A 2024 student-thesis project inside MESA Lab (chengyifei/yy_strategy_adjust, presentation titled "毕设交流_大模型策略控制" / "Thesis exchange: large-model strategy control") wires Moonshot AI's Kimi LLM into a "strategy adjustment" module via kimi_main.py/kimi_strategy.py/kimi_strategy_client.py and a content_role_system.txt role/prompt file, indicating exploratory R&D on using an LLM to drive dynamic policy/strategy decisions. Framed as a graduation-project exercise, not confirmed production use.

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.

The same 2024 IIE CAS thesis proposes 'PED' (Packet-context based Evasion Detection): an XGBoost flow-feature pre-filter followed by a GRU-based autoencoder trained on packet-context features (inter-packet plus protocol-layer, including HTTP-layer fields) that flags DPI-evasion traffic via reconstruction error, reporting accuracy/ROC-AUC above 0.88/0.97 and generalizing to the public CICIDS and MAWI datasets; the thesis also proposes a deployable LAN-gateway evasion-detection prototype and four DPI-middlebox hardening recommendations (mimic Suricata's strict field validation, enforce RFC-compliant parsing, add ML-based anomaly detection, run regular red-team evaluations).

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.

An IIE CAS technical whitepaper for the "High-speed Network Traffic Processing and Analysis System" names the "MESA" team (处理架构组MESA团队, "Massive Effective Stream Analysis") as part of IIE's Second Research Office, lists deployment case studies at China's National Administration of State Secrets Protection (国家保密局), the National Computer Network and Information Security Management Center, Guangdong provincial e-government services, and CNCERT (国家互联网应急中心), and gives a named IIE contact (name/phone/email redacted here). The product classifies behavior inside encrypted tunnels (remote control, audio/video calls, encrypted mail, VPN) and dozens of IM/social apps via SSL/SSH/IPSEC/QUIC/SOCKS protocol detection.

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.