wujiating/detection (by the same MESA Lab researcher behind wujiating/censorship_detection, a translated censorship-detection literature survey) is a CICFlowMeter-based ML traffic classifier trained on the public ISCX VPN-nonVPN dataset plus custom-captured DoH and generic web pcaps, organized into explicit closed-world (CW) and open-world (OW) evaluation splits — evidence of dedicated open-world DoH-traffic classification research at MESA Lab.
data/myData/CW/doh/*.pcap_Flow.csv, data/myData/OW/doh/*.pcap_Flow.csv, data/myData/CW/web/*.pcap_Flow.csv, data/myData/OW/web/*.pcap_Flow.csv; cicflow.py, binary.py, binary_cross.py, occ.py
Defense implications
- DoH-carrying circumvention traffic should be evaluated against open-world (not just closed-world) classifier setups, since that is explicitly the evaluation methodology MESA Lab researchers use here — a defense that only beats closed-world classification may still fail in the more realistic open-world setting a deployed censor actually uses.
Related findings
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.
MESA Lab meeting notes (Nov-Dec 2021) document development and a staged production rollout of a new sapp plugin that identifies encrypted video streams via "burst" (packet-timing/size-burst) traffic features designed to generalize across varying network conditions, trained with machine learning, with a first burst-rule version scheduled for live deployment and sapp modified to log additional features for continued training.
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 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.
A thesis-project assignment for hidden-service (VPN/Proxy/Tor) identification via heterogeneous graph neural networks on flow logs sources its one day of training data from a database explicitly named 'tsg_galaxy_p19' — tying the internal TSG naming convention to the taxonomy's P19/WMS-UTR Pakistan site codename — accessed over an internal 'Information Harbor' (信息港) VPN, with ground-truth hidden-service IP labels supplied by the commercial IP-intelligence service spur.us.