Packet-length frequency distributions reliably distinguish regular Skype calls from irregular streams using Earth Mover's Distance (EMD): regular streams consistently produce EMD < 0.1 against a reference stream, while irregular streams range from 0.025 to 0.25. At the breakeven threshold ∆I = 0.066, an EMD classifier achieves 83% accuracy (equal sensitivity and specificity). An aggressive policy (∆A) blocks 95% of legitimate calls to catch all irregular streams; a conservative policy (∆C = 0.11) passes 80% of irregular streams to avoid false positives.

DeltaShaper embeds covert TCP/IP data into Skype's encrypted video stream using a virtual camera interface, treating Skype as a black box rather than mimicking its protocol. This approach provides active-attack resistance by design: any in-path perturbation affects covert and legitimate streams identically, because real Skype software processes both. The system achieves a goodput of 2.56 Kbps (with Reed-Solomon ECC) or 3.12 Kbps (without ECC) at optimal encoding parameters (320x240 area, 8x8 cell size, 6 bits/cell, 1 fps), with RTT of approximately 3 seconds.

§4, §5, Table 2 defense

Encoding parameters must be jointly tuned to remain unobservable: only specific combinations stay below the intermediate blocking threshold ∆I. Valid configurations at 1 fps include (160x120, 4x4) and (320x240, 8x8) areas/cell sizes; increasing frame rate above 1 fps pushes EMD above ∆ for all multi-bit encodings. As bits per cell increase, video compression introduces more decoding errors — error rates become unacceptable above 6 bits/cell for the (320x240, 8x8) configuration, yielding the candidate encoding: 320x240 area, 8x8 cells, 6 bits/cell, 1 fps.

§6.2, §6.3, Figures 9–12 evaluation

Across eight combinations of traffic features (packet length, bi-gram packet length, inter-packet time, bi-gram inter-packet time) and two similarity metrics (EMD, KS), adversarial classification accuracy against DeltaShaper streams ranges from 72–90% in unperturbed conditions. Bi-gram inter-packet times with EMD achieves 88% accuracy, matching packet-length/EMD, but requires roughly 10x the computation (~64s vs ~6s). Bandwidth throttling to 300 Kbps degrades classifier accuracy from 88% to 75%, but also drops Skype frame rate from 30 to 5 FPS, creating collateral damage that limits censor deployment of throttling as a detection aid.

§6.4, §6.5, Table 3 detection

FreeWave, the VoIP-based predecessor, was vulnerable to passive traffic analysis because its covert Skype streams exhibited packet-size distributions different from legitimate calls, enabling detection with high probability. DeltaShaper's video-based approach with EMD-constrained encoding addresses this specific failure mode, but at a severe throughput cost: FreeWave achieves 18.75 Kbps vs DeltaShaper's 2.56–3.12 Kbps goodput. Competing systems benchmark: CovertCast ~168 Kbps (no unobservability constraints), Castle 3.48 Kbps, SkypeLine 0.064 Kbps, Rook 0.024–0.04 Kbps.

§6.3, §2 evaluation

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