A 2024 IIE CAS thesis proposal (advisor name redacted, a senior-grade engineer) explicitly frames "website identification under ECH encryption" as necessary infrastructure for China's CAC/MIIT/MPS "Clean Web"/"Sword Net" enforcement campaigns, and reports concrete progress already made: a Go-based DNS active-probing framework scanning 1.7B+ domains at roughly 11,000 domains/sec to build a global ECH-deployment map (10 anonymity sets / 79 domains found as of April 2024), and an already-implemented ECH-flow filter keyed on the TLS extension-type IDs used across ECH draft versions 07-18, with planned work on cross-flow traffic correlation plus raw-byte/flow-feature fusion to identify the specific site within an ECH anonymity set.
在国家层面，国家互联网信息办公室、工业和信息化部、公安部等部门联合开展净网、剑网等专项行动打击各类恶意网站服务，网站识别是不可或缺的一环。ECH 技术从根本上改变了初始 TLS 握手的启动方式，使得用户访问网站时的 SNI 信息不可见，基于 SNI 的识别方法将彻底失效。
Defense implications
- Do not assume ECH alone hides which specific site a user visited: this research pairs full-network ECH-deployment mapping (DNS active probing over HTTPS/SVCB records) with cross-connection traffic correlation (resource/third-party loads not covered by ECH) to de-anonymize sites within small anonymity sets — real-world anonymity sets observed were tiny (10 sets / 79 domains as of April 2024), so any circumvention use of ECH needs large, diverse cover sets or must pair ECH with connection coalescing/padding that also hides associated resource-load traffic.
- The described ECH-flow filter keys on the TLS extension type number, which has changed with nearly every draft revision (7 distinct values tracked across drafts 07-18) — any fallback/compatibility logic that emits a recognizable ECH extension codepoint is itself a fingerprintable signal even before decryption is attempted.
Related findings
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 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.
A master's thesis-in-progress supervised within this ecosystem (advisor name redacted, 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.
An internal "网站/应用资源测绘" (website/app resource-mapping) presentation describes active EDNS-Client-Subnet DNS probing from many simulated geographic vantage points to map Google's and Facebook's global service-IP distribution by country/province, a mobile-app pipeline combining UI automation with MITM-proxy/ SSL-pinning-bypass to extract button-to-URL mappings from decrypted app traffic, and a "网站指纹审查" section that explicitly documents domain fronting as an effective evasion of its own DNS/SNI/Host-based website-fingerprint detection, alongside literature-based behavioral/ML website-fingerprinting intended to survive the QUIC/ECH/DoH transition.
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.