Across all 36 stages in the nine end-to-end experiments, the median iteration at which the running intersection shrank to ≤10 candidates was 3, and 64% of stages reached this threshold within 5 iterations. Per-stage candidate sets shrank by 72–87% after the first intersection and by 85–96% by the third iteration, driven by the short (~0.5–1.8s) observation windows that prevent unrelated circuits from injecting persistent false candidates.

In the 30 November 2025 Onionoo snapshot, approximately 75% of both guard and middle relay selection-probability mass lies within Fourteen-Eyes jurisdictions, with Germany, the United States, and the Netherlands hosting the largest shares. Because path selection is bandwidth-weighted, a state-level adversary from one of these jurisdictions is statistically likely to have ISP-level visibility at most relays a target introduction circuit passes through.

Appendix D, Fig. 7 evaluation

An adaptive intersection attack exploiting the 18–24h fixed lifetime of Tor onion-service introduction circuits reconstructed the complete four-hop introduction circuit in all nine live-network experiments. The median reconstruction required 260 iterations with an estimated median wall-clock time of 2.24h (12% of the 18h circuit lifetime) when per-relay visibility cost v=0; the slowest run completed in 6.63h (37% of 18h).

§4.2, Table 1 detection

A persistent 'tail' of recurring candidates — not the early phase — dominates convergence cost and introduces high variance: three Vanguard stages at identical consensus weight 1200 converged at iterations 12, 142, and 251. In 13 of 36 stages, the intersection held ≥3 candidates at the penultimate iteration and collapsed to a singleton in one step, suggesting the tail is caused by persistent third-party guard/vanguard traffic rather than random noise.

§4.2, Table 2 detection

Periodically rebuilding only the internal service-side path of the introduction circuit (entry guard → vanguard → middle relay) approximately every 10 minutes — while keeping the publicly advertised Introduction Point fixed — limits evidence accumulation to one rebuild interval without requiring a descriptor update. Under this mitigation the effective circuit lifetime visible to an intersection attacker drops from 18–24h to ~10 min, matching ordinary Tor circuit lifetimes.

§5 defense

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

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. Per-class F1 is 95.7% for square-wave, the hardest class, versus 99.0% each for sinusoidal and triangular.

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