A 2020 IIE CAS master's thesis (author and advisor names redacted) develops ML methods to identify audio/video traffic within encrypted flows without payload inspection, by quantifying on/off transmission burst patterns and binning long packet-length/timing sequences into short fixed-length feature vectors usable from as little as 5 seconds of traffic, plus a lightweight heuristic-threshold variant with a congestion-correction feature scheme built into a deployed identification system.

A 2024 IIE/CAS PhD opening-report (author and advisor names redacted) lays out a four-part research plan for identifying specific encrypted video content transmitted over QUIC: (1) recovering per-video traffic fingerprints from downlink packet aggregation despite QUIC's full encryption, using linear-fit correction to reconstruct true video-chunk sizes; (2) matching randomized multi-segment video combinations via a random-walk segment model plus a traffic 'language model'; (3) a Siamese attention-CNN with contrastive learning to fingerprint-match mixed audio/video segment streams; and (4) a large-model-based classifier to keep fingerprint matching robust under network degradation and varied playback modes.

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 June 2022 IIE CAS master's thesis, 'Research on Key Technologies of DoH Traffic Identification' (author name redacted, supervised by Professor Fang Binxing -- publicly known as chief architect of the Great Firewall and later Geedge Networks' founder/chief scientist), develops: (1) a light-load connection-multiplexing active scanner to discover DoH resolvers at scale, (2) a one-class ML classifier trained only on DoH samples plus TLS statistical features to catch DoH traffic missed by rule matching, and (3) a DoH fingerprinting method that strips HTTP/2 control-frame noise -- explicitly aimed at 'identifying users visiting websites from passive DoH traffic,' i.e. website fingerprinting through encrypted-DNS-wrapped connections.

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 IIE/CAS master's thesis (author and advisor names redacted) develops a black-box adversarial-example attack against ML-based malicious-encrypted-traffic classifiers: transferable feature-space perturbations evade LSTM and FS-Net classifiers over 96.2% of the time (over 50.5% against SVM/LR/DNN), and a packet-length-and-timing perturbation algorithm that translates those perturbations into real traffic achieves an 82.1% evasion rate, 29.9 points higher than the prior state-of-the-art black-box method.