The authors propose a diversity-aware relay placement heuristic derived from bandwidth-weighted AS Hegemony scores to guide future mitigation of transit concentration. By incorporating consensus bandwidth weighting into hegemony calculations, the methodology reflects actual Tor traffic patterns rather than relay counts alone.

Country-level analysis shows significant sensitivity of AS-level transit dependency to the distribution of relay capacity across ASes within each country, implying that a censor controlling a dominant national transit AS gains disproportionately high visibility into Tor traffic if domestic relay capacity is concentrated in that AS's customer cone.

Abstract evaluation

SURF (AS1103) surged to over 50% exit hegemony in 2024, meaning a single AS gained visibility into the majority of bandwidth-weighted Tor exit traffic. This represents a dramatic concentration event in an otherwise persistent but distributed chokepoint landscape spanning 2015–2025.

Abstract evaluation

A longitudinal study of Tor transit dependency from 2015 to 2025 reveals persistent concentration in a small number of transit ASes, indicating that AS-level chokepoints enabling flow-correlation attacks are a structural rather than transient property of the Tor network.

Abstract evaluation

A three-stage detection pipeline exploiting the "dual-role" behavioral fingerprint of single-IP circumvention relays achieved 23.2% recall (96/414 ground-truth relays) with a 0.18% false-positive rate against 97,651 benign TLS servers, for an overall accuracy of 99.5%. The ground-truth set covered OpenVPN, WireGuard, and SOCKS relays identified in a 17 TB single-day backbone trace (WIDE Project, April 9, 2025).

2026-almutairi-server §3.4, Table 1 traffic-shapedpiflow-correlation

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

Active bandwidth perturbation has an inherent detectability–stability trade-off: overly aggressive low-rate phases cause Tor SENDME-based flow control stalls, retransmissions, timeouts, or circuit replacement before sufficient correlation evidence is collected. The paper selects a 30-second modulation period and an empirically determined minimum shaping rate; the usable shaping range varies with relay load, path length, TCP congestion control behavior, and Tor multiplexing.

2026-fan-activeflowmark-assessing-tor §VII-A flow-correlationtraffic-shape

Tornettools-based simulations with a 1%-scaled Tor network show that with 1 adversary-controlled exit relay (148 Mbps), the exit-observation probability pexit ≈ 2.13%; with 5 adversary-controlled relays, pexit exceeds 10%. Tor's bandwidth-weighted path selection means high-bandwidth malicious exits attract a disproportionate share of circuits, and repeated observations over multiple circuits compound correlation risk multiplicatively even at modest relay counts. The simulation scaled historical Tor consensus data to 1% of the real network (80 relays), and repeated observations across T independent windows compound as 1 - (1 - Pcorr)^T.

2026-fan-activeflowmark-assessing-tor §VI-A, Table I flow-correlation