Capturing as little as 30 seconds of a multimedia-tunneling flow is sufficient for XGBoost to reach the same AUC achieved with a 60-second window (AUC=0.99 for Facet s=50%, AUC=0.95 for DeltaShaper h320×240, 8×8, 6, 1i at 30s). Classification performance degrades monotonically below 30 seconds, reaching AUC≈0.81 (Facet) and 0.75 (DeltaShaper) at 1 second.

CovertCast — which scrapes web content into colored-matrix frames broadcast over YouTube live streams — is fully detected by the χ² similarity classifier with TPR=100% and FPR=2%. The KL and EMD classifiers achieve TPR>96.5%, indicating the system provides essentially no unobservability in practice.

§3, Table 1 detection

Feature importance analysis of XGBoost models reveals that Facet covert channels are identifiable primarily through packets in the 115–195 byte range (dominated by Skype audio packets), while DeltaShaper is identifiable through two distinct packet-length clusters: 85–100 bytes and 1105–1205 bytes. XGBoost assigns non-zero importance to only ~58% of the 300 quantized packet-length bins for Facet and ~42% of 600 bins for DeltaShaper, indicating that leakage is concentrated in a narrow portion of the packet-size distribution.

§4.4, Figure 4 detection

Unsupervised and semi-supervised anomaly detection methods (OCSVM, Isolation Forest, shallow autoencoders) perform near-random when attempting to detect multimedia protocol tunneling: OCSVM achieves average AUC between 0.518–0.584 across all tested configurations, Isolation Forest between 0.519–0.557, and autoencoders reach a maximum AUC of 0.702 only under optimal hyperparameter search. The paper concludes that labeled training data is a hard requirement for effective covert-channel detection.

§5, Table 5 detection

Decision tree classifiers (XGBoost) can flag 90% of Facet multimedia-tunneling traffic while erroneously flagging only 2% of legitimate Skype connections (FPR=2%). Against DeltaShaper at its most conservative configuration (h160×120, 4×4, 6, 1i), XGBoost achieves AUC=0.85, demonstrating that existing unobservability claims for all three systems (Facet, CovertCast, DeltaShaper) were flawed.

§4, Table 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