Even with tls-auth/tls-crypt HMAC protection making OpenVPN servers nominally 'probe-resistant' (silent to unauthenticated clients), the framework fingerprints servers via TCP-level timing side channels: a complete 16-byte client-reset probe triggers an immediate connection drop (HMAC validation fails after full packet reassembly), while a 15-byte truncated probe causes the server to stall awaiting the final byte until a server-specific handshake timeout expires. Over 97% of non-OpenVPN endpoints have RST thresholds below 500 or above 4,000 bytes, versus OpenVPN's characteristic 1,550–1,660 bytes derived from default MTU configurations.

OpenVPN's application-layer P_ACK packets — uniform in size and concentrated only in the handshake phase — provide a timing and count fingerprint detectable via threshold comparison over 10-packet bins. Tunnel-based obfuscation wrappers (Stunnel, SSH, obfs2/3, Shadowsocks) that do not add random padding preserve the 1:1 packet correspondence with the underlying OpenVPN stream, leaving 16 of 20 tested tunnel-based obfuscated configurations vulnerable to ACK fingerprinting.

§6.2, §9.1 detection

34 of 41 obfuscated OpenVPN configurations and 18 of 20 UDP configurations were co-located with vanilla TCP OpenVPN servers within the same /29 subnet; probing the /29 subnet of a suspected obfuscated or UDP endpoint revealed nearby vanilla TCP servers, enabling confirmation by 'guilt by association' even when the obfuscated endpoint itself resisted direct fingerprinting. Some providers additionally share infrastructure across different VPN brands, further compounding exposure.

§7.5, §9.1 detection

A two-phase passive-filter-plus-active-probing framework deployed at a 1-million-user ISP identified 85.90% of vanilla OpenVPN flows (1,718/2,000) and 72.67% of obfuscated flows (1,468/2,020), with an upper-bound false positive rate of 0.0039% across over 10 million flows — three orders of magnitude lower than prior ML-based approaches (1.4–5.5%). The system processed 15 TB and 2 billion flows per day on a single commodity server.

§9, Table 3 evaluation

OpenVPN's unencrypted opcode header byte is exploited to fingerprint vanilla and XOR-obfuscated flows: the XOR patch specification excludes the first buffer byte (the opcode) from reversal, so opcodes are always XOR-ed with the same key byte and map deterministically to fixed ciphertext values. All 4 of the top-5 VPN providers that offer obfuscated services use XOR-based obfuscation, and all were flagged by opcode fingerprinting over 90% of the time.

§6.1, §9.1 detection

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