StegoTorus distributes a fixed set of packet traces and HTTP covertext databases with the software, but allows users to record their own; classifiers trained on the distributed covertext will not generalize to user-generated databases. The paper further notes that reusing a small number of traces repeatedly creates a statistical fingerprint because censors can learn conversation patterns from packet sizes and timings alone, implying that trace diversity must be maintained over time.

The StegoTorus HTTP module degrades severely with network latency: it can sustain only a 50 kB/s stream at latencies below 200 ms and fails entirely at higher rates or latencies, because the HTTP request-response pattern transfers only one or two 512-byte Tor cells per round-trip. Plain Tor and chopper-only StegoTorus show no measurable throughput degradation at latencies up to 450 ms. Increasing parallel HTTP connections improves low-latency throughput but does not recover high-latency performance.

§6, Figure 6 evaluation

HTTP steganography in StegoTorus expands upstream traffic by a factor of 41× and downstream by 12× compared to a direct connection (uploading 966,964 bytes vs. 23,643 bytes to transfer a 1 MB file). Chopper-only operation adds only ~2.7× upstream overhead, comparable to plain Tor. Maximum achievable goodput with the HTTP module is ~27 kB/s (~4× a 56 kbps modem), which the authors attribute to a minimum expansion factor of 8× inherent in contemporary steganographic schemes.

§6, Table 3, Figure 7 evaluation

A naive-Bayes website-fingerprinting classifier achieves AUC > 0.94 against vanilla Tor for 8 of 9 Alexa top-ten sites (e.g., Wikipedia 0.9991, YouTube 0.9947). Against StegoTorus-HTTP, AUC drops to ≤ 0.75 for 7 of 9 sites (YouTube 0.4125, Facebook 0.5413, Google 0.6928), which the authors argue is too low for practical perimeter-scale deployment where near-perfect precision is required to avoid error floods.

§5.2, Table 2 evaluation

Tor's fixed 512-byte cells packed into TLS 1.0 records produce a characteristic TCP payload of 586 bytes (512 + 74 bytes of TLS overhead). A perimeter filter running a simple exponential moving average (τ ← ατ + (1−α)1ₗ₌₅₈₆, α=0.1, T=0.4) identifies Tor flows within a few dozen packets; this attack succeeds at backbone rates of ~540,000 packets/second on commodity hardware. Obfsproxy does not alter packet sizes or timings and therefore does not defeat this classifier.

§5.1, Figure 3 detection

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

Balboa's synchronous leaf-content replacement adds non-negligible timing differences that allow censors to identify its activity with up to ~90% accuracy over different network conditions. The timing anomaly arises because Balboa performs data substitution directly at each data exchange, delaying the server's response while covert data is prepared.

2026-kamali-huma §I (Introduction), §II-A traffic-shapeml-classifier

Huma's deferred-reply / double-request receive (DRR) protocol reduces a traffic-fingerprinting XGBoost classifier's accuracy to at most 54% (near random guessing) across geographically distributed clients (San Francisco, Frankfurt, Bangalore). A Kolmogorov-Smirnov test on absolute page-load timing distributions yields D=0.03, p=0.98 for U.S. clients — substantially tighter than Waterfall of Liberty's D=0.11 at p=0.5 — confirming that Huma flows are statistically indistinguishable from benign HTTPS fetches.

2026-kamali-huma §V-D, Table II traffic-shapeml-classifier

The PPBR (probabilistic profile-based routing) protocol leaks user community membership through observable routing decisions: in a controlled experiment with 800 majority and 200 minority users, a statistical disclosure attack achieved a true positive rate of 100% and false positive rate of 0% when identifying minority users. Even under a conservative PPBR configuration (top 1/3 fraction acceptance), the attack achieved 100% TPR and only 0.4% FPR.

2026-ratliff-mirage §V ml-classifiertraffic-shape

Banking apps from major Russian institutions (Sber, T-Bank, VTB, Alfa-Bank) combine VPN detection with behavioral biometrics — screen pressure, touch coordinates, and gesture timing — enabling cross-account re-identification of users behind proxies. 11 apps received a "RED" (maximum surveillance) rating. T-Bank, Yandex services, and MAX additionally deploy active anti-analysis features that detect research tooling on the device (rooted devices, emulators, Frida, etc.).

2026-rks-russian-apps-vpn-detection §5, §6 ml-classifiertraffic-shape

Amnesty International's 102-page investigation identifies a multi-vendor surveillance stack deployed in Pakistan: Chinese DPI (Geedge/MESA-derived), Canadian social-media monitoring (Netsweeper), and Emirati commercial spyware (Pegasus and FinFisher). The system enables deep packet inspection, SNI-based filtering, and traffic-shape classification at national scale, including targeted interception of encrypted messaging apps and VPN traffic.

2025-amnesty-pakistan-shadows §3, §5 dpisni-blockingip-blockingtraffic-shapeml-classifier