An internal research writeup ("研究点二：基于GRU神经网络的共享接入IP检测技术") builds a GRU/CNN model over sequences of TLS JA3/SNI/session-ticket and HTTP cookie fingerprints, trained on 6 days / 155GB of mirrored traffic captured from an internal gateway named "华严网关" (Huayan Gateway), to determine whether a single source IP represents one device or several devices sharing that IP (e.g. behind a NAT/proxy), reporting precision 0.844 / recall 0.874 / F1 0.859.

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.

Two IIE graduate-research repos (cuiyiming/gradproj, a 2019-2020 master's thesis project citing NDSS'17 TLS-interception-measurement and TLS-client-identification papers; daxiaoxu/xmr_bsexpr2, a 2022 project with GRU-based sequence classifiers over TCP/DNS flow JSON and deleted docs on TLS1.3 and Tencent's proprietary MMTLS protocol) document the ML feature-engineering methodology (TLS certificate length, handshake message sequences, JA3-style statistics, Markov-chain packet-size/timing models, GRU sequence models) that plausibly underlies production classifiers (e.g. stellar's later JA4/JA4S support, MESA_sts's randomness checks).

A MESA research-log entry details a NAT/shared-connection identification methodology combining TCP/IP fingerprinting (IP-ID, TTL, DF, window size, MSS, TCP-option ordering -- p0f-style), HTTP User-Agent diversity, TLS/SSL JA3 fingerprint diversity, and traffic statistical features (concurrent-TCP-connection count, idle-time jitter, upstream/downstream ratio stability, DNS query frequency) per endpoint over rolling time windows, with detection methods spanning direct UA inspection, threshold statistics, ML classifiers (random forest/SVM), and per-window entropy jumps across the fingerprint features.

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.

MESA Lab researcher notes on an encrypted-video-identification project describe adding SSL-layer information output to more precisely trace a flow's true source/identity, and considering reinforcement learning so the identification model adapts as network conditions change, working within/around sapp's plugin limitations.

Internal MESA Lab reading notes dissect the USENIX 2024 paper on fingerprinting obfuscated proxies via encapsulated TLS handshakes, highlighting its protocol-agnostic packet-size-3-gram-plus-Mahalanobis-distance-over-bursts classifier, which the paper's own mid-size-ISP deployment reliably fingerprinted across shadowsocks, vmess, trojan, and vless-family configurations at false-positive rates the notes explicitly say the annotator estimates the GFW would find operationally acceptable (<0.6%). The notes flag the technique's main gaps as: no public source code, sharply reduced true-positive rate under connection multiplexing (10-30% vs. 60-80% unmultiplexed), and no evaluation against UDP/QUIC.