TrafficMoE achieves 97.65% accuracy and F1-score on the ISCX-Tor2016 dataset, substantially outperforming all baselines including the best pretraining-based competitor FlowletFormer (91.16% F1), by separately modeling protocol headers and encrypted payloads via dual-branch sparse Mixture-of-Experts rather than treating them as a unified byte stream.

Routing-guided conditional aggregation (CA) that dynamically weights header versus payload contributions using per-sample MoE routing probabilities outperforms static fusion on all six datasets, demonstrating that the relative discriminative utility of headers versus payloads varies by application type — and that classifiers can adaptively shift reliance to whichever modality is less obfuscated.

§III-E, §IV-B detection

Explicitly disentangling packet headers (structured, low-entropy) from encrypted payloads (high-entropy, stochastic) into separate MoE branches yields consistent gains across six datasets: 86.85% F1 on 120-class TLS 1.3 traffic (CSTNET-TLS), 97.88% F1 on USTC-TFC2016 malware/benign flows, and 92.65% F1 on imbalanced IoT traffic (CIC-IoT2022), demonstrating that headers and payloads carry fundamentally different and independently exploitable discriminative signals.

§III-A, §IV-B, Tables II–III detection

Pretraining on 30 GB of unlabeled mixed traffic via masked language modeling (ISCX-VPN2016 NonVPN, CICIDS2017, WIDE backbone), then fine-tuning, enables TrafficMoE to classify VPN application traffic at 88.72% F1 and VPN service traffic at 92.61% F1, exceeding all fully supervised and prior pretraining baselines without requiring labeled training data for those domains.

§IV-A, §IV-B, Table III detection

An uncertainty-aware filtering (UF) mechanism quantifies per-token reliability via Shannon entropy of the cross-modal header–payload attention matrix, finding that encrypted payloads still contain low-entropy tokens with stable cross-modal alignment that serve as reliable classification anchors — demonstrating that nominally randomized byte streams retain exploitable low-entropy structure.

§III-D detection

NATA (Non-invasive Active Traffic-correlation Analysis) injects low-frequency bandwidth waveforms (sinusoidal, square-wave, triangular) into Tor TCP connections at an upstream gateway without endpoint compromise, payload decryption, or Tor-browser modification. BM-Net, a selective state-space classifier trained on the exit-side observations, achieves a 99.65% binary detection F1 score distinguishing watermarked from natural traffic on a 20,000-trace real-world dataset.

2026-fan-activeflowmark-assessing-tor §IV, §VI.D, Table III flow-correlationtraffic-shapeml-classifier

BM-Net achieves a 99.65% binary detection F1 score for distinguishing bandwidth-watermarked Tor flows from natural traffic, outperforming all evaluated baselines (next best: TikTok at 75.96% F1). The accuracy gap stems from active perturbation imposing a deterministic low-frequency throughput constraint rather than relying on subtle natural metadata, making the detection task fundamentally easier than passive website fingerprinting.

2026-fan-activeflowmark-assessing-tor §VI-D, Table III–IV flow-correlationtraffic-shapeml-classifier

BM-Net achieves a 99.65% binary detection F1 score distinguishing watermarked from natural Tor flows, and a 97.5% macro-F1 score for fine-grained modulation classification across sinusoidal, square-wave, and triangular patterns. The fine-grained test set contains 201 held-out samples collected from ten clients across five geographic regions (Europe, North America, Australia, Southeast Asia, East Asia), with training traces including traffic collected under WTF-PAD and Walkie-Talkie defenses.

2026-fan-activeflowmark-assessing-tor §VI-D, Table III, Table V flow-correlationml-classifiertraffic-shape

BM-Net achieves a 97.5% macro-F1 score for fine-grained classification of three modulation geometries (sinusoidal, square-wave, triangular) from noisy exit-side Tor observations using only 201 labeled test samples collected across cross-continental Tor paths. Residual errors concentrate between natural traffic and square-wave modulation, as abrupt low-rate transitions are partially smoothed by Tor multiplexing and network jitter.

2026-fan-activeflowmark-assessing-tor §VI-D, Table V flow-correlationml-classifiertraffic-shape

Fine-grained modulation classification (natural vs. sinusoidal vs. square-wave vs. triangular) achieves 97.5% macro-F1 on a 201-sample held-out test set. Square-wave waveforms are the hardest class (F1 = 95.7%), while sinusoidal and triangular each reach 99.0% F1, because abrupt square-wave transitions are partially smoothed by Tor multiplexing and network dynamics.

2026-fan-activeflowmark-assessing-tor §VI.D.2, Table V flow-correlationtraffic-shapeml-classifier

The host-profiling censor (passive traffic analysis: count connections per server, block those exceeding a threshold τ within a window w) blocks essentially all circumvention user traffic within 30 time steps for all classifier qualities tested (ρ_TP ∈ {0.9, 0.95, 0.99}), while causing far less collateral damage than zig-zag (never exceeding ~30% innocent server blocking). Snowflake resists this attack well: with w=3, τ=3, over 94.48% of users receive a proxy within 2 steps even with worst-classifier rules, and final unblocked server rates are 91.24–99.04%. The host profiling approach is feasible for passive censors who cannot enumerate the distribution channel.

2026-fares-game §5.2, Table 2, Table 3 traffic-shapeml-classifier