JANUS/Unified_CFM

Unified_CFM

A single multi-scale OT-CFM over one token sequence per flow:

[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:

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:

# 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

# 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:

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:

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.