Even when individual WebRTC flows pass traffic analysis, a censor can identify CRON users via three long-term statistical attack types: S1 (simultaneous video calls, atypical for normal users), S2 (sudden connections to previously unknown parties), and S3 (calls at anomalous times, frequencies, or durations). Relay nodes in multi-hop circuits are particularly exposed via S1 because conducting multiple simultaneous video calls is highly atypical in normal user profiles.

When a censor controls the WebRTC signaling plane, it can mount MITM attacks against CRON's vanilla covert encoding because the encoding 'fully replaces the video payload with an apparently random covert data signal that results in a scrambled video image at the receiver's endpoint.' By replaying the captured video through a WebRTC gateway, the censor obtains direct visual evidence of payload manipulation.

§4.2 detection

CRON restricts multi-hop covert circuits (N≥1 relays) to delay-tolerant traffic only, because establishing multiple simultaneous WebRTC video calls is 'highly atypical in normal user profiles' and would trigger S1 behavioral anomaly detection. Real-time interactive tunneling is limited to direct circuits (N=0) within pre-existing calls, and active mode introduces only bounded variability in call times and frequency to stay within plausible user-profile ranges.

§4.1 defense

Protozoa creates a ≈1.4 Mbps covert channel over WebRTC by replacing encoded video frames with covert payload while preserving SRTP packet size and timing properties, making Protozoa flows 'hardly distinguishable from unmodified WebRTC streams using existing ML-based traffic classifiers.' Since all unencrypted packet fields remain intact, DPI cannot detect the tunnel either.

§2 defense

CRON's stego circuits defend against adversary-controlled WebRTC services by embedding covert data into encoded video frames at the compressed data domain using video steganography algorithms, maintaining the visual characteristics of the video feed rather than replacing it entirely. Endpoint authentication uses public-key encryption with keys exchanged out-of-band, preventing MITM key substitution through the censor-controlled signaling server.

§4.2 defense

A three-stage detection pipeline exploiting the "dual-role" behavioral fingerprint of single-IP circumvention relays achieved 23.2% recall (96/414 ground-truth relays) with a 0.18% false-positive rate against 97,651 benign TLS servers, for an overall accuracy of 99.5%. The ground-truth set covered OpenVPN, WireGuard, and SOCKS relays identified in a 17 TB single-day backbone trace (WIDE Project, April 9, 2025).

2026-almutairi-server §3.4, Table 1 traffic-shapedpiflow-correlation

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

Multi-censor simulations show that single-censor-optimized distribution strategies perform suboptimally in realistic multi-region deployments. When two networks have different censor strategies (e.g., one optimal, one zig-zag), the distributor cannot detect that a proxy is blocked until all censors have blocked it; this leaves clients without reachable proxies despite the proxy appearing "available" from the distributor's view. The authors conclude that "single-censor evaluation does not accurately predict more realistic deployment performance." A zig-zag censor in one region with 0.25 weight caused 44.4% collateral damage while reducing proxy lifetime to a median of 4 steps.

2026-fares-game §6, Table 4 traffic-shapeflow-correlation

The zig-zag traffic analysis attack (confirmed supported in Geedge TSG leak) rapidly enumerates all static proxy pools. With ζ_watch ∈ {4, 6} steps and a best-quality classifier (ρ_TP=0.99, ρ_FP=0.001), almost total proxy enumeration and user blockage occurs well before step 300. Even ζ_watch=2 leaves ~50% of users blocked. Collateral damage is high across all settings when ζ_watch ≥ 4: eventually ~50% of innocent servers are also blocked. However, Snowflake-style ephemeral proxies resist zig-zag effectively: reachability remains above 95% after 360 steps because churn prevents the censor from expanding its known proxy set beyond agents' direct assignments.

2026-fares-game §5.1, Fig 3, Fig 4 traffic-shapeflow-correlation

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