Extending Slitheen to replace WebM video/audio frames reduced mean overhead from ~20x (image-only Slitheen) to 4.7x (±1.6) over 100 ten-minute sessions, while raising throughput to a mean of 581.7 kbps in video-only mode (max 2023.3 kbps, min 78.2 kbps) and 721.6 kbps in background-video mode (max 1528 kbps). This compares favorably to DeltaShaper's 2x overhead at only 7 kbps and Protozoa's up to 1.4 Mbps, while preserving Slitheen's resistance to traffic-analysis attacks.

OUStralopithecus (OUStral), a Selenium-based OUS implementing empirically-derived human browsing distributions — Weibull dwell times (λ=30s, k=0.75), Von der Weth action probabilities (45.1% internal-link clicks, 33% new-URL navigations), and Dubroy tab-switching rates — generated 471 requests with all Cloudflare Bot Management scores above the recommended blocking threshold of 30, while Slitheen and Waterfall consistently scored 1. Because Cloudflare has full HTTP-layer visibility (unavailable to a passive network censor), the paper argues a censor observing only encrypted traffic would be even less able to flag OUStral.

§3.1, §5.2.1, Figure 6 defense

Traffic replacement systems that only shape individual HTTPS flows remain vulnerable to censors monitoring inter-connection patterns over time. Waterfall's OUS (reloading the same page every second), Slitheen's OUS (naïve PhantomJS with no crawling), and Slitheen++'s OUS all produced non-human connection patterns detectable at the session level even when per-flow content is well-concealed. OUStral addresses this by shaping the distribution and sequencing of connections across an entire browsing session.

§1, §2.2, §3, §6 defense

Prior overt user simulators (OUS) using PhantomJS — including Slitheen, Waterfall, and Slitheen++ — received Cloudflare Bot Management scores of 1 (certainly bot-generated) and would be blocked by any operator following Cloudflare's recommended cut-off of 30. Slitheen++ improved marginally by adding user-agent randomization and brief inter-request pauses, but all PhantomJS-based OUS implementations were trivially detectable as bots.

§5.2.1, Figure 6 detection

Across tunnelling systems that apply traffic shaping against ML adversaries, a clear throughput cost emerges: Slitheen + OUStral with WebM replacement achieves up to 2.2 Mbps with 4.7x overhead; Protozoa (WebRTC, end-to-end) achieves up to 1.4 Mbps; DeltaShaper (VoIP) achieves only 7 kbps at 2x overhead. By contrast, Conjure (no traffic shaping) reaches 100 Mbps. Additionally, end-to-middle decoy-routing deployments incur a throughput penalty from packet-boundary parsing at the relay station that end-to-end systems (Protozoa, DeltaShaper) avoid.

§5.3.2, Table 2 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

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