UPGen's generator samples 18 independent parameters to produce 4.2×10^22 distinct structured encrypted protocols (entropy 38.4 bits). Each proxy is assigned a unique generated protocol, so identifying one protocol exposes only a single proxy. The generator was designed by studying 27 real-world encrypted protocols and sampling from observed structural patterns (greeting strings, handshake patterns, field orderings, key encodings).

Three open-source DPI tools (Zeek, libprotoident, nDPI) fail to identify 93–100% of UPGen flows across all tools. libprotoident misidentified 7% of UPGen flows as RTMP; nDPI and Zeek produced zero false labels. On a real-world MAWI/WIDE backbone capture, Zeek failed to recognize 90% of flows and nDPI failed on 67%, confirming that unidentified-protocol traffic is common in the wild; allowlisting without significant collateral damage (≥4%) is infeasible.

§4.4, Table 5, Table 6 evaluation

State-of-the-art ML classifiers (Deep Fingerprinting, Decision Tree, Random Forest, nPrintML) trained on known UPGen protocols and benign traffic always incur high out-of-distribution false-positive rates when attempting to block unknown UPGen protocols — in the vast majority of experiments the OOD FPR is 100%. The one exception (SSH OOD, Deep Fingerprinting) achieved a UPGen TPR of only 20%. By contrast, identical classifiers successfully generalize to block unknown Obfs4 flows with near-zero collateral damage in 3 of 4 cases.

§4.3.3, Table 3, Table 4 evaluation

In laboratory benchmarks, the best UPGen-generated protocol achieves 252 ms TTFB latency (vs 212 ms Obfs4, 313 ms TLS) and 4.25 Gbit/s throughput per core (vs 4.65 Gbit/s Obfs4, 9.42 Gbit/s TLS). The worst-case UPGen protocol (4.5 RTT handshake) reaches 677 ms TTFB but 3.70 Gbit/s throughput. In large-scale distributed Tor simulations, the choice of UPGen protocol had no statistically significant effect on end-to-end Tor flow performance.

§5, Table 7 evaluation

Stage 1 of the detection pipeline uses a lightweight heuristic: restrict analysis to IP addresses in "VPS-dense ASNs," which censors already target for resource-intensive inspection of fully-encrypted traffic. This pre-filter dramatically reduces the search space before applying the more expensive dual-role behavioral analysis. The evaluation was conducted without Stages 1 and 3 due to dataset limitations, meaning the reported 23% recall and 0.18% FPR are conservative lower bounds on the full pipeline's performance.

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Amnesty International's 102-page investigation identifies a multi-vendor surveillance stack deployed in Pakistan: Chinese DPI (Geedge/MESA-derived), Canadian social-media monitoring (Netsweeper), and Emirati commercial spyware (Pegasus and FinFisher). The system enables deep packet inspection, SNI-based filtering, and traffic-shape classification at national scale, including targeted interception of encrypted messaging apps and VPN traffic.

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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.

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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.

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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