A two-stage threshold classifier evaluated on 10,080 synthetic flows across 1,260 network condition combinations (20 RTTs × 21 loss rates × 3 bandwidths) achieved 100% accuracy in Stage 1 separating loss-based from non-loss-based CCAs, and produced only 16 false positives from BBR flows in Stage 2, correctly flagging all 1,257 Hysteria and 1,257 Brutal flows as custom CCAs.

Active mid-connection bandwidth throttling (e.g., 100 Mbps → 50 Mbps) cleanly separates BBR from Hysteria and TCP-Brutal: BBR converges to the new rate within a few probing cycles, while Hysteria and Brutal interpret reduced bandwidth as increased packet loss and raise their sending rate further. This active probing technique resolves the BBR ambiguity that passive measurement alone cannot.

§4.4 detection

BBR, a rate-based CCA already available in the Linux kernel, comes close to Hysteria's throughput performance when packet loss is below 20% — the typical range for cross-border Chinese links (5–15%, peak up to 50% per prior studies). Above 20% loss, Hysteria and Brutal maintain a significant throughput advantage over BBR, but the paper finds no compelling justification for custom CCAs given the marginal gains in that regime versus the fingerprinting cost.

§4.2, §6 defense

Custom CCAs that deviate from standard TCP/QUIC congestion response fundamentally contradict the core circumvention principle of traffic indistinguishability: by failing to back off under congestion signals, they produce traffic patterns that diverge from the vast majority of Internet flows that censors value, eliminating the collateral-damage protection that makes circumvention tools hard to block wholesale.

§1, §6 detection

Hysteria and TCP-Brutal maintain fixed sending rates regardless of packet loss, causing them to transmit at rates several orders of magnitude higher than loss-based CCAs (TCP/QUIC Cubic) at a 5% packet loss rate on a 100 Mbps link with 60ms RTT. This non-compliance with standard congestion backoff is reliably detectable across RTTs from 15ms to 300ms and loss rates from 0.1% to 20%.

§4.1 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