JANUS/README.md

JANUS

JANUS (Joint Anomaly via Normalizing-flows of Unified States) — flow-matching unsupervised network anomaly detection over packet sequences.

JANUS is a packet-causal Transformer with two output heads on a shared backbone:

• Continuous Flow Matching head over the (size, IAT, win) packet channels.
• Discrete Flow Matching head over the 6 binary protocol-flag / direction channels.

Trained jointly on benign traffic only (no attack labels at any stage). The deployable scalar score is a Mahalanobis-OAS distance over a 10-d per-flow score vector emitted by the trained model, with the aggregator fit on benign val only — entirely unsupervised end-to-end.

JANUS is the first NIDS method to use Flow Matching as the training paradigm in mixed continuous–discrete state spaces over packet sequences.

Headline results

3-seed mean ± std AUROC. Selection-bias-free Mahalanobis-OAS aggregator on the 10-d JANUS score vector, fit on benign val only.

Within-dataset comparison (AUROC %, mean ± std)

Method	Venue	CIC-IDS2017	CIC-DDoS2019	CIC-IoT2023	ISCXTor2016
Isolation Forest	classical	55.27 ± 0.4 †	—	—	—
OCSVM	classical	59.59 ± 0.6 †	—	—	—
AnoFormer	ICLR'22	63.37 ± 0.7 †	—	—	—
GANomaly	BMVC'18	82.75 ± 5.6 †	—	—	—
RD4AD	CVPR'22	83.78 ± 0.8 †	—	—	—
TSLANet	ICML'24	84.45 ± 1.7 †	—	—	—
ARCADE	—	84.85 ± 2.0 †	—	—	—
MFAD	—	86.02 ± 0.8 †	—	—	—
STFPM	BMVC'21	86.29 ± 1.7 †	—	—	—
MMR	—	89.26 ± 1.2 †	—	—	—
Shafir NF + Shapley	arXiv'26	93.03 ‡	93.00 ‡	99.51 §‡	87.31 ‡
ConMD	TIFS'26	94.43 ± 0.1 †	—	—	—
JANUS (ours)	—	98.26 ± 0.35	99.18 ± 0.05	95.90 ± 0.22	99.09 ± 0.13

† Numbers from ConMD (TIFS'26) Table I; protocol = train 10 K benign / test 5 K + 5 K balanced; 5-seed mean ± std. ‡ Numbers from Shafir et al. (arXiv'26); protocol = train 10 K benign / SHAP-selected feature subsets per dataset. § Metric mismatch on CIC-IoT2023: Shafir reports F1 = 99.51 (Youden's-J threshold tuned with attack labels), we report AUROC = 95.90 (threshold-free); not directly comparable. Thresholded F1 for JANUS is reported in RESULTS.md Section D and artifacts/route_comparison/THRESHOLDED.md.

JANUS sets new SOTA on 3/3 directly comparable benchmarks (CIC-IDS2017 +3.83, CIC-DDoS2019 +6.18, ISCXTor2016 +11.78) — all margins outside seed std. JANUS is fully unsupervised (benign-only training, no attack labels at any stage), and uses the Mahalanobis-OAS aggregator over its 10-d raw score vector with parameters fit on benign val only.

3×3 cross-dataset transfer matrix

Source (rows) trained on 10K benign of source dataset; target (columns) tested on full target benign + all target attacks. Aggregator fit on target benign val only — no attack labels at any stage. Diagonal italic = within-dataset.

Source ↓ / Target →	CICIDS17	CICDDoS19	CICIoT23
CICIDS17	0.9826 ± 0.0035	0.9690 ± 0.0047	0.8698 ± 0.0031
CICDDoS19	0.9413 ± 0.0212	0.9918 ± 0.0005	0.8767 ± 0.0068
CICIoT23	0.9394 ± 0.0063	0.9030 ± 0.0075	0.9590 ± 0.0022

Forward CICIDS17→CICDDoS19 (0.969) beats Shafir 0.89 by +0.08; reverse CICDDoS19→CICIDS17 (0.941) approximately matches Shafir 0.93. CICIoT23 is hardest both as source and target — its IoT-protocol diversity makes the "benign of source ≈ benign of target" assumption brittle. Full table at artifacts/route_comparison/CROSS_MATRIX_3x3.md.

Layout

common/                    Data contract — single source of truth for the
                           9-d packet schema, 20-d packet-derived flow schema,
                           label normalization, and packet preprocessing.
Mixed_CFM/                 The JANUS model. Mixed continuous–discrete CFM
                           with two output heads on a shared causal Transformer.
                             configs/   Per-(dataset × seed) training configs.
                             model.py   MixedTokenCFM + MixedVelocity.
                             train.py / eval_phase1.py / eval_cross.py
Unified_CFM/               Legacy unified token CFM. Mixed_CFM imports its
                           AdaLNBlock + sinusoidal time embedding for backbone
                           reuse. Kept as internal ablation reference.
scripts/                   Workspace-level pcap → artifact pipeline,
                           CSV adapters, cross-package eval tooling.
  download/                UNB/CIC dataset downloaders.
  baselines/               Third-party baseline runners (Kitsune, Shafir-NF,
                           Anomaly-Transformer).
  aggregate/               Mahalanobis-OAS score-router + cross-matrix
                           orchestration. aggregate_score_router.py is the
                           deployable score path; run_cross_3x3.sh +
                           cross_3x3_table.py produce the cross matrix.
tests/                     Data-contract unit tests.

The following directories are gitignored (live on the dev box, not in the repo):

artifacts/                 All run outputs (checkpoints, eval JSONs, score
                           npzs, figures). Per-(dataset × seed) model dirs at
                           artifacts/route_comparison/janus_<ds>_seed<N>/.
datasets/                  Raw + processed datasets (~1 TB).
baselines/                 Third-party baseline forks (Kitsune-py,
                           Anomaly-Transformer, ConMD, ganomaly, TIPSO-GAN, ...).
paper/                     Paper sources & external PDFs (Shafir 2026, Lipman
                           2210.02747, etc.).
.venv/                     uv-managed Python 3.14 virtual env.

Data contract

Every processed dataset under datasets/<name>/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/ — sharded PacketShardStore — 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

The 9-d packet schema and 20-d flow schema are FIXED in common/data_contract.py. Flow features are computed by compute_flow_features_from_packets(packet_tokens, lens) so row alignment is guaranteed.

Quick start

# Train JANUS on CICIDS2017 (3 seeds available: 42, 43, 44)
cd Mixed_CFM
uv run --no-sync python train.py --config configs/cicids2017_seed42.yaml

# Phase-1 evaluation: per-attack-class AUROC + 10-d score export
uv run --no-sync python eval_phase1.py \
    --model-dir <model_dir> --out-dir <eval_dir>

# Single cross-dataset eval
uv run --no-sync python eval_cross.py \
    --model-dir <src_model_dir> \
    --target-store datasets/<tgt>/processed/full_store \
    --target-flows datasets/<tgt>/processed/flows.parquet \
    --target-flow-features datasets/<tgt>/processed/flow_features.parquet \
    --benign-label normal --n-benign 10000 --n-attack 1000000 \
    --out <result.json>

# 3×3 cross matrix (6 off-diagonal directions × 3 seeds, 2-GPU parallel)
bash ../scripts/aggregate/run_cross_3x3.sh
uv run --no-sync python ../scripts/aggregate/cross_3x3_table.py

JANUS hyper-parameters (locked in Mixed_CFM/configs/<dataset>_seed*.yaml):

T: 64                  # max packet sequence length
d_model: 128
n_layers: 4
n_heads: 4
sigma: 0.1             # within-dataset; cross uses 0.6
lambda_disc: 1.0
use_ot: true           # OT-CFM (Sinkhorn coupling on benign batch)
reference_mode: causal_packets    # Route A: packet-causal attention

Producing the deployable scalar score

eval_phase1.py exports a 10-d per-flow score vector to phase1_scores.npz:

3 continuous-side scores  : terminal_norm, terminal_flow, terminal_packet
7 discrete-side scores    : disc_nll_total + disc_nll_ch{2,3,4,5,6,7}
                            (direction + 5 TCP flags)

The deployable scalar is the Mahalanobis-OAS distance:

d²(s) = (s − μ)ᵀ Σ⁻¹ (s − μ),    where (μ, Σ) come from sklearn.covariance.OAS
                                 fit on benign val ONLY (no attack labels).

Reference implementation: scripts/aggregate/aggregate_score_router.py. It reads artifacts/route_comparison/janus_<ds>_seed*/phase1_scores.npz and artifacts/route_comparison/cross/janus_seed*_<src>_to_<tgt>.npz, then writes artifacts/route_comparison/SCORE_ROUTER.md (within-dataset rows) and artifacts/route_comparison/CROSS_MATRIX_3x3.md (cross matrix, via cross_3x3_table.py).

Tests

uv run --no-sync python -m pytest tests/ Mixed_CFM/tests/ Unified_CFM/tests/

Adding a new dataset

Write one driver at scripts/extract_<name>.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. The extract pipeline writes all three artifacts (packets.npz, flows.parquet, flow_features.parquet) row-aligned by flow_id = arange(N).

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.

Authoritative documents

• RESULTS.md — full headline tables, ablations, per-attack analysis, JANUS configuration, thresholded operating-point metrics, what the experiments proved / disproved.
• Mixed_CFM/model.py and common/data_contract.py — model + data-contract source of truth.

Python environment

• requires-python = ">=3.14"; PyTorch pinned to the pytorch-cu128 index, plus mamba-ssm, causal-conv1d, scapy, dpkt, pyarrow, sklearn (for the OAS aggregator).
• Two pyproject.toml files exist: root and Mixed_CFM/; they are not declared as a uv workspace and resolve independently. Run uv run ... from whichever directory owns the entry point.
• Unified_CFM/ has no pyproject.toml; it uses the root venv (uv run --no-sync python <script.py>).
• Scripts under scripts/download/ are pure stdlib — invoke with python3.