A MESA graduate-project spec ("网络流量随机性检测技术研究与系统实现") calls for a prototype implementing 10+ classical statistical randomness tests (frequency, approximate-entropy, runs, matrix-rank, overlapping-subsequence, autocorrelation, etc., in C, 3000+ LOC) against TCP/UDP payloads to classify traffic as "encrypted data flow" (high entropy) vs. not, trained/validated on a 50GB+ labeled corpus of actively-generated encrypted and compressed traffic.

a MESA Lab researcher's (name redacted) repo packages the NIST SP800-22 statistical randomness test suite (frequency, block-frequency, cusum, discrete Fourier transform, linear complexity, longest-run-of-ones, (non)overlapping template matchings, poker, random excursions, rank, runs, serial, universal, approximate entropy) as "MESA_sts", exposed with a documented "randomlooking check" with per-test-function enable switches (branch feature-add_switch_for_randomlooking), and tested against captured WeChat voice-call (MMTLS) and Telegram MTProto (IPv4/IPv6, multiple key-negotiation variants) traffic — i.e. a production statistical-randomness classifier for identifying fully-encrypted/obfuscated protocol traffic.

Exported AppSketch/MAAT signature-object JSON shows named per-VPN detection rules combining multiple independent vectors: CyberGhost's WireGuard variant is matched by a hex UDP-payload pattern (04000000*), destination port 1337, and ip.proto=17 together (signature cyberghostvpn_wireguard); Windscribe's OpenVPN control channel is matched by two offset-anchored hex byte patterns plus an exact 86-byte payload length (windscribevpn_openvpn_payload); and ExpressVPN is matched independently via a literal JA3 hash (ssl.analysis.ja3), FQDN strings (expressapisv2.com, www.expressvpn.works), and a large enumerated IP list (700+ individual ip.dst entries observed before the 200KB text-extraction cutoff).

An internal schema doc describes an 'Unknown Protocol Identification Database': an Elasticsearch port-asset table tracking active/passive/fused protocol-type guesses and banner text per IP:port, feeding a MySQL clustering pipeline (cluster_info/cluster_task) that groups unclassified traffic by a 'fingerprint' field into named-protocol clusters -- an unsupervised discovery pipeline for identifying and naming new/unknown protocols at scale, distinct from MAAT/AppSketch's signature-matching against already-known protocols.

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.

The "ivacyvpn_udp_payload" signature detects IvacyVPN not via domain/IP but via a byte-pattern match on the UDP payload ("421f016e1648*" in hex, wildcarded) combined with an exact first-packet data length of 42 bytes -- i.e. a protocol-level handshake fingerprint independent of the app's server infrastructure, and robust to IP/domain rotation.