The classifier uses a 3,000-dimension binary vector recording which upstream and downstream packet sizes appear across the full session, combined with aggregate biflow statistics (total packets, burst length, transmission time, incoming/outgoing fractions). This packet-size histogram is the highest-dimensionality feature in the set.

The authors trained on 1 GB of captured Shadowsocks traffic and 1 GB of non-Shadowsocks traffic from a single host, then tested on over 1 GB of each from 26 randomly selected hosts. The cross-host generalization of the model is demonstrated but no explicit false-positive or false-negative rates are reported.

§V.B–C evaluation

A Random Forest classifier with 100 CART trees and a sqrt(C) feature-selection strategy achieves over 85% accuracy detecting Shadowsocks traffic from biflow statistics. Accuracy increases monotonically with train-set and test-set size before plateauing.

§V, Abstract detection

Shadowsocks traffic appears as ordinary TCP with no payload keywords or obvious protocol markers because the entire payload is encrypted; firewalls cannot distinguish it from generic TLS without behavioral flow analysis. This makes signature- and keyword-based detection ineffective against it.

§III.A detection

The paper identifies that Shadowsocks can also serve as a transport layer for Tor and VPN connections, meaning a Shadowsocks flow detector functions as a first-stage classifier that unmasks compounded anonymity systems. The authors explicitly cite this as a motivation for detection.

§II 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