A MESA Lab git repo (wangmeiqi/obfs4_meek_snowflake) trains closed-world Deep Fingerprinting (DF) website-fingerprinting classifiers (ClosedWorld_DF_NoDef.py) using pre-trained Keras models specifically for Tor's obfs4 pluggable transport, meek domain-fronting, and Snowflake -- the same transport families Lantern's own circumvention stack draws on.
File tree: ClosedWorld_DF_NoDef.py, models/meek_bk.h5, models/obfs4_bk.h5, models/snow_bk.h5, test_meek.py, test_obfs4.py, test_snow.py, 模型情况.txt (model_status.txt)
Defense implications
- Treat obfs4/meek/Snowflake as traffic patterns actively studied with closed-world DF classifiers by GFW-adjacent researchers; padding/shaping defenses known in the literature to reduce DF accuracy should be prioritized for any Lantern transport sharing these transports' packet-timing signatures.
- The repo name explicitly says 'NoDef' (no defense) -- worth searching the corpus for a follow-on MESA Lab repo testing WTF-PAD/FRONT-style defenses before assuming current DF accuracy against these transports in production.
Related findings
A master's thesis-in-progress supervised within this ecosystem (advisor 杨嵘, senior engineer) is explicitly tied to the national key R&D program "海量公害 网页、图片、视频流量识别技术" (massive nuisance webpage/image/video traffic identification technology) and a VoIP engineering project; it builds cross- platform (YouTube/Facebook/Bilibili) encrypted-video identification that matches a single per-video traffic fingerprint across different CDNs/resolutions/codecs via trend-similarity, fuzzy-search, and meta-learning matching, explicitly framed around detecting "非法视频" (illegal/harmful video) spread across platforms.
A CAS-institute thesis ("面向加密场景的视频入口发现关键技术研究") builds a system that associates known encrypted video-resource traffic back to its hosting webpage/app "entry" domain even when the video itself is served from generic CDN infrastructure, using co-occurrence/sequence mining plus active-learning ranking (XGBoost) partly trained on plaintext-flow ground truth; the author's prior project record lists a "某音视频节目库项目" funded under the national "242" information-security special program (2019–2020), and the thesis is explicitly framed as enabling detection of "有害视频" (harmful video) sources.
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.
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 2023 master's thesis from the Institute of Information Engineering, Chinese Academy of Sciences (MESA Lab's parent institute) develops an online encrypted-video-stream title identification method using response-time distribution and chunk-fingerprint matching that survives CDN node switching, reporting 96.19% title-identification accuracy using only 6 observed video chunks, without decrypting traffic.