A June 2024 MESA Lab internal survey ("针对审查系统的科学研究及探测技术调研报告") explicitly states its purpose is to catalog academic/public censorship-measurement and circumvention research (OONI, Augur, Satellite, GFWatch, Citizen Lab, CensorBib, FOCI/IMC/NDSS/CCS/USENIX Security papers) in order to find and patch GFW/censorship-system vulnerabilities before outside researchers exploit them. It systematically covers circumvention protocols/tools (Shadowsocks, VMess, Trojan, decoy routing, Parrot-style mimicry, CovertCast, Slitheen++, ESNI/ECH, uTLS) and notes GFW blocked ESNI (not ECH) since July 2020, plus historical TLS-fingerprint blocking of meek by a Cyberoam firewall (2016).

A June 2024 internal MESA Team survey ('针对审查系统的科学研究及探测技术调研报告') catalogs the academic censorship-measurement toolkit (OONI, Augur, Satellite, Quack/Hyperquack, GFWatch, GFWeb, middlebox weaponization studies, traceroute-based middlebox localization, device fingerprinting) and separately reviews circumvention-tool countermeasures, explicitly naming Lantern alongside Psiphon, Tor Meek and Signal as tools using uTLS-style TLS ClientHello mimicry and domain-fronting.

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.

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.

A MESA Lab task tracker records the assignment "TSG: determine how many clients are behind an IP address," and a companion technical design document details the method: identify distinct TLS clients sharing one public IP using a <JA3 fingerprint, server domain, server IP> 3-tuple (JA3 alone collides across different apps), then use TLS Session Ticket reuse/lifetime sequences per identified client to detect multiple concurrent devices (i.e. NAT) behind that IP.

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.

TSG/CM ships with pre-built, first-class 'Learning Object' entries specifically for Freegate (Object ID 18) and Psiphon3 (Object ID 19), plus a generic 'Top Server IP' object (ID 20) -- default product features, not customer-commissioned custom signatures. The Psiphon3 object auto-learns and dynamically updates a live blocklist that reached roughly 70,000 IPs at one deployment before a database issue temporarily dropped it to ~50,000.