A standalone DPI plugin (liuchang/pkt_seq_matcher, built against Hyperscan and a Ragel-generated state machine) computes per-flow packet-payload-length sequences and matches them against compiled patterns, explicitly excluding zero-payload packets from the length calculation -- a packet-length statistical fingerprinting approach, the kind of technique used to identify fully-encrypted/obfuscated pluggable-transport traffic by its handshake length signature rather than by content.

A MESA team ("第二研究室 Mesa团队", lead name redacted) technical overview describes the traffic-classification engine underlying TSG/sapp as performing deep field-level parsing across 29 protocols (HTTP with 32 standard + 400+ extended fields; SMTP/POP3/IMAP/DNS/FTP/RTSP/RTMP/HLS/DASH/ SSL/SSH/OpenVPN/BT etc.), and identifies VPN traffic specifically via a two-stage approach -- coarse protocol-signature classification followed by fine-grained statistical/behavioral-feature classification for traffic that can't be matched by signature alone.

An internal presentation by MESA Lab's "4th Research Office, Processing Architecture Group" reviews the SIGCOMM'23 GGFAST paper's packet-length "snippet" technique for automatically generating encrypted-traffic classifiers, reporting it reaches 97.3% accuracy identifying DoH traffic from as few as 8 length/direction snippets and near-expert F1 scores (0.99+) fingerprinting SSH/RDP authentication methods purely from encrypted flow length patterns — direct evidence the lab evaluates automated length-pattern classifiers for possible operational adoption.

A patent draft (applicant not present in the extracted text, but topically and methodologically identical to MESA Lab's SAPP audio/video-identification plugin work in the same corpus) describes a content-level encrypted-video identification system: an SNI-triggered flow classifier groups downlink ACK-aligned packets into 'chunks', converts the chunk-size sequence into a long/short word-frequency signature, and matches it in O(1) against a fingerprint database built by actively crawling target sites through a MITM proxy. Tested on 1,000 YouTube videos captured from 5 locations, it reaches 96.19% accuracy needing only 6 online chunks at 3.33us per match, 90x+ faster than three prior published methods it benchmarks against.

A ClickHouse SQL query against table tsg_galaxy_v3.session_record_local implements a heuristic for flagging likely proxy/circumvention-tool usage: within 5-second windows, it flags any client IP that connects to 30+ distinct server IPs on high ports (>=10000) with small, tightly-bounded packet counts (6-15 sent, 4-15 received) where the session's application-layer protocol is unclassified (app_transition = 'unknown'). "tsg_galaxy_v3" indicates a named TSG data-warehouse/analytics layer distinct from the sapp/maat real-time inspection path.

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 dedicated internal "运营商前端分析团队" (Carrier Front-end Analysis Team) produces weekly, carrier-specific traffic-analysis reports explicitly titled "新疆移动流量分析报告" (Xinjiang Mobile Traffic Analysis Report) and "新疆联通流量分析报告" (Xinjiang Unicom Traffic Analysis Report), with instances dated from at least July 2022 through March 2024. One instance (Xinjiang Mobile, 2023-05-15) reports total carrier bandwidth of 4.4Tbps (1Tbps access bandwidth), 27.34PB of weekly throughput, 123 distinct identified application-layer protocols, and a section (4.1, pages 17+) explicitly tracking server IP/location for 28 named overseas apps (BBC, Discord, ESPN, Facebook, Gmail, Google, Hulu, Instagram, Netflix, Reddit, Skype, Telegram, Twitter, Uber, WhatsApp, Wikipedia, YouTube, Zoom, etc.).