_arc} → {out_dir}/ (unzip)') + t0 = time.time() + subprocess.run(['unzip', '-q', '-o', str(tor_arc), '-d', str(out_dir)], check=True) + print(f'[decompress] Tor done in {time.time() - t0:.1f}s') + else: + print(f'[decompress] {out_tor} already exists — skipping Tor unpack') + +def _find_pcap_files(decompressed_root: Path) -> list[tuple[Path, str, dict]]: + triples: list[tuple[Path, str, dict]] = [] + for (sub, coarse) in (('NonTor', 'nontor'), ('Tor', 'tor')): + sub_dir = decompressed_root / sub + if not sub_dir.exists(): + print(f'[warn] {sub_dir} not found — skipping') + continue + pcaps = sorted(sub_dir.rglob('*.pcap')) + sorted(sub_dir.rglob('*.pcapng')) + for p in pcaps: + activity = _infer_activity(p.name) + triples.append((p, coarse, {'activity': activity})) + return triples + +def main() -> None: + ap = argparse.ArgumentParser(description=__doc__) + ap.add_argument('--archive-dir', type=Path, default=DEFAULT_PCAP_ARCHIVE_DIR) + ap.add_argument('--decompressed-dir', type=Path, default=DEFAULT_DECOMPRESS_DIR) + ap.add_argument('--out-packets', type=Path, default=DEFAULT_OUT_PACKETS) + ap.add_argument('--out-flows', type=Path, default=DEFAULT_OUT_FLOWS) + ap.add_argument('--out-store', type=Path, default=None, help='Optional sharded packet store output. When set, writes store_root/{metadata,manifest,flows,packets/*} instead of the monolithic packets.npz/flows.parquet pair.') + ap.add_argument('--shard-size', type=int, default=100000, help='Rows per packet shard when --out-store is set.') + ap.add_argument('--worker-flush-size', type=int, default=10000, help='Flows per temporary worker chunk when --out-store is set.') + ap.add_argument('--spool-dir', type=Path, default=None, help='Optional temporary spool directory for worker chunks.') + ap.add_argument('--T-full', type=int, default=256) + ap.add_argument('--idle-timeout', type=float, default=120.0) + ap.add_argument('--jobs', type=int, default=0) + ap.add_argument('--max-packets-per-pcap', type=int, default=None) + ap.add_argument('--decompress-only', action='store_true', help='Extract the archives then stop (for staged runs).') + ap.add_argument('--skip-decompress', action='store_true', help='Assume decompressed-dir is already populated.') + args = ap.parse_args() + if not args.skip_decompress: + _decompress_archives(args.archive_dir, args.decompressed_dir) + if args.decompress_only: + print('[decompress-only] exiting as requested.') + return + triples = _find_pcap_files(args.decompressed_dir) + if not triples: + raise RuntimeError(f'No pcap files found under {args.decompressed_dir}') + print(f'\n[discover] found {len(triples)} pcap file(s)') + by_coarse: dict[str, int] = {} + by_act: dict[str, int] = {} + for (_, lbl, extra) in triples: + by_coarse[lbl] = by_coarse.get(lbl, 0) + 1 + by_act[extra['activity']] = by_act.get(extra['activity'], 0) + 1 + print(f' by label: {by_coarse}') + print(f' by activity: {by_act}') + print(f'\n[extract] writing to {args.out_packets} + {args.out_flows}') + extract_labeled_pcaps(pcap_files_with_labels=triples, out_packets=args.out_packets, out_flows=args.out_flows, out_store=args.out_store, shard_size=args.shard_size, worker_flush_size=args.worker_flush_size, spool_dir=args.spool_dir, T_full=args.T_full, idle_timeout=args.idle_timeout, max_packets_per_pcap=args.max_packets_per_pcap, n_jobs=args.jobs, extra_column_names=('activity',)) +if __name__ == '__main__': + main() diff --git a/scripts/extract_lib.py b/scripts/extract_lib.py new file mode 100644 index 0000000..0f7fce7 --- /dev/null +++ b/scripts/extract_lib.py @@ -0,0 +1,774 @@ +from __future__ import annotations +import os +import shutil +import socket +import sys +import tempfile +import time as _time +from collections import defaultdict +from concurrent.futures import ProcessPoolExecutor, as_completed +from dataclasses import dataclass +from pathlib import Path +from typing import Iterator +import dpkt +import numpy as np +import pandas as pd +from scipy.optimize import linear_sum_assignment +_SCRIPT_DIR = Path(__file__).resolve().parent +_REPO_ROOT = _SCRIPT_DIR.parent +sys.path.insert(0, str(_REPO_ROOT / 'Packet_CFM')) +from packet_store import PacketShardWriter +PACKET_FEATURE_NAMES = ('log_size', 'log_dt_ms', 'direction', 'tcp_syn', 'tcp_fin', 'tcp_rst', 'tcp_psh', 'tcp_ack', 'log_win') +PACKET_D = len(PACKET_FEATURE_NAMES) +(FIN, SYN, RST, PSH, ACK) = (1, 2, 4, 8, 16) + +@dataclass(slots=True) +class PacketRecord: + timestamp: float + src_ip: str + dst_ip: str + src_port: int + dst_port: int + protocol: int + tcp_flags: int + payload_len: int + header_len: int + total_len: int + window_size: int + +def _try_open_pcap(f): + try: + return dpkt.pcap.Reader(f) + except ValueError: + f.seek(0) + return dpkt.pcapng.Reader(f) + +def iter_packets(pcap_path: Path, max_packets: int | None=None) -> Iterator[PacketRecord]: + n = 0 + with open(pcap_path, 'rb') as f: + reader = _try_open_pcap(f) + link_type = reader.datalink() + for (ts, buf) in reader: + try: + if link_type == dpkt.pcap.DLT_EN10MB: + eth = dpkt.ethernet.Ethernet(buf) + if eth.type != dpkt.ethernet.ETH_TYPE_IP: + continue + ip = eth.data + elif link_type == dpkt.pcap.DLT_RAW: + ip = dpkt.ip.IP(buf) + elif link_type == dpkt.pcap.DLT_LINUX_SLL: + sll = dpkt.sll.SLL(buf) + if sll.ethtype != dpkt.ethernet.ETH_TYPE_IP: + continue + ip = sll.data + else: + continue + if not isinstance(ip, dpkt.ip.IP): + continue + src_ip = socket.inet_ntoa(ip.src) + dst_ip = socket.inet_ntoa(ip.dst) + transport = ip.data + if isinstance(transport, dpkt.tcp.TCP): + yield PacketRecord(timestamp=ts, src_ip=src_ip, dst_ip=dst_ip, src_port=transport.sport, dst_port=transport.dport, protocol=6, tcp_flags=transport.flags, payload_len=len(transport.data), header_len=transport.off * 4, total_len=ip.len, window_size=transport.win) + elif isinstance(transport, dpkt.udp.UDP): + yield PacketRecord(timestamp=ts, src_ip=src_ip, dst_ip=dst_ip, src_port=transport.sport, dst_port=transport.dport, protocol=17, tcp_flags=0, payload_len=len(transport.data), header_len=8, total_len=ip.len, window_size=0) + else: + continue + except (dpkt.NeedData, dpkt.UnpackError, AttributeError): + continue + n += 1 + if max_packets is not None and n >= max_packets: + return + +def _packet_token(pkt: PacketRecord, prev_ts: float | None, direction: int) -> np.ndarray: + dt_ms = 0.0 if prev_ts is None else max(0.0, (pkt.timestamp - prev_ts) * 1000.0) + syn = int(bool(pkt.tcp_flags & SYN)) + fin = int(bool(pkt.tcp_flags & FIN)) + rst = int(bool(pkt.tcp_flags & RST)) + psh = int(bool(pkt.tcp_flags & PSH)) + ack = int(bool(pkt.tcp_flags & ACK)) + return np.array([float(np.log1p(max(pkt.total_len, 0))), float(np.log1p(dt_ms)), float(direction), syn, fin, rst, psh, ack, float(np.log1p(max(pkt.window_size, 0)))], dtype=np.float32) + +class _TokenFlow: + __slots__ = ('key_fwd', 'start_ts', 'last_ts', 'fin_count', 'tokens', 'prev_ts', 'n_pkts') + + def __init__(self, key_fwd: tuple, start_ts: float) -> None: + self.key_fwd = key_fwd + self.start_ts = start_ts + self.last_ts = start_ts + self.fin_count = 0 + self.tokens: list[np.ndarray] = [] + self.prev_ts: float | None = None + self.n_pkts: int = 0 + + def add(self, pkt: PacketRecord, is_forward: bool, max_len: int) -> None: + direction = 0 if is_forward else 1 + if len(self.tokens) < max_len: + self.tokens.append(_packet_token(pkt, self.prev_ts, direction)) + self.prev_ts = pkt.timestamp + self.last_ts = pkt.timestamp + self.n_pkts += 1 + +def stream_token_flows(packet_iter: Iterator[PacketRecord], idle_timeout: float, max_len: int, gc_every: int=200000) -> Iterator[_TokenFlow]: + active: dict[tuple, _TokenFlow] = {} + last_pkt_ts = 0.0 + n_seen = 0 + for pkt in packet_iter: + last_pkt_ts = pkt.timestamp + fwd_key = (pkt.src_ip, pkt.dst_ip, pkt.src_port, pkt.dst_port, pkt.protocol) + bwd_key = (pkt.dst_ip, pkt.src_ip, pkt.dst_port, pkt.src_port, pkt.protocol) + flow: _TokenFlow | None = None + key = fwd_key + is_forward = True + if fwd_key in active: + (flow, key, is_forward) = (active[fwd_key], fwd_key, True) + elif bwd_key in active: + (flow, key, is_forward) = (active[bwd_key], bwd_key, False) + if flow is not None and pkt.timestamp - flow.last_ts > idle_timeout: + old = active.pop(key) + yield old + flow = None + if flow is None: + flow = _TokenFlow(key_fwd=fwd_key, start_ts=pkt.timestamp) + key = fwd_key + is_forward = True + active[key] = flow + flow.add(pkt, is_forward, max_len) + if pkt.protocol == 6: + if pkt.tcp_flags & RST: + yield active.pop(key) + elif pkt.tcp_flags & FIN: + flow.fin_count += 1 + if flow.fin_count >= 2: + yield active.pop(key) + n_seen += 1 + if n_seen % gc_every == 0: + stale = [k for (k, fl) in active.items() if last_pkt_ts - fl.last_ts > idle_timeout] + for k in stale: + yield active.pop(k) + for fl in list(active.values()): + yield fl + active.clear() + +def _canonical_key(src_ip: str, dst_ip: str, src_port: int, dst_port: int, proto: int) -> tuple: + a = (src_ip, src_port) + b = (dst_ip, dst_port) + if a <= b: + return (a[0], a[1], b[0], b[1], proto) + return (b[0], b[1], a[0], a[1], proto) + +def _to_fixed_tensor(flow_tokens: list[np.ndarray], max_len: int) -> np.ndarray: + out = np.zeros((max_len, PACKET_D), dtype=np.float32) + n = min(len(flow_tokens), max_len) + if n > 0: + out[:n] = np.stack(flow_tokens[:n], axis=0) + return out + +class _WorkerChunkWriter: + + def __init__(self, root: Path, *, prefix: str, T_full: int, chunk_size: int) -> None: + self.root = Path(root) + self.root.mkdir(parents=True, exist_ok=True) + self.prefix = prefix + self.T_full = T_full + self.chunk_size = max(1, int(chunk_size)) + self._tokens: list[np.ndarray] = [] + self._records: list[dict] = [] + self._next_chunk = 0 + self.chunks: list[dict[str, str]] = [] + + def add_csv_match(self, row_i: int, tok: np.ndarray, ln: int, meta: dict) -> None: + rec = dict(meta) + rec['csv_row_idx'] = int(row_i) + rec['packet_length'] = int(ln) + self._add(tok, rec) + + def add_labeled(self, tok: np.ndarray, ln: int, meta: dict, label: str, extra: dict) -> None: + rec = dict(meta) + rec['packet_length'] = int(ln) + rec['label'] = str(label) + for (k, v) in extra.items(): + rec[str(k)] = v + self._add(tok, rec) + + def close(self) -> list[dict[str, str]]: + if self._tokens: + self._flush() + return self.chunks + + def _add(self, tok: np.ndarray, rec: dict) -> None: + self._tokens.append(tok.astype(np.float32, copy=False)) + self._records.append(rec) + if len(self._tokens) >= self.chunk_size: + self._flush() + + def _flush(self) -> None: + n = len(self._tokens) + tokens = np.empty((n, self.T_full, PACKET_D), dtype=np.float32) + for (i, tok) in enumerate(self._tokens): + tokens[i] = tok + stem = f'{self.prefix}-chunk-{self._next_chunk:06d}' + token_path = self.root / f'{stem}.npy' + meta_path = self.root / f'{stem}.parquet' + np.save(token_path, tokens, allow_pickle=False) + pd.DataFrame(self._records).to_parquet(meta_path, compression='snappy', index=False) + self.chunks.append({'tokens': str(token_path), 'meta': str(meta_path)}) + self._tokens.clear() + self._records.clear() + self._next_chunk += 1 + +def _flow_meta(fl: _TokenFlow) -> dict: + (sip, dip, sp, dp, proto) = fl.key_fwd + return {'start_ts': float(fl.start_ts), 'src_ip': str(sip), 'dst_ip': str(dip), 'src_port': int(sp), 'dst_port': int(dp), 'protocol': int(proto), 'n_pkts': int(fl.n_pkts)} + +def _build_stream_csv_index(csv_rows_for_day: dict[tuple, list[tuple[int, float]]]) -> dict[tuple, dict[str, np.ndarray]]: + out: dict[tuple, dict[str, np.ndarray]] = {} + for (ck, rows) in csv_rows_for_day.items(): + finite = [(int(row_i), float(ts)) for (row_i, ts) in rows if not np.isnan(ts)] + if not finite: + continue + finite.sort(key=lambda x: (x[1], x[0])) + row_idx = np.asarray([r for (r, _) in finite], dtype=np.int64) + ts = np.asarray([t for (_, t) in finite], dtype=np.float64) + used = np.zeros(len(finite), dtype=bool) + out[ck] = {'row_idx': row_idx, 'ts': ts, 'used': used} + return out + +def _nearest_unused_row(entry: dict[str, np.ndarray], ts: float, tolerance: float) -> tuple[int | None, float | None]: + csv_ts = entry['ts'] + used = entry['used'] + pos = int(np.searchsorted(csv_ts, ts, side='left')) + best_i: int | None = None + best_abs = float('inf') + j = pos - 1 + while j >= 0: + diff = abs(float(csv_ts[j]) - ts) + if diff > tolerance: + break + if not bool(used[j]) and diff < best_abs: + best_i = j + best_abs = diff + j -= 1 + j = pos + n = len(csv_ts) + while j < n: + diff = abs(float(csv_ts[j]) - ts) + if diff > tolerance: + break + if not bool(used[j]) and diff < best_abs: + best_i = j + best_abs = diff + j += 1 + if best_i is None: + return (None, None) + used[best_i] = True + return (int(entry['row_idx'][best_i]), ts - float(csv_ts[best_i])) + +def _extract_day_worker(day: str, pcap_files_str: list[str], csv_rows_for_day: dict[tuple, list[tuple[int, float]]], max_len: int, idle_timeout: float, time_tolerance_seconds: float, max_packets_per_pcap: int | None, spool_dir: str | None=None, worker_flush_size: int=10000, match_strategy: str='hungarian') -> dict: + if match_strategy == 'stream_nearest': + if spool_dir is None: + raise ValueError('stream_nearest requires spool_dir') + return _extract_day_worker_stream_nearest(day=day, pcap_files_str=pcap_files_str, csv_rows_for_day=csv_rows_for_day, max_len=max_len, idle_timeout=idle_timeout, time_tolerance_seconds=time_tolerance_seconds, max_packets_per_pcap=max_packets_per_pcap, spool_dir=spool_dir, worker_flush_size=worker_flush_size) + pcap_by_key: dict[tuple, list[_TokenFlow]] = defaultdict(list) + n_pkts = 0 + t_start = _time.time() + + def _counting_iter(pkt_iter): + nonlocal n_pkts + for pkt in pkt_iter: + n_pkts += 1 + yield pkt + for pcap_path_str in pcap_files_str: + pkt_iter = iter_packets(Path(pcap_path_str), max_packets=max_packets_per_pcap) + for fl in stream_token_flows(_counting_iter(pkt_iter), idle_timeout=idle_timeout, max_len=max_len): + (sip, dip, sp, dp, proto) = fl.key_fwd + ck = _canonical_key(sip, dip, sp, dp, proto) + pcap_by_key[ck].append(fl) + n_flows = sum((len(v) for v in pcap_by_key.values())) + elapsed = _time.time() - t_start + BIG = time_tolerance_seconds * 1000.0 + results: list[tuple[int, np.ndarray, int, dict]] = [] + chunk_writer = _WorkerChunkWriter(Path(spool_dir), prefix=f'day-{day}', T_full=max_len, chunk_size=worker_flush_size) if spool_dir is not None else None + n_joined = 0 + n_collision = 0 + n_csv_keys = len(csv_rows_for_day) + n_intersection = 0 + + def _emit(row_i: int, fl: _TokenFlow) -> None: + nonlocal n_joined + tok = _to_fixed_tensor(fl.tokens, max_len) + ln = min(len(fl.tokens), max_len) + meta = _flow_meta(fl) + if chunk_writer is not None: + chunk_writer.add_csv_match(row_i, tok, ln, meta) + else: + results.append((row_i, tok, ln, meta)) + n_joined += 1 + for (ck, rows) in sorted(csv_rows_for_day.items(), key=lambda kv: kv[1][0][0]): + if ck not in pcap_by_key: + continue + n_intersection += 1 + pcap_flows = pcap_by_key[ck] + csv_ts = np.array([r[1] for r in rows], dtype=np.float64) + pcap_ts = np.array([fl.start_ts for fl in pcap_flows], dtype=np.float64) + (n_csv, n_pcap) = (len(csv_ts), len(pcap_ts)) + if n_csv == 1 and n_pcap == 1: + row_i = rows[0][0] + ts = csv_ts[0] + fl = pcap_flows[0] + if not np.isnan(ts) and abs(fl.start_ts - ts) <= time_tolerance_seconds: + _emit(row_i, fl) + else: + n_collision += 1 + continue + cost = np.abs(csv_ts[:, None] - pcap_ts[None, :]) + cost[np.isnan(cost)] = BIG + cost[cost > time_tolerance_seconds] = BIG + (row_ind, col_ind) = linear_sum_assignment(cost) + for (r, c) in zip(row_ind, col_ind): + if cost[r, c] >= BIG: + n_collision += 1 + continue + row_i = rows[r][0] + fl = pcap_flows[c] + _emit(row_i, fl) + deltas: list[float] = [] + sampled = 0 + for (ck, rows) in csv_rows_for_day.items(): + if sampled >= 10000 or ck not in pcap_by_key: + if sampled >= 10000: + break + continue + (row_i, ts) = rows[0] + if np.isnan(ts): + continue + deltas.append(pcap_by_key[ck][0].start_ts - ts) + sampled += 1 + return {'day': day, 'results': results, 'chunks': [] if chunk_writer is None else chunk_writer.close(), 'n_joined': n_joined, 'n_pkts': n_pkts, 'n_flows': n_flows, 'elapsed': elapsed, 'n_pcap_keys': len(pcap_by_key), 'n_csv_keys': n_csv_keys, 'n_intersection': n_intersection, 'n_collision': n_collision, 'deltas': deltas, 'match_strategy': match_strategy} + +def _extract_day_worker_stream_nearest(*, day: str, pcap_files_str: list[str], csv_rows_for_day: dict[tuple, list[tuple[int, float]]], max_len: int, idle_timeout: float, time_tolerance_seconds: float, max_packets_per_pcap: int | None, spool_dir: str, worker_flush_size: int) -> dict: + t_start = _time.time() + n_pkts = 0 + n_flows = 0 + n_joined = 0 + n_collision = 0 + seen_pcap_keys: set[tuple] = set() + intersected_keys: set[tuple] = set() + deltas: list[float] = [] + csv_index = _build_stream_csv_index(csv_rows_for_day) + chunk_writer = _WorkerChunkWriter(Path(spool_dir), prefix=f'day-{day}', T_full=max_len, chunk_size=worker_flush_size) + + def _counting_iter(pkt_iter): + nonlocal n_pkts + for pkt in pkt_iter: + n_pkts += 1 + yield pkt + for pcap_path_str in pcap_files_str: + pkt_iter = iter_packets(Path(pcap_path_str), max_packets=max_packets_per_pcap) + for fl in stream_token_flows(_counting_iter(pkt_iter), idle_timeout=idle_timeout, max_len=max_len): + n_flows += 1 + (sip, dip, sp, dp, proto) = fl.key_fwd + ck = _canonical_key(sip, dip, sp, dp, proto) + seen_pcap_keys.add(ck) + entry = csv_index.get(ck) + if entry is None: + continue + intersected_keys.add(ck) + (row_i, delta) = _nearest_unused_row(entry, float(fl.start_ts), time_tolerance_seconds) + if row_i is None: + n_collision += 1 + continue + tok = _to_fixed_tensor(fl.tokens, max_len) + ln = min(len(fl.tokens), max_len) + chunk_writer.add_csv_match(row_i, tok, ln, _flow_meta(fl)) + n_joined += 1 + if delta is not None and len(deltas) < 10000: + deltas.append(float(delta)) + elapsed = _time.time() - t_start + return {'day': day, 'results': [], 'chunks': chunk_writer.close(), 'n_joined': n_joined, 'n_pkts': n_pkts, 'n_flows': n_flows, 'elapsed': elapsed, 'n_pcap_keys': len(seen_pcap_keys), 'n_csv_keys': len(csv_rows_for_day), 'n_intersection': len(intersected_keys), 'n_collision': n_collision, 'deltas': deltas, 'match_strategy': 'stream_nearest'} + +def _print_day_stats(res: dict) -> None: + day = res['day'] + strategy = res.get('match_strategy', 'hungarian') + print(f"[{day}] {res['n_pkts']:,} pkts → {res['n_flows']:,} flows in {res['elapsed']:.1f}s match={strategy} ({res['n_pkts'] / max(res['elapsed'], 0.001) / 1000000.0:.2f}M pkts/s)") + print(f" pcap_keys={res['n_pcap_keys']:,} csv_keys={res['n_csv_keys']:,} intersection={res['n_intersection']:,} joined={int(res.get('n_joined', len(res.get('results', ())))):,} within-key-miss={res['n_collision']:,}") + deltas = res.get('deltas') or [] + if deltas: + arr = np.asarray(deltas, dtype=np.float64) + print(f' time-delta (pcap_start - csv_ts), seconds: median={np.median(arr):+.2f} mean={arr.mean():+.2f} std={arr.std():.2f} p05={np.percentile(arr, 5):+.2f} p95={np.percentile(arr, 95):+.2f}') + med = float(np.median(arr)) + if abs(med) > 2.0: + print(f' -> median |{med:.1f}s| > 2s: rerun with --time-offset {med:.0f}') + +def extract_dataset(*, csv_rows_by_day: dict[str, dict[tuple, list[tuple[int, float]]]], labels_by_row: np.ndarray, pcap_files_by_day: dict[str, list[Path]], out_packets: Path, out_flows: Path, out_store: Path | None=None, shard_size: int=100000, worker_flush_size: int=10000, spool_dir: Path | None=None, match_strategy: str | None=None, T_full: int=256, idle_timeout: float=120.0, time_tolerance_seconds: float=2.0, max_packets_per_pcap: int | None=None, n_jobs: int=0) -> None: + N_csv = len(labels_by_row) + print(f'[extract_dataset] N_csv={N_csv:,} T_full={T_full} days={sorted(csv_rows_by_day.keys())}') + if match_strategy is None: + match_strategy = 'stream_nearest' if out_store is not None else 'hungarian' + if match_strategy not in ('hungarian', 'stream_nearest'): + raise ValueError("match_strategy must be 'hungarian' or 'stream_nearest'") + if match_strategy == 'stream_nearest' and out_store is None: + raise ValueError('stream_nearest is only supported with --out-store') + print(f'[extract_dataset] match_strategy={match_strategy}') + tasks: list[tuple] = [] + for (day, rows_dict) in csv_rows_by_day.items(): + pcap_files = pcap_files_by_day.get(day, []) + if not pcap_files: + print(f'[{day}] NO pcap files — skipping ({len(rows_dict):,} CSV keys unmatched)') + continue + tasks.append((day, [str(p) for p in pcap_files], dict(rows_dict))) + if not tasks: + raise RuntimeError('No days with pcap files — nothing to extract.') + if n_jobs <= 0: + n_jobs = min(len(tasks), os.cpu_count() or 1) + print(f'[extract_dataset] running {len(tasks)} day(s) with {n_jobs} worker(s)') + store_writer: PacketShardWriter | None = None + spool_root: Path | None = None + if out_store is not None: + print(f'[extract_dataset] sharded output enabled: {out_store} shard_size={shard_size:,}') + store_writer = PacketShardWriter(out_store, shard_size=shard_size, T_full=T_full, D=PACKET_D, overwrite=True) + if spool_dir is None: + out_store_parent = Path(out_store).parent + out_store_parent.mkdir(parents=True, exist_ok=True) + spool_root = Path(tempfile.mkdtemp(prefix=f'.{Path(out_store).name}.spool.', dir=out_store_parent)) + else: + spool_root = Path(spool_dir) + if spool_root.exists(): + shutil.rmtree(spool_root) + spool_root.mkdir(parents=True, exist_ok=True) + print(f'[extract_dataset] worker spool={spool_root} flush_size={worker_flush_size:,}') + tok_chunks: list[np.ndarray] = [] + len_chunks: list[np.ndarray] = [] + row_chunks: list[np.ndarray] = [] + meta_chunks: list[list[dict]] = [] + total_joined = 0 + + def _materialize_results(results: list[tuple[int, np.ndarray, int, dict]]) -> tuple[np.ndarray, np.ndarray, np.ndarray, list[dict]]: + results = sorted(results, key=lambda x: x[0]) + n = len(results) + tok_arr = np.empty((n, T_full, PACKET_D), dtype=np.float32) + len_arr = np.empty(n, dtype=np.int32) + row_arr = np.empty(n, dtype=np.int64) + meta_arr: list[dict] = [None] * n + for (i, (row_i, tok, ln, meta)) in enumerate(results): + tok_arr[i] = tok + len_arr[i] = ln + row_arr[i] = row_i + meta_arr[i] = meta + return (tok_arr, len_arr, row_arr, meta_arr) + + def _flows_from_meta(row_arr: np.ndarray, meta_arr: list[dict]) -> pd.DataFrame: + labels = labels_by_row[row_arr].astype(str) + return pd.DataFrame({'label': labels, 'start_ts': np.asarray([m['start_ts'] for m in meta_arr], dtype=np.float64), 'src_ip': np.asarray([m['src_ip'] for m in meta_arr], dtype=object), 'dst_ip': np.asarray([m['dst_ip'] for m in meta_arr], dtype=object), 'src_port': np.asarray([m['src_port'] for m in meta_arr], dtype=np.uint32), 'dst_port': np.asarray([m['dst_port'] for m in meta_arr], dtype=np.uint32), 'protocol': np.asarray([m['protocol'] for m in meta_arr], dtype=np.uint8), 'n_pkts': np.asarray([m['n_pkts'] for m in meta_arr], dtype=np.uint32)}) + + def _append_spool_chunks(res: dict) -> None: + chunks = res.get('chunks') or [] + for chunk in chunks: + tokens = np.load(chunk['tokens'], mmap_mode='r') + meta_df = pd.read_parquet(chunk['meta']) + if meta_df.empty: + continue + meta_df = meta_df.assign(__token_row=np.arange(len(meta_df), dtype=np.int64)) + meta_df = meta_df.sort_values('csv_row_idx', kind='stable').reset_index(drop=True) + row_arr = meta_df['csv_row_idx'].to_numpy(dtype=np.int64) + lengths = meta_df['packet_length'].to_numpy(dtype=np.int32) + order = meta_df['__token_row'].to_numpy(dtype=np.int64) + labels = labels_by_row[row_arr].astype(str) + flows = pd.DataFrame({'label': labels, 'start_ts': meta_df['start_ts'].to_numpy(dtype=np.float64), 'src_ip': meta_df['src_ip'].to_numpy(dtype=object), 'dst_ip': meta_df['dst_ip'].to_numpy(dtype=object), 'src_port': meta_df['src_port'].to_numpy(dtype=np.uint32), 'dst_port': meta_df['dst_port'].to_numpy(dtype=np.uint32), 'protocol': meta_df['protocol'].to_numpy(dtype=np.uint8), 'n_pkts': meta_df['n_pkts'].to_numpy(dtype=np.uint32)}) + assert store_writer is not None + store_writer.add_batch(np.asarray(tokens[order]), lengths, flows) + + def _absorb(res: dict, *, print_stats: bool=True) -> None: + if print_stats: + _print_day_stats(res) + results = res['results'] + if not results: + return + (tok_arr, len_arr, row_arr, meta_arr) = _materialize_results(results) + if store_writer is not None: + store_writer.add_batch(tok_arr, len_arr, _flows_from_meta(row_arr, meta_arr)) + else: + tok_chunks.append(tok_arr) + len_chunks.append(len_arr) + row_chunks.append(row_arr) + meta_chunks.append(meta_arr) + if n_jobs <= 1: + try: + for (i, (day, pcaps, rows)) in enumerate(tasks): + task_spool = None if spool_root is None else str(spool_root / f'task-{i:04d}-{day}') + res = _extract_day_worker(day, pcaps, rows, T_full, idle_timeout, time_tolerance_seconds, max_packets_per_pcap, task_spool, worker_flush_size, match_strategy) + _print_day_stats(res) + total_joined += int(res.get('n_joined', 0)) + if store_writer is not None: + _append_spool_chunks(res) + else: + _absorb(res, print_stats=False) + finally: + if spool_root is not None: + shutil.rmtree(spool_root, ignore_errors=True) + else: + try: + with ProcessPoolExecutor(max_workers=n_jobs) as pool: + futs = [] + for (i, (day, pcaps, rows)) in enumerate(tasks): + task_spool = None if spool_root is None else str(spool_root / f'task-{i:04d}-{day}') + futs.append(pool.submit(_extract_day_worker, day, pcaps, rows, T_full, idle_timeout, time_tolerance_seconds, max_packets_per_pcap, task_spool, worker_flush_size, match_strategy)) + if store_writer is not None: + completed: dict[str, dict] = {} + for fut in as_completed(futs): + res = fut.result() + _print_day_stats(res) + completed[res['day']] = res + for (day, _, _) in tasks: + if day in completed: + total_joined += int(completed[day].get('n_joined', 0)) + _append_spool_chunks(completed[day]) + else: + for fut in as_completed(futs): + _absorb(fut.result()) + finally: + if spool_root is not None: + shutil.rmtree(spool_root, ignore_errors=True) + if store_writer is not None: + if total_joined == 0: + raise RuntimeError('No matched flows — check timestamps (--time-offset) and pcap×CSV correspondence.') + store_writer.close() + print(f'[extract_dataset] wrote sharded store {out_store}') + return + if not tok_chunks: + raise RuntimeError('No matched flows — check timestamps (--time-offset) and pcap×CSV correspondence.') + tokens = np.concatenate(tok_chunks, axis=0) + lengths = np.concatenate(len_chunks, axis=0) + csv_rows = np.concatenate(row_chunks, axis=0) + meta_list: list[dict] = [m for chunk in meta_chunks for m in chunk] + del tok_chunks, len_chunks, row_chunks, meta_chunks + order = np.argsort(csv_rows, kind='stable') + tokens = tokens[order] + lengths = lengths[order] + csv_rows = csv_rows[order] + meta_list = [meta_list[i] for i in order] + N_matched = len(tokens) + labels = labels_by_row[csv_rows].astype(str) + flow_id = np.arange(N_matched, dtype=np.uint64) + print(f'\n[extract_dataset] matched {N_matched:,}/{N_csv:,} ({100.0 * N_matched / max(N_csv, 1):.2f}%)') + print(f'[extract_dataset] label distribution (matched rows):') + (ulabels, counts) = np.unique(labels, return_counts=True) + for (lbl, cnt) in sorted(zip(ulabels, counts), key=lambda x: -x[1]): + print(f' {lbl:<40s} {cnt:>10,}') + out_packets.parent.mkdir(parents=True, exist_ok=True) + np.savez_compressed(out_packets, packet_tokens=tokens, packet_lengths=lengths, flow_id=flow_id) + print(f'[extract_dataset] wrote {out_packets} ({out_packets.stat().st_size / 1000000000.0:.2f} GB)') + out_flows.parent.mkdir(parents=True, exist_ok=True) + flow_df = pd.DataFrame({'flow_id': flow_id, 'label': labels, 'start_ts': np.asarray([m['start_ts'] for m in meta_list], dtype=np.float64), 'src_ip': np.asarray([m['src_ip'] for m in meta_list], dtype=object), 'dst_ip': np.asarray([m['dst_ip'] for m in meta_list], dtype=object), 'src_port': np.asarray([m['src_port'] for m in meta_list], dtype=np.uint32), 'dst_port': np.asarray([m['dst_port'] for m in meta_list], dtype=np.uint32), 'protocol': np.asarray([m['protocol'] for m in meta_list], dtype=np.uint8), 'n_pkts': np.asarray([m['n_pkts'] for m in meta_list], dtype=np.uint32)}) + flow_df.to_parquet(out_flows, compression='snappy', index=False) + print(f'[extract_dataset] wrote {out_flows} ({out_flows.stat().st_size / 1000000.0:.2f} MB)') + _write_canonical_flow_features(tokens=tokens, lengths=lengths, flow_id=flow_id, labels=labels, out_path=out_flows.parent / 'flow_features.parquet') + +def _write_canonical_flow_features(*, tokens: np.ndarray, lengths: np.ndarray, flow_id: np.ndarray, labels: np.ndarray, out_path: Path) -> None: + sys.path.insert(0, str(Path(__file__).resolve().parents[1])) + from common.data_contract import CANONICAL_FLOW_FEATURE_NAMES, compute_flow_features_from_packets + print(f'[extract_dataset] computing canonical {len(CANONICAL_FLOW_FEATURE_NAMES)}-d flow features from packet tokens ...') + feats = compute_flow_features_from_packets(tokens, lengths) + out_path.parent.mkdir(parents=True, exist_ok=True) + df = pd.DataFrame({'flow_id': flow_id, 'label': labels}) + for (i, name) in enumerate(CANONICAL_FLOW_FEATURE_NAMES): + df[name] = feats[:, i] + df.to_parquet(out_path, compression='snappy', index=False) + print(f'[extract_dataset] wrote {out_path} ({out_path.stat().st_size / 1000000.0:.2f} MB)') + +def _extract_single_pcap_worker(pcap_path_str: str, label: str, extra: dict, max_len: int, idle_timeout: float, max_packets_per_pcap: int | None, spool_dir: str | None=None, worker_flush_size: int=10000) -> dict: + t_start = _time.time() + n_pkts = 0 + n_flows = 0 + results: list[tuple[np.ndarray, int, dict]] = [] + chunk_writer = _WorkerChunkWriter(Path(spool_dir), prefix=f'pcap-{Path(pcap_path_str).stem}', T_full=max_len, chunk_size=worker_flush_size) if spool_dir is not None else None + + def _counting_iter(pkt_iter): + nonlocal n_pkts + for pkt in pkt_iter: + n_pkts += 1 + yield pkt + pkt_iter = iter_packets(Path(pcap_path_str), max_packets=max_packets_per_pcap) + for fl in stream_token_flows(_counting_iter(pkt_iter), idle_timeout=idle_timeout, max_len=max_len): + (sip, dip, sp, dp, proto) = fl.key_fwd + meta = {'start_ts': float(fl.start_ts), 'src_ip': str(sip), 'dst_ip': str(dip), 'src_port': int(sp), 'dst_port': int(dp), 'protocol': int(proto), 'n_pkts': int(fl.n_pkts)} + tok = _to_fixed_tensor(fl.tokens, max_len) + ln = min(len(fl.tokens), max_len) + if chunk_writer is not None: + chunk_writer.add_labeled(tok, ln, meta, label, extra) + else: + results.append((tok, ln, meta)) + n_flows += 1 + elapsed = _time.time() - t_start + return {'pcap': pcap_path_str, 'label': label, 'extra': extra, 'results': results, 'chunks': [] if chunk_writer is None else chunk_writer.close(), 'n_pkts': n_pkts, 'n_flows': n_flows, 'elapsed': elapsed} + +def extract_labeled_pcaps(*, pcap_files_with_labels: list[tuple[Path, str, dict]], out_packets: Path, out_flows: Path, out_store: Path | None=None, shard_size: int=100000, worker_flush_size: int=10000, spool_dir: Path | None=None, T_full: int=256, idle_timeout: float=120.0, max_packets_per_pcap: int | None=None, n_jobs: int=0, extra_column_names: tuple[str, ...]=()) -> None: + N_pcap = len(pcap_files_with_labels) + print(f'[extract_labeled_pcaps] n_pcaps={N_pcap} T_full={T_full} extra_cols={extra_column_names}') + for (p, lbl, extra) in pcap_files_with_labels[:10]: + print(f' {lbl:<20s} {Path(p).name:<60s} extra={extra}') + if N_pcap > 10: + print(f' ... ({N_pcap - 10} more)') + if n_jobs <= 0: + n_jobs = min(N_pcap, os.cpu_count() or 1) + print(f'[extract_labeled_pcaps] running {N_pcap} pcap(s) with {n_jobs} worker(s)') + store_writer: PacketShardWriter | None = None + spool_root: Path | None = None + if out_store is not None: + print(f'[extract_labeled_pcaps] sharded output enabled: {out_store} shard_size={shard_size:,}') + store_writer = PacketShardWriter(out_store, shard_size=shard_size, T_full=T_full, D=PACKET_D, overwrite=True) + if spool_dir is None: + out_store_parent = Path(out_store).parent + out_store_parent.mkdir(parents=True, exist_ok=True) + spool_root = Path(tempfile.mkdtemp(prefix=f'.{Path(out_store).name}.spool.', dir=out_store_parent)) + else: + spool_root = Path(spool_dir) + if spool_root.exists(): + shutil.rmtree(spool_root) + spool_root.mkdir(parents=True, exist_ok=True) + print(f'[extract_labeled_pcaps] worker spool={spool_root} flush_size={worker_flush_size:,}') + tok_chunks: list[np.ndarray] = [] + len_chunks: list[np.ndarray] = [] + meta_chunks: list[list[dict]] = [] + label_chunks: list[np.ndarray] = [] + extra_chunks: list[dict[str, list]] = [] + total_flows = 0 + + def _flows_for_labeled_chunk(res: dict, meta_arr: list[dict], n: int) -> pd.DataFrame: + cols = {'label': np.full(n, res['label'], dtype=object), 'start_ts': np.asarray([m['start_ts'] for m in meta_arr], dtype=np.float64), 'src_ip': np.asarray([m['src_ip'] for m in meta_arr], dtype=object), 'dst_ip': np.asarray([m['dst_ip'] for m in meta_arr], dtype=object), 'src_port': np.asarray([m['src_port'] for m in meta_arr], dtype=np.uint32), 'dst_port': np.asarray([m['dst_port'] for m in meta_arr], dtype=np.uint32), 'protocol': np.asarray([m['protocol'] for m in meta_arr], dtype=np.uint8), 'n_pkts': np.asarray([m['n_pkts'] for m in meta_arr], dtype=np.uint32)} + for col in extra_column_names: + cols[col] = np.full(n, res['extra'].get(col, ''), dtype=object) + return pd.DataFrame(cols) + + def _append_labeled_spool_chunks(res: dict) -> None: + chunks = res.get('chunks') or [] + for chunk in chunks: + tokens = np.load(chunk['tokens'], mmap_mode='r') + flows = pd.read_parquet(chunk['meta']) + if flows.empty: + continue + flows = flows.assign(__token_row=np.arange(len(flows), dtype=np.int64)) + sort_keys = ['label', 'src_ip', 'dst_ip', 'src_port', 'dst_port', 'protocol', 'start_ts'] + flows = flows.sort_values(sort_keys, kind='stable').reset_index(drop=True) + order = flows['__token_row'].to_numpy(dtype=np.int64) + lengths = flows['packet_length'].to_numpy(dtype=np.int32) + flows = flows.drop(columns=['packet_length', '__token_row']) + assert store_writer is not None + store_writer.add_batch(np.asarray(tokens[order]), lengths, flows) + + def _absorb(res: dict, *, print_stats: bool=True) -> None: + pcap_name = Path(res['pcap']).name + lbl = res['label'] + extra = res['extra'] + if print_stats: + print(f"[pcap:{pcap_name}] label={lbl} {res['n_pkts']:,} pkts → {res['n_flows']:,} flows in {res['elapsed']:.1f}s ({res['n_pkts'] / max(res['elapsed'], 0.001) / 1000000.0:.2f}M pkts/s)") + if not res['results']: + return + n = len(res['results']) + tok_arr = np.empty((n, T_full, PACKET_D), dtype=np.float32) + len_arr = np.empty(n, dtype=np.int32) + meta_arr: list[dict] = [None] * n + for (i, (tok, ln, meta)) in enumerate(res['results']): + tok_arr[i] = tok + len_arr[i] = ln + meta_arr[i] = meta + if store_writer is not None: + flows = _flows_for_labeled_chunk(res, meta_arr, n) + order = np.lexsort((flows['start_ts'].to_numpy(dtype=np.float64), flows['protocol'].to_numpy(dtype=np.int64), flows['dst_port'].to_numpy(dtype=np.int64), flows['src_port'].to_numpy(dtype=np.int64), flows['dst_ip'].to_numpy(dtype=object), flows['src_ip'].to_numpy(dtype=object), flows['label'].to_numpy(dtype=object))) + store_writer.add_batch(tok_arr[order], len_arr[order], flows.iloc[order].reset_index(drop=True)) + else: + tok_chunks.append(tok_arr) + len_chunks.append(len_arr) + meta_chunks.append(meta_arr) + label_chunks.append(np.full(n, lbl, dtype=object)) + ex: dict[str, list] = {} + for col in extra_column_names: + val = extra.get(col, '') + ex[col] = [val] * n + extra_chunks.append(ex) + if n_jobs <= 1: + try: + for (i, (p, lbl, extra)) in enumerate(pcap_files_with_labels): + task_spool = None if spool_root is None else str(spool_root / f'task-{i:04d}-{Path(p).stem}') + res = _extract_single_pcap_worker(str(p), lbl, extra, T_full, idle_timeout, max_packets_per_pcap, task_spool, worker_flush_size) + _absorb(res) + total_flows += int(res.get('n_flows', 0)) + if store_writer is not None: + _append_labeled_spool_chunks(res) + finally: + if spool_root is not None: + shutil.rmtree(spool_root, ignore_errors=True) + else: + try: + with ProcessPoolExecutor(max_workers=n_jobs) as pool: + futs = [] + for (i, (p, lbl, extra)) in enumerate(pcap_files_with_labels): + task_spool = None if spool_root is None else str(spool_root / f'task-{i:04d}-{Path(p).stem}') + futs.append(pool.submit(_extract_single_pcap_worker, str(p), lbl, extra, T_full, idle_timeout, max_packets_per_pcap, task_spool, worker_flush_size)) + if store_writer is not None: + completed: dict[str, dict] = {} + for fut in as_completed(futs): + res = fut.result() + pcap_name = Path(res['pcap']).name + print(f"[pcap:{pcap_name}] label={res['label']} {res['n_pkts']:,} pkts → {res['n_flows']:,} flows in {res['elapsed']:.1f}s ({res['n_pkts'] / max(res['elapsed'], 0.001) / 1000000.0:.2f}M pkts/s)") + completed[str(res['pcap'])] = res + for (p, _, _) in pcap_files_with_labels: + res = completed.get(str(p)) + if res is not None: + total_flows += int(res.get('n_flows', 0)) + _append_labeled_spool_chunks(res) + else: + for fut in as_completed(futs): + _absorb(fut.result()) + finally: + if spool_root is not None: + shutil.rmtree(spool_root, ignore_errors=True) + if store_writer is not None: + if total_flows == 0: + raise RuntimeError('No flows emitted — check pcap contents.') + store_writer.close() + print(f'[extract_labeled_pcaps] wrote sharded store {out_store}') + return + if not tok_chunks: + raise RuntimeError('No flows emitted — check pcap contents.') + tokens = np.concatenate(tok_chunks, axis=0) + lengths = np.concatenate(len_chunks, axis=0) + meta_list: list[dict] = [m for chunk in meta_chunks for m in chunk] + labels = np.concatenate(label_chunks, axis=0) + extra_dict: dict[str, list] = {col: [] for col in extra_column_names} + for chunk in extra_chunks: + for col in extra_column_names: + extra_dict[col].extend(chunk[col]) + del tok_chunks, len_chunks, meta_chunks, label_chunks, extra_chunks + sip_arr = np.asarray([m['src_ip'] for m in meta_list], dtype=object) + dip_arr = np.asarray([m['dst_ip'] for m in meta_list], dtype=object) + sp_arr = np.asarray([m['src_port'] for m in meta_list], dtype=np.int64) + dp_arr = np.asarray([m['dst_port'] for m in meta_list], dtype=np.int64) + pr_arr = np.asarray([m['protocol'] for m in meta_list], dtype=np.int64) + ts_arr = np.asarray([m['start_ts'] for m in meta_list], dtype=np.float64) + order = np.lexsort((ts_arr, pr_arr, dp_arr, sp_arr, dip_arr, sip_arr, labels)) + tokens = tokens[order] + lengths = lengths[order] + labels = labels[order] + meta_list = [meta_list[i] for i in order] + for col in extra_column_names: + extra_dict[col] = [extra_dict[col][i] for i in order] + N = len(tokens) + flow_id = np.arange(N, dtype=np.uint64) + print(f'\n[extract_labeled_pcaps] total flows: {N:,}') + print(f'[extract_labeled_pcaps] label distribution:') + (ulabels, counts) = np.unique(labels, return_counts=True) + for (lbl, cnt) in sorted(zip(ulabels, counts), key=lambda x: -x[1]): + print(f' {lbl:<40s} {cnt:>10,}') + out_packets.parent.mkdir(parents=True, exist_ok=True) + np.savez_compressed(out_packets, packet_tokens=tokens, packet_lengths=lengths, flow_id=flow_id) + print(f'[extract_labeled_pcaps] wrote {out_packets} ({out_packets.stat().st_size / 1000000000.0:.2f} GB)') + out_flows.parent.mkdir(parents=True, exist_ok=True) + cols = {'flow_id': flow_id, 'label': labels.astype(str), 'start_ts': np.asarray([m['start_ts'] for m in meta_list], dtype=np.float64), 'src_ip': np.asarray([m['src_ip'] for m in meta_list], dtype=object), 'dst_ip': np.asarray([m['dst_ip'] for m in meta_list], dtype=object), 'src_port': np.asarray([m['src_port'] for m in meta_list], dtype=np.uint32), 'dst_port': np.asarray([m['dst_port'] for m in meta_list], dtype=np.uint32), 'protocol': np.asarray([m['protocol'] for m in meta_list], dtype=np.uint8), 'n_pkts': np.asarray([m['n_pkts'] for m in meta_list], dtype=np.uint32)} + for col in extra_column_names: + cols[col] = np.asarray(extra_dict[col], dtype=object) + flow_df = pd.DataFrame(cols) + flow_df.to_parquet(out_flows, compression='snappy', index=False) + print(f'[extract_labeled_pcaps] wrote {out_flows} ({out_flows.stat().st_size / 1000000.0:.2f} MB) cols={list(flow_df.columns)}') + _write_canonical_flow_features(tokens=tokens, lengths=lengths, flow_id=flow_id, labels=labels.astype(str), out_path=out_flows.parent / 'flow_features.parquet') diff --git a/scripts/generate_flow_features.py b/scripts/generate_flow_features.py new file mode 100644 index 0000000..eae99b5 --- /dev/null +++ b/scripts/generate_flow_features.py @@ -0,0 +1,97 @@ +from __future__ import annotations +import argparse +import sys +import time +from pathlib import Path +import numpy as np +import pandas as pd +sys.path.insert(0, str(Path(__file__).resolve().parents[1])) +from common.data_contract import CANONICAL_FLOW_FEATURE_NAMES, compute_flow_features_from_packets + +def _from_npz(args: argparse.Namespace) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]: + print(f'[read] {args.packets_npz}') + pz = np.load(args.packets_npz) + tokens = pz['packet_tokens'] + lens = pz['packet_lengths'].astype(np.int32) + packet_flow_id = pz['flow_id'] if 'flow_id' in pz.files else None + T_stored = tokens.shape[1] + if args.T is not None: + if args.T > T_stored: + raise ValueError(f'requested T={args.T} > stored {T_stored}') + tokens = tokens[:, :args.T, :] + lens = np.minimum(lens, args.T).astype(np.int32) + print(f'[read] {args.flows_parquet}') + flows = pd.read_parquet(args.flows_parquet, columns=['flow_id', 'label']) + if len(flows) != len(tokens): + raise ValueError(f'row count mismatch: packets={len(tokens):,} flows={len(flows):,}') + flow_id = np.asarray(flows['flow_id'].to_numpy(), dtype=np.uint64) + if packet_flow_id is not None: + if not np.array_equal(flow_id, packet_flow_id.astype(np.uint64)): + raise ValueError('packets.npz flow_id != flows.parquet flow_id') + labels = flows['label'].astype(str).to_numpy() + print(f'[compute] {len(tokens):,} flows × T={tokens.shape[1]} → {len(CANONICAL_FLOW_FEATURE_NAMES)} features ...') + t0 = time.time() + feats = compute_flow_features_from_packets(tokens, lens) + dt = time.time() - t0 + print(f'[compute] {dt:.1f}s ({len(tokens) / max(dt, 1e-06):.0f} flows/s)') + return (feats, flow_id, labels, np.array([T_stored if args.T is None else args.T])) + +def _from_store(args: argparse.Namespace) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]: + sys.path.insert(0, str(Path(__file__).resolve().parents[1] / 'Packet_CFM')) + from packet_store import PacketShardStore + store = PacketShardStore.open(args.source_store) + T_stored = int(store.manifest['packet_length'].max()) + T = args.T if args.T is not None else T_stored + if T > T_stored: + raise ValueError(f'requested T={T} > stored max {T_stored}') + print(f'[read] {args.flows_parquet}') + flows = pd.read_parquet(args.flows_parquet, columns=['flow_id', 'label']) + n = len(flows) + store_flows = store.read_flows(columns=['flow_id']) + if len(store_flows) != n: + raise ValueError(f'store has {len(store_flows):,} rows but flows.parquet has {n:,}') + if not np.array_equal(store_flows['flow_id'].to_numpy(dtype=np.uint64), flows['flow_id'].to_numpy(dtype=np.uint64)): + raise ValueError('store flow_id ordering differs from flows.parquet') + flow_id = flows['flow_id'].to_numpy(dtype=np.uint64) + labels = flows['label'].astype(str).to_numpy() + feats = np.zeros((n, len(CANONICAL_FLOW_FEATURE_NAMES)), dtype=np.float32) + print(f'[stream] {n:,} flows × T={T} (full={T_stored}), batch={args.batch} ...') + t0 = time.time() + all_idx = np.arange(n, dtype=np.int64) + for start in range(0, n, args.batch): + end = min(start + args.batch, n) + idx = all_idx[start:end] + (tok, lens) = store.read_packets(idx, T=T) + lens = np.minimum(lens, T).astype(np.int32) + feats[start:end] = compute_flow_features_from_packets(tok, lens) + if start // args.batch % 20 == 0 or end == n: + dt = time.time() - t0 + rate = end / max(dt, 1e-06) + eta = (n - end) / max(rate, 1.0) + print(f'[stream] {end:,}/{n:,} dt={dt:.1f}s rate={rate:.0f} flows/s ETA={eta:.0f}s', flush=True) + return (feats, flow_id, labels, np.array([T])) + +def main() -> None: + p = argparse.ArgumentParser(description=__doc__) + p.add_argument('--packets-npz', type=Path, default=None, help='Monolithic packets.npz path (mutually exclusive with --source-store).') + p.add_argument('--source-store', type=Path, default=None, help='PacketShardStore directory (mutually exclusive with --packets-npz).') + p.add_argument('--flows-parquet', type=Path, required=True) + p.add_argument('--out', type=Path, required=True) + p.add_argument('--T', type=int, default=None, help='Truncate packet sequences to first T positions (default: use stored T_full).') + p.add_argument('--batch', type=int, default=100000, help='Batch size when streaming from --source-store.') + args = p.parse_args() + if (args.packets_npz is None) == (args.source_store is None): + p.error('pass exactly one of --packets-npz or --source-store') + if args.packets_npz is not None: + (feats, flow_id, labels, _) = _from_npz(args) + else: + (feats, flow_id, labels, _) = _from_store(args) + args.out.parent.mkdir(parents=True, exist_ok=True) + df = pd.DataFrame({'flow_id': flow_id, 'label': labels}) + for (i, name) in enumerate(CANONICAL_FLOW_FEATURE_NAMES): + df[name] = feats[:, i] + df.to_parquet(args.out, compression='snappy', index=False) + sz_mb = args.out.stat().st_size / 1000000.0 + print(f'[write] {args.out} ({sz_mb:.2f} MB, {len(df):,} rows × {len(df.columns)} cols)') +if __name__ == '__main__': + main() diff --git a/scripts/generate_spectral_features.py b/scripts/generate_spectral_features.py new file mode 100644 index 0000000..0273dd4 --- /dev/null +++ b/scripts/generate_spectral_features.py @@ -0,0 +1,122 @@ +from __future__ import annotations +import argparse +import sys +import time +from pathlib import Path +import numpy as np +import pandas as pd +sys.path.insert(0, str(Path(__file__).resolve().parents[1])) +from common.data_contract import CANONICAL_FLOW_FEATURE_NAMES + +def compute_spectral_features(packet_tokens: np.ndarray, packet_lengths: np.ndarray, n_bands: int=8) -> np.ndarray: + (N, T, _) = packet_tokens.shape + mask = (np.arange(T)[None, :] < packet_lengths[:, None]).astype(np.float32) + sig = packet_tokens[..., :2].astype(np.float32) * mask[..., None] + Z = np.fft.rfft(sig, axis=1) + if n_bands > Z.shape[1]: + raise ValueError(f'n_bands={n_bands} > {Z.shape[1]} available bins') + Z_K = Z[:, :n_bands] + size_re = Z_K[..., 0].real.astype(np.float32) + size_im = Z_K[..., 0].imag.astype(np.float32) + iat_re = Z_K[..., 1].real.astype(np.float32) + iat_im = Z_K[..., 1].imag.astype(np.float32) + out = np.concatenate([size_re, size_im, iat_re, iat_im], axis=1) + return out + +def _spectral_column_names(n_bands: int) -> list[str]: + cols: list[str] = [] + for prefix in ('spec_size_re', 'spec_size_im', 'spec_iat_re', 'spec_iat_im'): + for k in range(n_bands): + cols.append(f'{prefix}_K{k}') + return cols + +def _from_npz(args: argparse.Namespace) -> tuple[np.ndarray, np.ndarray, np.ndarray]: + print(f'[read] {args.packets_npz}') + pz = np.load(args.packets_npz) + tokens = pz['packet_tokens'] + lens = pz['packet_lengths'].astype(np.int32) + if args.T is not None: + if args.T > tokens.shape[1]: + raise ValueError(f'requested T={args.T} > stored {tokens.shape[1]}') + tokens = tokens[:, :args.T, :] + lens = np.minimum(lens, args.T).astype(np.int32) + flow_id = pz['flow_id'].astype(np.uint64) if 'flow_id' in pz.files else None + print(f'[compute] {len(tokens):,} flows × T={tokens.shape[1]} → {4 * args.n_bands} spectral cols ...') + t0 = time.time() + spec = compute_spectral_features(tokens, lens, n_bands=args.n_bands) + print(f'[compute] {time.time() - t0:.1f}s') + return (spec, flow_id, lens) + +def _from_store(args: argparse.Namespace) -> tuple[np.ndarray, np.ndarray, np.ndarray]: + sys.path.insert(0, str(Path(__file__).resolve().parents[1] / 'Packet_CFM')) + from packet_store import PacketShardStore + store = PacketShardStore.open(args.source_store) + T_stored = int(store.manifest['packet_length'].max()) + T = args.T if args.T is not None else T_stored + if T > T_stored: + raise ValueError(f'requested T={T} > stored max {T_stored}') + store_flows = store.read_flows(columns=['flow_id']) + n = len(store_flows) + flow_id = store_flows['flow_id'].to_numpy(dtype=np.uint64) + spec = np.zeros((n, 4 * args.n_bands), dtype=np.float32) + print(f'[stream] {n:,} flows × T={T} (full={T_stored}), batch={args.batch} ...') + t0 = time.time() + all_idx = np.arange(n, dtype=np.int64) + for start in range(0, n, args.batch): + end = min(start + args.batch, n) + idx = all_idx[start:end] + (tok, lens) = store.read_packets(idx, T=T) + lens = np.minimum(lens, T).astype(np.int32) + spec[start:end] = compute_spectral_features(tok, lens, n_bands=args.n_bands) + if start // args.batch % 20 == 0 or end == n: + dt = time.time() - t0 + rate = end / max(dt, 1e-06) + eta = (n - end) / max(rate, 1.0) + print(f'[stream] {end:,}/{n:,} dt={dt:.1f}s rate={rate:.0f} flows/s ETA={eta:.0f}s', flush=True) + return (spec, flow_id, None) + +def main() -> None: + p = argparse.ArgumentParser(description=__doc__) + p.add_argument('--packets-npz', type=Path, default=None, help='Monolithic packets.npz path (mutually exclusive with --source-store).') + p.add_argument('--source-store', type=Path, default=None, help='PacketShardStore directory (mutually exclusive with --packets-npz).') + p.add_argument('--flows-parquet', type=Path, required=True, help='flows.parquet for flow_id + label.') + p.add_argument('--base-features', type=Path, required=True, help='Existing canonical flow_features.parquet (20-d).') + p.add_argument('--out', type=Path, required=True) + p.add_argument('--n-bands', type=int, default=8) + p.add_argument('--T', type=int, default=None, help='Truncate to first T packets before FFT (default: stored T).') + p.add_argument('--batch', type=int, default=100000) + args = p.parse_args() + if (args.packets_npz is None) == (args.source_store is None): + p.error('pass exactly one of --packets-npz or --source-store') + print(f'[read] {args.flows_parquet}') + flows = pd.read_parquet(args.flows_parquet, columns=['flow_id', 'label']) + n = len(flows) + flow_id_flows = flows['flow_id'].to_numpy(dtype=np.uint64) + labels = flows['label'].astype(str).to_numpy() + print(f'[read] {args.base_features}') + base = pd.read_parquet(args.base_features) + if len(base) != n: + raise ValueError(f'base features rows {len(base):,} != flows rows {n:,}') + if 'flow_id' in base.columns: + if not np.array_equal(base['flow_id'].to_numpy(dtype=np.uint64), flow_id_flows): + raise ValueError('base flow_id != flows flow_id (row alignment broken)') + if args.packets_npz is not None: + (spec, flow_id_pkt, _) = _from_npz(args) + else: + (spec, flow_id_pkt, _) = _from_store(args) + if flow_id_pkt is not None and (not np.array_equal(flow_id_pkt, flow_id_flows)): + raise ValueError('packet flow_id != flows flow_id') + out_df = pd.DataFrame({'flow_id': flow_id_flows, 'label': labels}) + for name in CANONICAL_FLOW_FEATURE_NAMES: + if name not in base.columns: + raise ValueError(f'base parquet missing canonical feature {name!r}') + out_df[name] = base[name].to_numpy(dtype=np.float32) + spec_cols = _spectral_column_names(args.n_bands) + for (i, name) in enumerate(spec_cols): + out_df[name] = spec[:, i] + args.out.parent.mkdir(parents=True, exist_ok=True) + out_df.to_parquet(args.out, compression='snappy', index=False) + sz_mb = args.out.stat().st_size / 1000000.0 + print(f'[write] {args.out} ({sz_mb:.2f} MB, {len(out_df):,} rows × {len(out_df.columns)} cols, +{4 * args.n_bands} spectral)') +if __name__ == '__main__': + main() diff --git a/scripts/iscxtor_companion.sh b/scripts/iscxtor_companion.sh new file mode 100755 index 0000000..f0d4810 --- /dev/null +++ b/scripts/iscxtor_companion.sh @@ -0,0 +1,132 @@ +#!/bin/bash +# Companion to scripts/repr_experiment.sh. +# +# Timeline: +# Phase I (parallel with main pipeline after S2b): +# extract ISCXTor2016 pcaps into unified artifacts. +# CPU-bound, coexists with GPU training of E0/E1/E2. +# Phase II (after main pipeline DONE): +# for each trained model (E0, E1, E2), run detect + per_class +# against ISCXTor2016 (benign=nontor, attack=tor), emitting +# `iscxtor_eval/` subdir per model. +# Phase III : unified summary across both transfer targets. +# +# Log layout (reused from main repr_experiment): +# $MAIN_DIR/companion.log — this script's orchestration log +# $MAIN_DIR/companion_iscxtor_extract.log +# $MAIN_DIR//iscxtor_eval/ — per-model ISCXTor2016 results + +set -uo pipefail + +ROOT=/home/chy/mambafortrafficmodeling +cd "$ROOT" + +MAIN_DIR="runs/repr_experiment_20260423_092147" +MAIN_LOG="$MAIN_DIR/orch.log" +COMP_LOG="$MAIN_DIR/companion.log" +mkdir -p "$MAIN_DIR" +exec > >(tee -a "$COMP_LOG") 2>&1 + +N_VAL=20000 +N_ATK=20000 # ISCXTor2016 has fewer attack flows than CICDDoS2019 +SPLIT_SEED=42 + +echo "========================================================================" +echo "= $(date): iscxtor_companion START =" +echo "= main dir: $MAIN_DIR =" +echo "========================================================================" + +wait_for_pattern() { + local pattern=$1 log=$2 desc=$3 + echo ">>> $(date): waiting for '$desc' (pattern='$pattern' in $log)" + local waited=0 + while ! grep -q "$pattern" "$log" 2>/dev/null; do + sleep 60 + waited=$((waited + 60)) + if (( waited % 600 == 0 )); then + echo " [heartbeat $(date +%H:%M:%S)] waited ${waited}s for $desc" + fi + done + echo "<<< $(date): '$desc' detected after ${waited}s wait" +} + +run_stage() { + local name=$1; shift + local log="$MAIN_DIR/${name}.log" + echo "" + echo ">>> $(date): [$name] START" + local t0=$(date +%s) + if ! "$@" > "$log" 2>&1; then + local t1=$(date +%s) + echo "!!! $(date): [$name] FAILED after $((t1-t0))s — see $log" + tail -30 "$log" + exit 1 + fi + local t1=$(date +%s) + echo "<<< $(date): [$name] OK in $((t1-t0))s (log: $log)" + tail -6 "$log" | sed 's/^/ | /' +} + +# ===================================================================== +# Phase I — ISCXTor2016 extraction (after S2b, parallel with main training) +# ===================================================================== + +wait_for_pattern "s2b_extract_cicddos2019_01-12.*OK" "$MAIN_LOG" \ + "S2b CICDDoS2019 01-12 extraction to complete" + +run_stage "companion_iscxtor_extract" \ + nice -n 10 ionice -c 3 uv run python scripts/extract_iscxtor2016.py \ + --skip-decompress --jobs 6 + +# ===================================================================== +# Phase II — wait for main pipeline DONE, then detect + per_class +# ===================================================================== + +wait_for_pattern "repr_experiment DONE" "$MAIN_LOG" \ + "main repr_experiment to finish (S7 summary)" + +detect_and_per_class_iscxtor() { + local tag=$1 + local src="$MAIN_DIR/$tag" + local dst="$MAIN_DIR/$tag/iscxtor_eval" + + if [ ! -f "$src/model.pt" ]; then + echo "!!! $(date): [$tag] model.pt not found at $src — skipping" + return 1 + fi + + mkdir -p "$dst" + # Symlink the trained model into the eval subdir — detect.py reads model.pt + # from --save-dir. This keeps the original $tag/ directory pristine + # (CICDDoS2019 artifacts stay where they were). + ln -sf "../model.pt" "$dst/model.pt" + + run_stage "${tag}_detect_iscxtor" \ + uv run python -m detect \ + --save-dir "$dst" \ + --packets-npz datasets/iscxtor2016/processed/packets.npz \ + --flows-parquet datasets/iscxtor2016/processed/flows.parquet \ + --benign-label nontor \ + --per-class-column activity \ + --n-val "$N_VAL" --n-atk "$N_ATK" --seed "$SPLIT_SEED" + + run_stage "${tag}_per_class_iscxtor" \ + uv run python -m eval.per_class --save-dir "$dst" +} + +detect_and_per_class_iscxtor "e0_baseline" +detect_and_per_class_iscxtor "e1_relv2" +detect_and_per_class_iscxtor "e2_relv2_ctx" + +# ===================================================================== +# Phase III — unified summary across both transfer targets +# ===================================================================== + +run_stage "companion_summary" \ + uv run python scripts/summarize_repr_exp.py --root "$MAIN_DIR" --with-iscxtor + +echo "" +echo "========================================================================" +echo "= $(date): iscxtor_companion DONE =" +echo "= results: $MAIN_DIR/{summary.txt, summary.json} =" +echo "========================================================================" diff --git a/scripts/merge_cicddos_shards.py b/scripts/merge_cicddos_shards.py new file mode 100644 index 0000000..e1e8dd4 --- /dev/null +++ b/scripts/merge_cicddos_shards.py @@ -0,0 +1,54 @@ +from __future__ import annotations +import argparse +from pathlib import Path +import numpy as np +import pandas as pd +DEFAULT_DIR = Path('datasets/cicddos2019/processed') + +def _load_shard(dir: Path, shard: str) -> tuple[dict, pd.DataFrame]: + p = np.load(dir / f'packets.{shard}.npz') + f = pd.read_parquet(dir / f'flows.{shard}.parquet') + assert set(p.files) == {'packet_tokens', 'packet_lengths', 'flow_id'}, p.files + assert set(f.columns) == {'flow_id', 'label'}, f.columns + assert len(p['flow_id']) == len(f), f'row count mismatch in {shard}' + assert np.array_equal(p['flow_id'], f['flow_id'].to_numpy()), f'flow_id mismatch in {shard}' + return ({'packet_tokens': p['packet_tokens'], 'packet_lengths': p['packet_lengths'], 'flow_id': p['flow_id']}, f) + +def main() -> None: + ap = argparse.ArgumentParser(description=__doc__) + ap.add_argument('--dir', type=Path, default=DEFAULT_DIR) + ap.add_argument('--out-packets', type=Path, default=None) + ap.add_argument('--out-flows', type=Path, default=None) + args = ap.parse_args() + out_p = args.out_packets or args.dir / 'packets.npz' + out_f = args.out_flows or args.dir / 'flows.parquet' + print(f'=== merging shards from {args.dir} ===') + (p1, f1) = _load_shard(args.dir, '01-12') + (p3, f3) = _load_shard(args.dir, '03-11') + n1 = len(f1) + n3 = len(f3) + N = n1 + n3 + print(f'01-12 rows: {n1:,} 03-11 rows: {n3:,} total: {N:,}') + tokens = np.concatenate([p1['packet_tokens'], p3['packet_tokens']], axis=0) + lengths = np.concatenate([p1['packet_lengths'], p3['packet_lengths']], axis=0) + flow_id = np.arange(N, dtype=np.uint64) + print(f' tokens shape={tokens.shape} dtype={tokens.dtype}') + print(f' lengths shape={lengths.shape} dtype={lengths.dtype}') + flows = pd.concat([f1.drop(columns=['flow_id']), f3.drop(columns=['flow_id'])], ignore_index=True) + flows.insert(0, 'flow_id', flow_id) + print(f" flows rows={len(flows):,} label unique={flows['label'].nunique()}") + assert len(tokens) == N == len(flows) + assert np.array_equal(flow_id, flows['flow_id'].to_numpy()) + print(f'\n=== writing {out_p} ===') + out_p.parent.mkdir(parents=True, exist_ok=True) + np.savez_compressed(out_p, packet_tokens=tokens, packet_lengths=lengths, flow_id=flow_id) + sz = out_p.stat().st_size / 1000000000.0 + print(f' wrote {sz:.2f} GB') + print(f'\n=== writing {out_f} ===') + flows.to_parquet(out_f, compression='snappy', index=False) + sz = out_f.stat().st_size / 1000000.0 + print(f' wrote {sz:.2f} MB') + print(f'\n=== summary ===') + print(flows['label'].value_counts().to_string()) +if __name__ == '__main__': + main() diff --git a/scripts/merge_shard_artifacts.py b/scripts/merge_shard_artifacts.py new file mode 100644 index 0000000..57c9ab3 --- /dev/null +++ b/scripts/merge_shard_artifacts.py @@ -0,0 +1,70 @@ +from __future__ import annotations +import argparse +from pathlib import Path +import numpy as np +import pandas as pd +LABEL_ALIASES = {'UDP-lag': 'UDPLag'} + +def _infer_flows_path(packets_path: Path) -> Path: + name = packets_path.name + if name.startswith('packets.'): + flows_name = 'flows.' + name[len('packets.'):].removesuffix('.npz') + '.parquet' + else: + raise ValueError(f'Cannot infer flows path from {packets_path}') + return packets_path.parent / flows_name + +def main(): + ap = argparse.ArgumentParser(description=__doc__) + ap.add_argument('--in', dest='inputs', action='append', type=Path, required=True, help='packets..npz path. Pass multiple times (one per shard). flows..parquet is inferred.') + ap.add_argument('--out-packets', type=Path, required=True) + ap.add_argument('--out-flows', type=Path, required=True) + args = ap.parse_args() + tok_chunks: list[np.ndarray] = [] + len_chunks: list[np.ndarray] = [] + flow_dfs: list[pd.DataFrame] = [] + META_COLS = ['flow_id', 'label', 'start_ts', 'src_ip', 'dst_ip', 'src_port', 'dst_port', 'protocol', 'n_pkts'] + for pkt_path in args.inputs: + flow_path = _infer_flows_path(pkt_path) + if not pkt_path.exists(): + raise FileNotFoundError(pkt_path) + if not flow_path.exists(): + raise FileNotFoundError(flow_path) + p = np.load(pkt_path) + available = set(pd.read_parquet(flow_path).columns) + cols = [c for c in META_COLS if c in available] + f = pd.read_parquet(flow_path, columns=cols) + if len(p['flow_id']) != len(f): + raise ValueError(f'{pkt_path.name}: row count mismatch with {flow_path.name}') + if not np.array_equal(p['flow_id'], f['flow_id'].to_numpy()): + raise ValueError(f'{pkt_path.name}: flow_id mismatch with {flow_path.name}') + tok_chunks.append(np.asarray(p['packet_tokens'])) + len_chunks.append(np.asarray(p['packet_lengths'])) + flow_dfs.append(f) + print(f"[load] {pkt_path.name} : {len(p['flow_id']):>10,} rows cols={cols}") + T_full_set = {t.shape[1] for t in tok_chunks} + D_set = {t.shape[2] for t in tok_chunks} + if len(T_full_set) != 1 or len(D_set) != 1: + raise ValueError(f'inconsistent T/D across shards: T={T_full_set} D={D_set}') + tokens = np.concatenate(tok_chunks, axis=0) + lengths = np.concatenate(len_chunks, axis=0) + flow_df = pd.concat(flow_dfs, ignore_index=True) + del tok_chunks, len_chunks, flow_dfs + if LABEL_ALIASES: + flow_df['label'] = flow_df['label'].map(lambda s: LABEL_ALIASES.get(s, s)).astype(str) + N = len(tokens) + flow_id = np.arange(N, dtype=np.uint64) + flow_df['flow_id'] = flow_id + labels = flow_df['label'].to_numpy().astype(str) + print(f'\n[merge] total rows: {N:,}') + print(f'[merge] label distribution:') + (ulabels, counts) = np.unique(labels, return_counts=True) + for (lbl, cnt) in sorted(zip(ulabels, counts), key=lambda x: -x[1]): + print(f' {lbl:<40s} {cnt:>10,}') + args.out_packets.parent.mkdir(parents=True, exist_ok=True) + np.savez(args.out_packets, packet_tokens=tokens, packet_lengths=lengths, flow_id=flow_id) + print(f'\n[merge] wrote {args.out_packets} ({args.out_packets.stat().st_size / 1000000000.0:.2f} GB)') + args.out_flows.parent.mkdir(parents=True, exist_ok=True) + flow_df.to_parquet(args.out_flows, compression='snappy', index=False) + print(f'[merge] wrote {args.out_flows} ({args.out_flows.stat().st_size / 1000000.0:.2f} MB) cols={list(flow_df.columns)}') +if __name__ == '__main__': + main() diff --git a/scripts/repr_experiment.sh b/scripts/repr_experiment.sh new file mode 100755 index 0000000..31b4d8e --- /dev/null +++ b/scripts/repr_experiment.sh @@ -0,0 +1,124 @@ +#!/bin/bash +# End-to-end representation experiment: re-extract CICIDS2017 + CICDDoS2019 +# with metadata columns, then train E0/E1/E2 with fixed 10k benign and +# evaluate on CICDDoS2019. +# +# Stages (each with wall-clock logging + per-stage log file): +# S1 re-extract CICIDS2017 → datasets/cicids2017/processed/* +# S2a re-extract CICDDoS2019 03-11 shard +# S2b re-extract CICDDoS2019 01-12 shard +# S2c merge CICDDoS2019 shards +# S3 train E0 (mixed_dequant, no ctx) [configs/n10k_baseline.yaml] +# S4 train E1 (relative_v2, no ctx) [configs/n10k_relv2.yaml] +# S5 train E2 (relative_v2, with 8-d ctx) [configs/n10k_relv2_ctx.yaml] +# S6 detect+per_class for each on CICDDoS2019 +# S7 summary table +# +# Any stage's failure aborts the rest and leaves the partial log intact. +set -uo pipefail + +ROOT=/home/chy/mambafortrafficmodeling +cd "$ROOT" + +STAMP=$(date +%Y%m%d_%H%M%S) +OUT_DIR="runs/repr_experiment_${STAMP}" +mkdir -p "$OUT_DIR" +MAIN_LOG="$OUT_DIR/orch.log" +exec > >(tee -a "$MAIN_LOG") 2>&1 + +N_VAL=20000 +N_ATK=100000 +SPLIT_SEED=42 + +echo "========================================================================" +echo "= $(date): repr_experiment start =" +echo "= output root: $OUT_DIR =" +echo "========================================================================" + +run_stage() { + local name=$1; shift + local log="$OUT_DIR/${name}.log" + echo "" + echo ">>> $(date): [$name] START" + echo ">>> $(date): [$name] command: $*" + local t0=$(date +%s) + if ! "$@" > "$log" 2>&1; then + local t1=$(date +%s); echo "!!! $(date): [$name] FAILED after $((t1-t0))s — see $log" + tail -30 "$log" + exit 1 + fi + local t1=$(date +%s) + echo "<<< $(date): [$name] OK in $((t1-t0))s (log: $log)" + # Print tail of log so orch.log shows meaningful progress. + tail -10 "$log" | sed 's/^/ | /' +} + +# ==================================================================== +# S1 — re-extract CICIDS2017 +# ==================================================================== +run_stage "s1_extract_cicids2017" \ + uv run python scripts/extract_cicids2017.py --jobs 5 --time-offset 28800 + +# ==================================================================== +# S2 — re-extract CICDDoS2019 (per-shard) + merge +# ==================================================================== +run_stage "s2a_extract_cicddos2019_03-11" \ + uv run python scripts/extract_cicddos2019.py \ + --shards 03-11 --jobs 1 \ + --out-packets datasets/cicddos2019/processed/packets.03-11.npz \ + --out-flows datasets/cicddos2019/processed/flows.03-11.parquet + +run_stage "s2b_extract_cicddos2019_01-12" \ + uv run python scripts/extract_cicddos2019.py \ + --shards 01-12 --jobs 1 \ + --out-packets datasets/cicddos2019/processed/packets.01-12.npz \ + --out-flows datasets/cicddos2019/processed/flows.01-12.parquet + +run_stage "s2c_merge_cicddos2019" \ + uv run python scripts/merge_shard_artifacts.py \ + --in datasets/cicddos2019/processed/packets.03-11.npz \ + --in datasets/cicddos2019/processed/packets.01-12.npz \ + --out-packets datasets/cicddos2019/processed/packets.npz \ + --out-flows datasets/cicddos2019/processed/flows.parquet + +# ==================================================================== +# S3..S5 — train E0 / E1 / E2 with the same 10k benign +# ==================================================================== +train_and_eval() { + local tag=$1 cfg=$2 + local run_dir="$OUT_DIR/$tag" + mkdir -p "$run_dir" + + # Copy config and patch save_dir to our per-tag directory. + cp "$cfg" "$run_dir/config.yaml" + sed -i "s#^save_dir:.*#save_dir: $run_dir#" "$run_dir/config.yaml" + + run_stage "${tag}_train" \ + uv run python -m train --config "$run_dir/config.yaml" + + run_stage "${tag}_detect_ddos" \ + uv run python -m detect \ + --save-dir "$run_dir" \ + --packets-npz datasets/cicddos2019/processed/packets.npz \ + --flows-parquet datasets/cicddos2019/processed/flows.parquet \ + --n-val "$N_VAL" --n-atk "$N_ATK" --seed "$SPLIT_SEED" + + run_stage "${tag}_per_class" \ + uv run python -m eval.per_class --save-dir "$run_dir" +} + +train_and_eval "e0_baseline" "configs/n10k_baseline.yaml" +train_and_eval "e1_relv2" "configs/n10k_relv2.yaml" +train_and_eval "e2_relv2_ctx" "configs/n10k_relv2_ctx.yaml" + +# ==================================================================== +# S7 — summary table +# ==================================================================== +run_stage "s7_summary" \ + uv run python scripts/summarize_repr_exp.py --root "$OUT_DIR" + +echo "" +echo "========================================================================" +echo "= $(date): repr_experiment DONE =" +echo "= results under: $OUT_DIR =" +echo "========================================================================" diff --git a/scripts/summarize_repr_exp.py b/scripts/summarize_repr_exp.py new file mode 100644 index 0000000..9abd5bb --- /dev/null +++ b/scripts/summarize_repr_exp.py @@ -0,0 +1,93 @@ +from __future__ import annotations +import argparse +import json +from pathlib import Path +HARD_CLASSES = ('Syn', 'UDPLag', 'DrDoS_NTP') + +def _load_pc(run_dir: Path) -> dict | None: + p = run_dir / 'per_class.json' + if not p.exists(): + print(f'[warn] missing {p}') + return None + return json.loads(p.read_text())['terminal_norm'] + +def _render_block(title: str, data: list[dict], hard_classes: tuple[str, ...]) -> list[str]: + lines: list[str] = [] + lines.append('') + lines.append('=' * 96) + lines.append(f'# {title}') + lines.append('=' * 96) + lines.append(f"{'experiment':<40s} {'overall AUROC':>14s} {'macro AUROC':>14s} {'TPR@1%FPR':>12s} {'FPR@95%TPR':>12s}") + lines.append('-' * 96) + if not data: + lines.append('(no results)') + return lines + base = data[0]['pc'] + for d in data: + pc = d['pc'] + delta_overall = pc['overall_auroc'] - base['overall_auroc'] + delta_macro = pc['macro_auroc'] - base['macro_auroc'] + delta_tpr = pc['tpr_at_1fpr'] - base['tpr_at_1fpr'] + lines.append(f"{d['label']:<40s} {pc['overall_auroc']:>8.4f} ({delta_overall:+.4f}) {pc['macro_auroc']:>8.4f} ({delta_macro:+.4f}) {pc['tpr_at_1fpr']:>6.4f} ({delta_tpr:+.4f}) {pc['fpr_at_95tpr']:>12.4f}") + if hard_classes: + lines.append('') + lines.append(f"--- focus classes: {', '.join(hard_classes)} ---") + for c in hard_classes: + row = f'{c:<18s}' + for d in data: + pc = d['pc'] + match = next((r for r in pc['per_class'] if r['class'] == c), None) + if match is None: + row += f" {d['tag']}:n/a" + else: + row += f" {d['tag']}:{match['auroc']:.3f}(tpr={match['tpr_at_1fpr']:.3f})" + lines.append(row) + lines.append('') + lines.append('--- all classes (sorted by E0 AUROC ascending) ---') + base_pc = data[0]['pc']['per_class'] + ordered = sorted(base_pc, key=lambda r: r['auroc']) + hdr2 = f"{'class':<22s} {'N':>8s}" + ''.join((f" {d['tag']:>14s}" for d in data)) + lines.append(hdr2) + for row_b in ordered: + cls = row_b['class'] + row = f"{cls:<22s} {row_b['n']:>8d}" + for d in data: + pc = d['pc'] + match = next((r for r in pc['per_class'] if r['class'] == cls), None) + row += f" {match['auroc']:>14.4f}" if match else f" {'—':>14s}" + lines.append(row) + return lines + +def main(): + ap = argparse.ArgumentParser(description=__doc__) + ap.add_argument('--root', type=Path, required=True) + ap.add_argument('--with-iscxtor', action='store_true', help='Also load iscxtor_eval/per_class.json under each tag and render a second comparison block for the CICIDS2017 → ISCXTor2016 transfer target.') + args = ap.parse_args() + runs = [('E0 baseline (mixed_dequant)', 'e0_baseline'), ('E1 relative_v2 (channel rehab)', 'e1_relv2'), ('E2 relative_v2 + 8-d context', 'e2_relv2_ctx')] + ddos_data: list[dict] = [] + for (label, tag) in runs: + pc = _load_pc(args.root / tag) + if pc is not None: + ddos_data.append({'label': label, 'tag': tag, 'pc': pc}) + iscx_data: list[dict] = [] + if args.with_iscxtor: + for (label, tag) in runs: + pc = _load_pc(args.root / tag / 'iscxtor_eval') + if pc is not None: + iscx_data.append({'label': label, 'tag': tag, 'pc': pc}) + if not ddos_data and (not iscx_data): + print('[err] no results found under', args.root) + return + lines: list[str] = [] + if ddos_data: + lines.extend(_render_block('CICIDS2017 → CICDDoS2019 (target=DDoS attacks; benign=normal)', ddos_data, HARD_CLASSES)) + if iscx_data: + lines.extend(_render_block('CICIDS2017 → ISCXTor2016 (target=Tor flows; benign=nontor)', iscx_data, ())) + txt = '\n'.join(lines) + print(txt) + (args.root / 'summary.txt').write_text(txt + '\n') + combined = {'cicddos2019': ddos_data, 'iscxtor2016': iscx_data} + (args.root / 'summary.json').write_text(json.dumps(combined, indent=2)) + print(f"\n[saved] {args.root / 'summary.txt'}") +if __name__ == '__main__': + main() diff --git a/tests/common/test_data_contract.py b/tests/common/test_data_contract.py new file mode 100644 index 0000000..01462eb --- /dev/null +++ b/tests/common/test_data_contract.py @@ -0,0 +1,173 @@ +from __future__ import annotations +import sys +from pathlib import Path +import numpy as np +import pytest +sys.path.insert(0, str(Path(__file__).resolve().parents[2])) +from common.data_contract import BENIGN_TOKEN, CANONICAL_FLOW_FEATURE_NAMES, FLOW_COUNT_FEATURE_NAMES, FLOW_COUNT_IDX, FLOW_CONTINUOUS_IDX, FLOW_D, IDLE_THRESHOLD_MS, PACKET_BINARY_CHANNEL_IDX, PACKET_CONTINUOUS_CHANNEL_IDX, PACKET_D, PACKET_FEATURE_NAMES, UNKNOWN_LABEL_TOKEN, apply_mixed_dequant, canonical_5tuple, compute_flow_features_from_packets, fit_packet_stats, normalize_label, zscore + +def test_packet_schema_invariants(): + assert PACKET_D == 9 + assert len(PACKET_FEATURE_NAMES) == PACKET_D + assert len(set(PACKET_FEATURE_NAMES)) == PACKET_D + cont = set(PACKET_CONTINUOUS_CHANNEL_IDX) + binary = set(PACKET_BINARY_CHANNEL_IDX) + assert cont.isdisjoint(binary) + assert cont | binary == set(range(PACKET_D)) + +def test_flow_schema_invariants(): + assert FLOW_D == 20 + assert len(CANONICAL_FLOW_FEATURE_NAMES) == FLOW_D + assert len(set(CANONICAL_FLOW_FEATURE_NAMES)) == FLOW_D + assert set(FLOW_COUNT_IDX).isdisjoint(FLOW_CONTINUOUS_IDX) + assert set(FLOW_COUNT_IDX) | set(FLOW_CONTINUOUS_IDX) == set(range(FLOW_D)) + for name in FLOW_COUNT_FEATURE_NAMES: + assert name in CANONICAL_FLOW_FEATURE_NAMES + +@pytest.mark.parametrize('raw,expected', [('BENIGN', BENIGN_TOKEN), ('Benign', BENIGN_TOKEN), ('benign', BENIGN_TOKEN), ('normal', BENIGN_TOKEN), (' BENIGN ', BENIGN_TOKEN), ('DrDoS_DNS', 'DrDoS_DNS'), ('', UNKNOWN_LABEL_TOKEN), (' ', UNKNOWN_LABEL_TOKEN)]) +def test_normalize_label(raw, expected): + assert normalize_label(raw) == expected + +def test_canonical_5tuple_direction_agnostic(): + a = canonical_5tuple('10.0.0.1', 1234, '10.0.0.2', 80, 6) + b = canonical_5tuple('10.0.0.2', 80, '10.0.0.1', 1234, 6) + assert a == b + +def test_canonical_5tuple_distinct_flows(): + a = canonical_5tuple('10.0.0.1', 1234, '10.0.0.2', 80, 6) + c = canonical_5tuple('10.0.0.1', 1234, '10.0.0.3', 80, 6) + assert a != c + +def test_canonical_5tuple_string_inputs(): + a = canonical_5tuple('10.0.0.1', '1234', '10.0.0.2', '80', '6') + b = canonical_5tuple('10.0.0.2', 80, '10.0.0.1', 1234, 6) + assert a == b + +def _make_packets(n_flows: int, T: int, n_real: int, seed: int=0) -> tuple[np.ndarray, np.ndarray]: + rng = np.random.default_rng(seed) + tokens = np.zeros((n_flows, T, PACKET_D), dtype=np.float32) + tokens[:, :n_real, 0] = rng.uniform(0, 5, (n_flows, n_real)) + tokens[:, :n_real, 1] = rng.uniform(0, 3, (n_flows, n_real)) + tokens[:, :n_real, 2] = rng.integers(0, 2, (n_flows, n_real)) + tokens[:, :n_real, 3] = rng.integers(0, 2, (n_flows, n_real)) + tokens[:, :n_real, 7] = rng.integers(0, 2, (n_flows, n_real)) + tokens[:, :n_real, 8] = rng.uniform(0, 4, (n_flows, n_real)) + lens = np.full(n_flows, n_real, dtype=np.int32) + return (tokens, lens) + +def test_fit_packet_stats_ignores_padding(): + (tokens, lens) = _make_packets(8, 16, 4, seed=1) + tokens[:, 4:, :] = 999.0 + (mean, std) = fit_packet_stats(tokens, lens) + assert np.all(np.abs(mean) < 50) + assert np.all(std < 50) + +def test_zscore_basic(): + x = np.array([[1.0, 2.0], [3.0, 4.0]], dtype=np.float32) + mean = np.array([2.0, 3.0], dtype=np.float32) + std = np.array([1.0, 1.0], dtype=np.float32) + z = zscore(x, mean, std) + assert np.allclose(z, [[-1.0, -1.0], [1.0, 1.0]]) + +def test_zscore_handles_zero_std(): + x = np.ones((3, 2), dtype=np.float32) + mean = np.ones(2, dtype=np.float32) + std = np.zeros(2, dtype=np.float32) + z = zscore(x, mean, std) + assert np.all(np.isfinite(z)) + +def test_apply_mixed_dequant_zeros_padding(): + (tokens, lens) = _make_packets(4, 8, 3, seed=2) + (mean, std) = fit_packet_stats(tokens, lens) + z = apply_mixed_dequant(tokens, lens, mean, std, split_tag='train', seed=0) + assert np.all(z[:, 3:, :] == 0.0) + +def test_apply_mixed_dequant_stable_under_same_seed(): + (tokens, lens) = _make_packets(4, 8, 3, seed=3) + (mean, std) = fit_packet_stats(tokens, lens) + z1 = apply_mixed_dequant(tokens, lens, mean, std, split_tag='train', seed=42) + z2 = apply_mixed_dequant(tokens, lens, mean, std, split_tag='train', seed=42) + assert np.allclose(z1, z2) + +def test_apply_mixed_dequant_split_noise_differs(): + (tokens, lens) = _make_packets(4, 8, 3, seed=4) + (mean, std) = fit_packet_stats(tokens, lens) + z_train = apply_mixed_dequant(tokens, lens, mean, std, split_tag='train', seed=42) + z_val = apply_mixed_dequant(tokens, lens, mean, std, split_tag='val', seed=42) + b_idx = list(PACKET_BINARY_CHANNEL_IDX) + assert not np.allclose(z_train[..., b_idx], z_val[..., b_idx]) + +def test_flow_features_shape(): + (tokens, lens) = _make_packets(5, 16, 8, seed=5) + feats = compute_flow_features_from_packets(tokens, lens) + assert feats.shape == (5, FLOW_D) + assert feats.dtype == np.float32 + +def test_flow_features_rejects_wrong_shape(): + tokens_bad = np.zeros((3, 4, 7), dtype=np.float32) + lens = np.full(3, 2, dtype=np.int32) + with pytest.raises(ValueError): + compute_flow_features_from_packets(tokens_bad, lens) + +def test_flow_features_zero_for_empty_flow(): + tokens = np.zeros((2, 4, PACKET_D), dtype=np.float32) + lens = np.array([0, 3], dtype=np.int32) + tokens[1, :3, 0] = 2.0 + feats = compute_flow_features_from_packets(tokens, lens) + assert np.all(feats[0] == 0.0) + assert np.any(feats[1] != 0.0) + +def test_flow_features_ack_syn_counts(): + tokens = np.zeros((1, 5, PACKET_D), dtype=np.float32) + tokens[0, :3, 7] = 1.0 + tokens[0, 0, 3] = 1.0 + tokens[0, 3:, 7] = 1.0 + tokens[0, 3:, 3] = 1.0 + lens = np.array([3], dtype=np.int32) + feats = compute_flow_features_from_packets(tokens, lens) + ack_idx = CANONICAL_FLOW_FEATURE_NAMES.index('ack_cnt') + syn_idx = CANONICAL_FLOW_FEATURE_NAMES.index('syn_cnt') + assert feats[0, ack_idx] == pytest.approx(3.0) + assert feats[0, syn_idx] == pytest.approx(1.0) + +def test_flow_features_fwd_bwd_counts(): + tokens = np.zeros((1, 6, PACKET_D), dtype=np.float32) + tokens[0, :4, 2] = np.array([0, 1, 0, 1]) + lens = np.array([4], dtype=np.int32) + feats = compute_flow_features_from_packets(tokens, lens) + fwd_idx = CANONICAL_FLOW_FEATURE_NAMES.index('fwd_count') + bwd_idx = CANONICAL_FLOW_FEATURE_NAMES.index('bwd_count') + assert feats[0, fwd_idx] == 2.0 + assert feats[0, bwd_idx] == 2.0 + +def test_flow_features_active_vs_idle(): + tokens = np.zeros((1, 4, PACKET_D), dtype=np.float32) + lens = np.array([4], dtype=np.int32) + tokens[0, 0, 1] = 0.0 + tokens[0, 1, 1] = np.log1p(100.0) + tokens[0, 2, 1] = np.log1p(5000.0) + tokens[0, 3, 1] = np.log1p(200.0) + feats = compute_flow_features_from_packets(tokens, lens, idle_threshold_ms=1000.0) + active_mean_idx = CANONICAL_FLOW_FEATURE_NAMES.index('active_mean') + idle_mean_idx = CANONICAL_FLOW_FEATURE_NAMES.index('idle_mean') + assert feats[0, active_mean_idx] == pytest.approx(np.log1p(150.0), rel=0.0001) + assert feats[0, idle_mean_idx] == pytest.approx(np.log1p(5000.0), rel=0.0001) + +def test_flow_features_padding_does_not_leak(): + T = 12 + (tokens_clean, lens) = _make_packets(3, T, 5, seed=6) + feats_clean = compute_flow_features_from_packets(tokens_clean, lens) + tokens_poisoned = tokens_clean.copy() + tokens_poisoned[:, 5:, :] = 9999.0 + feats_poisoned = compute_flow_features_from_packets(tokens_poisoned, lens) + assert np.allclose(feats_clean, feats_poisoned, atol=0.0001) + +def test_flow_features_single_packet_graceful(): + tokens = np.zeros((1, 4, PACKET_D), dtype=np.float32) + tokens[0, 0, 0] = 3.0 + tokens[0, 0, 2] = 0 + lens = np.array([1], dtype=np.int32) 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