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).

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

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.

§V.A, Table I evaluation

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