GoHop's naïve traffic shaping targeting a uniform packet-size distribution (0–MTU) successfully morphed both HTTP and SSH flows: K-S test D values were 0.019 (HTTP) and 0.022 (SSH), both below the 0.025 rejection threshold, with p-values of 0.20 and 0.11 respectively. After shaping, packet-size CDFs and statistical metrics (mean ~782–783 bytes, variance ~163,600) for both protocols became nearly identical, eliminating the size signals that distinguish them.

GoHop without traffic shaping achieved 76.8–78.5 Mbps (virtual NIC) on a 1 Gbps LAN; traffic shaping reduced this to 58.1 Mbps (~26% overhead from fragmentation). In a Beijing-to-Seattle real-world download test, GoHop delivered 960–999 KB/s against a 1,544 KB/s direct baseline, with the 96.7 Mbps WAN link—not GoHop—as the bottleneck. This compares to Tor's 40–300 KB/s (30–80 KB/s with obfuscation plugins such as SkypeMorph).

§V.B, Table II, Table III evaluation

Packet padding alone is insufficient to defeat statistical traffic analysis unless every packet is padded to MTU; small-size padding has minimal effect on classifier accuracy (citing Hjelmvik & John 2010). Traffic shaping that also fragments large packets—transforming the full packet-size CDF to match a target distribution rather than merely inflating small packets—is required to statistically impersonate a target traffic class.

§III.B defense

A pre-shared key enables encrypting the entire GoHop packet—header, payload, and padding bytes—achieving true randomness in the full byte stream. Standard VPN protocols such as OpenVPN encrypt only the payload while leaving headers in plaintext, exposing protocol-identifying fields to DPI without payload inspection. This design choice is a prerequisite for defeating header-based fingerprinting.

§III.A defense

Spreading UDP datagrams across a randomized port range breaks traditional 5-tuple-based session tracking, randomizes per-port inter-arrival times, and reduces per-port throughput to a small fraction of the aggregate—making per-flow statistical analysis significantly harder. Critically, the number of random ports does not reduce aggregate throughput: GoHop measured 76.8 Mbps (1 port) versus 78.5 Mbps (100 ports) at the virtual NIC.

§III.C, §V.A, Table II defense

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