Salmon's defense against the active zig-zag attack — where a censor blocks a known server to force users onto new ones and watches for correlated reassignments — requires both per-user authentication (unique login credentials per server so unauthorized probes receive a plausible HTTPS page) and traffic camouflage. Without authentication, the server must respond as a functioning proxy to any connection, fully exposing itself to the censor; without camouflage, even a rejected connection may reveal the server's nature.

Salmon simulations show that a censor with agents comprising 1% of 10,000 users can block at most 4A servers (one block per agent per full group) against a system with 1,000–2,000 servers; server groups with a hard cap of M=10 users that fill entirely with legitimate users before any agent joins become permanently invincible to server discovery. The censor's optimal strategy is to ensure each agent is always alone in its group at the time of joining, which requires knowing the user arrival rate — information Salmon withholds by not publishing user statistics.

§5.1 / §5 evaluation

Without recommendation-tree grouping logic, a censor starting agents at trust level 6 who each recommend 1–2 additional agents (requiring 4–5 months of waiting) can cut off over 95% of users even at agent percentages in the 15–30% range, as shown in Figure 6. With recommendation-tree grouping enforced, the same attack at equivalent agent fractions produces dramatically lower service disruption because agents cluster among themselves rather than spreading across innocent user groups.

§5.3 / Figure 6 detection

Salmon's trust-level mechanism (7 discrete levels; promotion from level n to n+1 requires 2^(n+1) days; banning triggered when suspicion exceeds T=1/3) reduces the fraction of users cut off by an attacking censor by more than 3× relative to rBridge under the same agent-percentage conditions. Simulations with 10,000 users (1–10% censor agents) and 1,000–2,000 servers show that trust levels keep high-seniority innocent users isolated from newer users where agents concentrate.

§5.2 / §3.6 defense

A single harvesting script running for 9 days on one free Amazon EC2 instance verified 3,101 working VPN Gate servers by testing 44,039 IP addresses, demonstrating that VPN Gate's collective defense mechanism — which relies on detecting automated scanning patterns — can be fully bypassed by routing successive queries through previously verified VPN servers. This result implies that a censor could, with no collateral damage, essentially completely shut down VPN Gate by blocking all verified servers.

§4.1 evaluation

The September 2025 leak of ~600 GB from Geedge Networks and the MESA Lab (Institute of Information Engineering, Chinese Academy of Sciences) is the largest known document disclosure from the GFW vendor ecosystem. It establishes a direct lineage: MESA Lab (founded 2012 by Fang Binxing's team, annual contracted revenue >35M RMB by 2016) spun out Geedge Networks in 2018, with MESA alumni filling key engineering roles (e.g. Zheng Chao as CTO). The leak includes ~64 GB of MESA git repositories, ~35 GB of MESA internal documents, ~15 GB of Geedge internal documents, and a ~3 GB Jira export — providing direct access to source code, work logs, and internal communications behind GFW R&D.

2025-geedge-mesa-leak §4 dpiactive-probingml-classifiersni-blockingfully-encrypted-detect

Internal Geedge documents confirm active contracts to deploy GFW-derived censorship and surveillance infrastructure in Myanmar, Pakistan, Ethiopia, Kazakhstan, and at least one additional unidentified country under the Belt and Road framework, in addition to domestic deployments in Xinjiang, Jiangsu, and Fujian. The exported product (the Tiangou Secure Gateway / TSG line) is not a stripped-down export variant — leaked TSG documentation shows DPI, active-probing, ML classifiers, and granular per-region traffic control rules that mirror the domestic GFW capability set.

2025-geedge-mesa-leak §5, §6 dpiactive-probingml-classifiersni-blockingdns-poisoningfully-encrypted-detecttraffic-shape

InterSecLab frames the Geedge/TSG export program as the commoditization of national firewall capability: rather than each censor state independently developing detection infrastructure, they contract Geedge for a turnkey system incorporating the cumulative R&D of MESA Lab (>10 years, National Science and Technology Progress Award winners). This structural shift means the marginal cost for an autocratic government to acquire GFW-grade censorship is now a procurement decision, not a multi-year engineering program. The report identifies that Geedge's relationship with the MESA Lab gives customer states indirect access to ongoing academic R&D improvements, not just a static product.

2025-interseclab-internet-coup §2, §6–§7 (InterSecLab report) dpiactive-probingml-classifier

InterSecLab's 76-page analysis of the Geedge/MESA leak (based on nine months of indexing and translating >100,000 documents) characterizes the Tiangou Secure Gateway (TSG) product line as a commercially deployable detection stack that combines deep packet inspection, real-time mobile subscriber monitoring, active probing, ML-based traffic classifiers, and granular per-region rule sets. TSG is not a research prototype — leaked documentation includes deployment timelines and client government interactions for Kazakhstan, Ethiopia, Pakistan, Myanmar, and one unnamed country, with censorship rules explicitly tailored to each region.

2025-interseclab-internet-coup §3–§5 (InterSecLab report) dpiactive-probingml-classifiersni-blockingip-blockingtraffic-shapefully-encrypted-detect

Justice for Myanmar documents that Geedge Networks supplied Myanmar's military junta with GFW-derived surveillance and censorship infrastructure under Belt and Road frameworks following the February 2021 coup. The deployed system (Tiangou Secure Gateway / TSG) incorporates the same DPI, active-probing, and ML-classifier capabilities as the domestic Chinese GFW, giving Myanmar one of the most technically capable censorship systems in Southeast Asia.

2025-jfm-silk-road-surveillance §3, §5 dpisni-blockingip-blockingtraffic-shapeml-classifieractive-probing

The paper proposes modeling HCS undetectability as a simulation-based cryptographic distinguishability problem: if traces produced by the real-world HCS channel are computationally indistinguishable from ideal-world application-channel traces (T_HCS ∼ T_simulator), the HCS achieves provable security against any adversary — passive or active. The simulation paradigm is parametric in adversary capability, meaning a single proof covers the full spectrum from passive SNI monitoring to active DPI.

2025-pereira-position §1, §2 dpitraffic-shapeactive-probing