A semantics-based attack that flags HTTP flows carrying structurally invalid PDF documents as Stegotorus produces false-positive rates as high as 43% across three campus datasets (10,847 PDF flows examined), because malformed, partial, and non-standard PDFs are common in real network traffic. By contrast, active HTTP-response fingerprinting of a suspected Stegotorus server yields only 0.03% false positives (3 matching servers out of 9,320 Alexa-top-10K servers), but requires active probing and is detectable by the proxy operator.

Format-transforming encryption (FTE) as deployed in the Tor Browser Bundle is detected by combining a URI Shannon-entropy threshold (≥5.5 bits) with an exact URI length check (239 bytes) on the first HTTP GET request. This embellished test produces only 264 false positives across approximately 10 million HTTP URIs in three campus datasets, while a length-only test causes roughly 15% false-positive rate over the same flows.

§5.2, Figure 6 detection

CART decision-tree classifiers trained on entropy-based and packet-header features detect all five Tor pluggable transports (obfsproxy3/4, FTE, meek-amazon, meek-google) with average PR-AUC=0.987, TPR=0.986, and FPR=0.003 on synthetic traces. On 14 million real campus flows the highest per-obfuscator FPR is 0.65%, and meek-google yields only 842 false positives across all three datasets. However, cross-environment portability is poor: classifiers trained on an Ubuntu/campus setup and tested on a Windows/home network achieve true-positive rates as low as 52% with false-positive rates reaching 12%.

§6.1, Tables 8–9 detection

The paper demonstrates that 'having no fingerprint is itself a fingerprint': randomizing obfuscators that emit uniformly random bytes from the first packet are detectable precisely because conventional protocols (TLS, SSH, HTTP) always begin with fixed plaintext headers. This structural distinction requires no deep payload parsing — the attack operates on only the first TCP packet — and achieves TPR=1.0 / FPR=0.002 against obfsproxy3/4 using commodity-implementable statistics.

§1, §5.1 defense

Obfsproxy3 and obfsproxy4 are reliably detected by an entropy-distribution test (KS test, block size k=8) applied to the first 2,048 bytes of the first client-to-server packet, combined with a minimum payload-length check of 149 bytes. On three university campus datasets totaling over 14 million TCP flows, the test achieves TPR=1.0 with FPR ranging from 0.24% to 0.33%. Omitting the length check raises the SSL/TLS false-positive rate to approximately 23%.

§5.1, Table 5 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.

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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