A Random Forest classifier trained solely on structural features of third-party request trees achieves ROC AUC of 0.81 and 72% balanced accuracy across 4,660 news domains with ≥50 daily observations. Performance degrades to ROC AUC 0.78 and 0.68 for domains requiring ≥100 and ≥150 daily observations respectively, driven by reduced training-set size rather than feature quality.

152 of 5,478 crawled domains (approximately 2.8%) deployed active bot-detection measures—captcha delivery or perimeter protection—that blocked automated OpenWPM crawling entirely. The authors note this disproportionately excludes untrustworthy sites, biasing the training dataset toward well-resourced trustworthy outlets and limiting recall on the untrustworthy class.

§4 Limitations evaluation

The five most important predictive features are: (1) average children per non-leaf tree node, (2) 7-day rolling average of maximum tree breadth, (3) 7-day rolling average of average breadth, (4) average children per parent, and (5) 7-day rolling average of third-party requests. Temporal stability features (rolling means and daily deltas) rank ahead of most static snapshot features, indicating that behavioral consistency over time is more discriminative than point-in-time structure.

§5.1, Figure 4 detection

Of 8,004 unique third-party domains identified across 3,410 crawled news sites, 997 appear exclusively on untrustworthy websites and 2,992 appear exclusively on trustworthy ones. Domains disproportionately associated with untrustworthy sites include Yandex, Zamanta, and PayPal; domains exclusive to trustworthy sites are predominantly small-to-medium advertising and analytics actors rather than major platform giants.

§5.2 evaluation

Trustworthy news sites show dramatically more complex third-party structures than untrustworthy ones: mean MaxBreadth 39.22 vs 19.63, mean ThirdPartyRequests 137.45 vs 74.12, and mean unique third-party domains 44.15 vs 20.31. This finding reverses prior work (Han et al. 2022) and the authors attribute it to untrustworthy sites being under-resourced and optimized for content spread rather than user experience.

§5, Table 1 detection

Stage 1 of the detection pipeline uses a lightweight heuristic: restrict analysis to IP addresses in "VPS-dense ASNs," which censors already target for resource-intensive inspection of fully-encrypted traffic. This pre-filter dramatically reduces the search space before applying the more expensive dual-role behavioral analysis. The evaluation was conducted without Stages 1 and 3 due to dataset limitations, meaning the reported 23% recall and 0.18% FPR are conservative lower bounds on the full pipeline's performance.

2026-almutairi-server §2.1, §2.2, §3.4 fully-encrypted-detectdpiml-classifier

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