A CAS-institute master's thesis ("基于IP相似性分析的VPN服务识别技术研究") builds a VPN-server-identification system combining active TCP/UDP port probing (SYN scan on ports 1194 OpenVPN, 500/4500 IPSec, 1701 L2TP, 22, 443, 51820 WireGuard, 992, 4090, 655; UDP scan on 80/53/25/465/110/143/389/21/3389/445/69/3306/6379) with passive-traffic "IP similarity" graph analysis, reaching 92.44% online-test accuracy and supporting dynamic feature-library updates as services change.

A 2024 IIE CAS thesis proposal (advisor name redacted, a senior-grade engineer) explicitly frames "website identification under ECH encryption" as necessary infrastructure for China's CAC/MIIT/MPS "Clean Web"/"Sword Net" enforcement campaigns, and reports concrete progress already made: a Go-based DNS active-probing framework scanning 1.7B+ domains at roughly 11,000 domains/sec to build a global ECH-deployment map (10 anonymity sets / 79 domains found as of April 2024), and an already-implemented ECH-flow filter keyed on the TLS extension-type IDs used across ECH draft versions 07-18, with planned work on cross-flow traffic correlation plus raw-byte/flow-feature fusion to identify the specific site within an ECH anonymity set.

A MESA Lab student research report catalogs the GFW's known Shadowsocks-detection methodology (passive detection via first-packet length/entropy; active probing triggered after as few as 13 legitimate client connections, typically within seconds of the first legitimate connection) alongside six published ML-based Shadowsocks traffic-classification techniques (a packet-size-image CNN at >98% accuracy, random-forest on flow/host/DNS-behavior features, PCA-Pearson feature selection), compiled as apparent background research for in-house detection work.

The same internal research note's second research point develops an ML-based detector for Geneva-style automated censorship-evasion traffic; simple flow-level features (flow size, max packet size, RST/SYN/FIN flag counts, forward init-window bytes, inter-arrival timing) achieve near-perfect (ROC-AUC ~1.00) classification of Geneva-generated evasion traffic against CICIDS2017 and MAWI backbone background traffic using decision trees, LightGBM, XGBoost and random forest, with abnormal flow size (~150 bytes vs. 1000-30000 bytes typical) identified as the single most discriminative feature.

A June 2022 IIE CAS master's thesis, 'Research on Key Technologies of DoH Traffic Identification' (author name redacted, supervised by Professor Fang Binxing -- publicly known as chief architect of the Great Firewall and later Geedge Networks' founder/chief scientist), develops: (1) a light-load connection-multiplexing active scanner to discover DoH resolvers at scale, (2) a one-class ML classifier trained only on DoH samples plus TLS statistical features to catch DoH traffic missed by rule matching, and (3) a DoH fingerprinting method that strips HTTP/2 control-frame noise -- explicitly aimed at 'identifying users visiting websites from passive DoH traffic,' i.e. website fingerprinting through encrypted-DNS-wrapped connections.

A 2020 IIE CAS master's thesis (author and advisor names redacted) develops ML methods to identify audio/video traffic within encrypted flows without payload inspection, by quantifying on/off transmission burst patterns and binning long packet-length/timing sequences into short fixed-length feature vectors usable from as little as 5 seconds of traffic, plus a lightweight heuristic-threshold variant with a congestion-correction feature scheme built into a deployed identification system.

A 2020 MESA Lab monthly report describes building a sapp plugin that extracts packet-sequence features specifically "for DoH (DNS-over-HTTPS) service discovery," alongside a broader CSTNET DoH measurement-report effort and configuring a DNS-to-DoH gateway -- confirming sapp is used to fingerprint DoH traffic via statistical sequence features rather than plaintext DNS content.