The StegoTorus HTTP module degrades severely with network latency: it can sustain only a 50 kB/s stream at latencies below 200 ms and fails entirely at higher rates or latencies, because the HTTP request-response pattern transfers only one or two 512-byte Tor cells per round-trip. Plain Tor and chopper-only StegoTorus show no measurable throughput degradation at latencies up to 450 ms. Increasing parallel HTTP connections improves low-latency throughput but does not recover high-latency performance.

HTTP steganography in StegoTorus expands upstream traffic by a factor of 41× and downstream by 12× compared to a direct connection (uploading 966,964 bytes vs. 23,643 bytes to transfer a 1 MB file). Chopper-only operation adds only ~2.7× upstream overhead, comparable to plain Tor. Maximum achievable goodput with the HTTP module is ~27 kB/s (~4× a 56 kbps modem), which the authors attribute to a minimum expansion factor of 8× inherent in contemporary steganographic schemes.

§6, Table 3, Figure 7 evaluation

A naive-Bayes website-fingerprinting classifier achieves AUC > 0.94 against vanilla Tor for 8 of 9 Alexa top-ten sites (e.g., Wikipedia 0.9991, YouTube 0.9947). Against StegoTorus-HTTP, AUC drops to ≤ 0.75 for 7 of 9 sites (YouTube 0.4125, Facebook 0.5413, Google 0.6928), which the authors argue is too low for practical perimeter-scale deployment where near-perfect precision is required to avoid error floods.

§5.2, Table 2 evaluation

Tor's fixed 512-byte cells packed into TLS 1.0 records produce a characteristic TCP payload of 586 bytes (512 + 74 bytes of TLS overhead). A perimeter filter running a simple exponential moving average (τ ← ατ + (1−α)1ₗ₌₅₈₆, α=0.1, T=0.4) identifies Tor flows within a few dozen packets; this attack succeeds at backbone rates of ~540,000 packets/second on commodity hardware. Obfsproxy does not alter packet sizes or timings and therefore does not defeat this classifier.

§5.1, Figure 3 detection

StegoTorus distributes a fixed set of packet traces and HTTP covertext databases with the software, but allows users to record their own; classifiers trained on the distributed covertext will not generalize to user-generated databases. The paper further notes that reusing a small number of traces repeatedly creates a statistical fingerprint because censors can learn conversation patterns from packet sizes and timings alone, implying that trace diversity must be maintained over time.

§4.1.1, §5.2 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 paper identifies a fundamental architectural vulnerability in single-IP circumvention designs: a relay must generate new observable flows (via DNS or TLS SNI) to reach end services after receiving client connections, creating a detectable server-and-client behavioral contrast. A relay accessing user-facing domains (news, social media) scores high on a Relay Suspicion Score (w=0.9) versus infrastructure domains (w=0.1). The paper argues this host-level signal is censorship-invariant and cannot be concealed by link obfuscation.

2026-almutairi-server §2.4, §4 traffic-shapedpisni-blocking

Simulations extending the ENEM19 game-theory framework show that ephemeral proxy schemes (modeled on Snowflake/Lantern) effectively neutralize both the "optimal" and "aggressive" censors from the original framework. In overprovisioned settings (proxies arriving at 250/step vs. 200 clients/step), even the null censor scenario outperforms either censor in equal-arrival settings. Over 90% of waiting users receive a proxy within 1 time step. The critical variable is not censor sophistication but proxy arrival rate relative to client demand—high proxy churn combined with high arrival rate defeats both enumeration strategies tested.

2026-fares-game §4, Fig 1, Fig 2, Table 1 dpitraffic-shape

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