Against the state-of-the-art DeepCoFFEA flow-correlation attacker, FC-DeTorrent reduces the true positive rate at a 10^-5 false positive rate to approximately 0.12 — less than half that of the next-best defense Decaf (TPR ≈ 0.29) — while using 97.3% bandwidth overhead, without delaying any real traffic packets.

An attacker who generates 10 defended copies of each training trace (re-sampling noise each time) improves Tik-Tok accuracy against DeTorrent from 31.9% to 48.2%, demonstrating that dataset augmentation with multiple defended samples is a practical countermeasure against randomized padding defenses including DeTorrent and FRONT.

§6.4 detection

DeTorrent exhibits strong diminishing returns in the bandwidth-performance tradeoff: increasing the dummy-download budget from N=1,000 to N=3,000 reduces Tik-Tok accuracy by ~19.1 percentage points, while a further increase from N=5,000 to N=7,000 yields only an additional 4.9-point reduction (accuracy floor near 20.8% at ~210% overhead). At the lowest tested budget (~40% overhead) Tik-Tok accuracy is still only 52.8%.

§6.2, Figure 6 evaluation

DeTorrent is implemented as a Tor pluggable transport on top of the WFPadTools/Obfsproxy framework and deployed against live Tor traffic; a modest VPS with 4 GB RAM and 2 vCPUs running at under 50% CPU utilization can defend five simultaneous connections in real time with no GPU required. Performance drops only 0.7% when the generator is trained on one dataset partition and tested on another.

§5.4 deployment

DeTorrent reduces closed-world Tik-Tok attack accuracy from 93.4% to 31.9% on the BE dataset — 10.5 percentage points better than the next-best padding-only defense (FRONT at 42.4%) — and reduces Deep Fingerprinting accuracy from 94.3% to 30.0%, at a bandwidth overhead of 98.9%. On the larger DF dataset, Tik-Tok accuracy falls from 97.7% to 79.5%.

§6.1, Table 4 evaluation

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

Automated proxy engines (e.g., Xray-core running VLESS Reality in automated mode) generate deterministically rigid inter-arrival time distributions because they cannot synthesize the stochastic variance of human-driven IAT, even when volumetrically anchored to benign distributions ('Fat Middle' anchoring via AMOI). The AEGIS Thermodynamic Variance Detector identifies this rigidity via Shannon Entropy of hidden states across 1,000-packet causal windows, rendering volumetric anchoring mathematically distinguishable from genuine human traffic.

2026-ferrel-aegis-adversarial-entropy-guided §III-E, §V-B ml-classifiertraffic-shapeflow-correlation