An internal talk deconstructing MESA Lab's own WWW2024 paper "Identifying VPN Servers through Graph-Represented Behaviors" (VPNTracker) confirms it was built on the group's own internal production log data ("组内数据"), and details its core active-probing feature: "Stealth Ports" (TCP ports observed open in passive traffic that refuse/ignore active probes) combined with vendor-specific patterns in the error responses VPN servers return to out-of-protocol active probes (e.g. a DNS query sent to the port), fed into a graph neural network over client-IP/server-IP/port relationships; framed explicitly as anti-geo-fraud tooling against services like ChatGPT, Netflix, and NetEase Cloud Music.

A 2024 CAS/IIE master's mid-term thesis report, "面向DPI中间件的探测行为识别关键技术研究" (Key Technologies for Identifying Probing Behavior Targeting DPI Middleboxes), builds and evaluates a system that detects and fingerprints active-measurement traffic from OONI, Censored Planet, and GFWatch/GFWeb (via their server-contact patterns, distinct-domain-count thresholds, and response-timing signatures — e.g. flagging a probing srcIP once it queries ≥500-2000 distinct domains at one dstIP), and separately trains a graph-neural-network classifier on raw packet bytes to detect Geneva/SymTCP-style automated censorship-evasion probes, explicitly framed as reducing the DPI middlebox's exposure of its own characteristics to circumvention researchers.

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.

Two versions of an academic active-probing VPN-server-detection paper ("VPNChecker"/"VPNSniffer", WWW'24) held in this corpus specifically profile Psiphon3: Psiphon3 servers account for the single largest share (6.64%) of labeled VPN servers in the authors' ISP dataset, most respond to an OpenVPN probe by silently timing out rather than returning the standard OpenVPN response (a "probe-resistant" behavior the paper explicitly attributes to Psiphon3), and Psiphon3 servers share a small number of characteristic "Probing Port Combinations" (e.g. {443,53,22}, {443,554,22}) across the vendor's fleet that the paper's graph-based classifier uses to link servers together.

An internal TSG functional-requirements spec ('加密协议JG') defines device support for identifying and blocking ECH, ESNI and QUIC traffic via per-connection SNI/region-matching tables (e.g. DF_QUIC_REGION), plus a companion 'FD报文全流程感知' feature that both passively monitors and actively injects synthetic verification traffic end-to-end through the network path to compute a live per-rule 'CT率' (breakthrough/penetration rate) — the system self-measures how often its own QUIC/ESNI/ECH blocks fail to take effect, and separately throttles logging/blocking detail for rule IDs receiving unusually high hit counts (DF_ATTACK_PROTECTION, 'targeted attack detection').

A MESA Lab monthly report states that adjustments to a VPN-detection module accumulated over 10,000 Psiphon3 server IPs and delayed a Psiphon3 client's ability to get online by at least 3 minutes in the researchers' test environment, an earlier-stage data point consistent with the much larger-scale (~70-73K IP) 'vpn-thwarting'/CyberNarrator Psiphon-harvesting pipeline documented elsewhere in this corpus.

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.