Once passive analysis flags a connection, the GFW sends seven distinct active probe types in staged sequence: five replay-based (R1–R5, where R1 is an identical replay and R2–R5 alter specific byte offsets to attack stream vs. AEAD cipher variants) and two non-replay random-length probes (NR1, NR2). The system operates in stages: R3/R4/R5 probes are withheld until the server responds to R1/R2, meaning a server with replay protection (like Shadowsocks-libev ≥ v3.3.1) never receives stage-2 probes, while one without (original OutlineVPN) escalates to full probing.

The GFW's passive classifier uses two features of the first data packet to flag probable Shadowsocks traffic: (1) high Shannon entropy (per-byte entropy > ~7 bits strongly correlates with replay probability, which is nearly 4x higher at entropy 7.2 than at 3.0) and (2) packet length in the range 160–700 bytes with specific remainders mod 16. A single data packet after the TCP handshake is sufficient to trigger the downstream active-probing pipeline.

§4.2 detection

The GFW's active probers originate from thousands of distinct IP addresses, but a network-level side-channel (shared IP ID counter sequences) reveals they are controlled by a small number of centralized structures. Probe delay from legitimate connection to first active probe can be as short as 0.28 seconds, ruling out any reactive defense that relies on out-of-band blocking before probes arrive.

§3.3, §3.5 detection

The blog author, drawing on evaluation experience, concludes that LetsVPN's failure was not caused by IP exhaustion or ordinary node instability but by precise protocol-signature identification: once the GFW extracts a client handshake feature, it can simultaneously block all connections sharing that signature across hundreds of thousands of users.

2026-anon-letsvpn-vpn §公告里的几句大实话 dpitls-fingerprintactive-probing

FlowPaint evades all three GFW-style rule-based middlebox configurations — stateful TCP keyword reassembler (Censor A), heuristic header filter dropping flows with TCP data offset ≥ 9 (Censor B), and anti-replay validator caching (SEQ, payload_hash) pairs and injecting bidirectional RSTs on replay detection (Censor C). Geneva's duplicate-packet desynchronization strategy fails against Censor C because its malformed artifacts simultaneously satisfy the anti-replay blocking condition, while FlowPaint succeeds by recalculating checksums and injecting no extra packets.

2026-ling-one-prompt-censorship-evasion §5.2, Table 4 dpirst-injectionactive-probing

As of August 2026 in Fuzhou, Fujian, most censorship-resistant service providers are non-functional; a small number of providers using v2ray remain partially functional, while Tor Browser bridges and Snowflake are no longer usable.

2026-molotoves-wall-behind-wall Issue body dpiactive-probingtraffic-shape

The Reality protocol triggers immediate RST flood responses from Iranian ISP infrastructure at the completion of the TLS handshake, suggesting Iranian DPI has developed a fingerprint or active-probe response specifically targeting Reality's TLS handshake forwarding mechanism as of June 2026.

2026-pooribitwise-iran-advanced-dpi Issue body — Other Observations §2 dpirst-injectiontls-fingerprintactive-probing

Authoritarian regimes blocked Snowflake primarily through DPI targeting fingerprints in Pion's DTLS handshake and TLS fingerprints in complementary WebRTC protocols, not through ML-based traffic analysis — confirming that cost-effective censors consistently favor simple, deterministic methods over computationally expensive classifiers.

2026-vilalonga-obscura-enabling-ephemeral §1 dpitls-fingerprintactive-probing

The September 2025 leak of ~600 GB from Geedge Networks and the MESA Lab (Institute of Information Engineering, Chinese Academy of Sciences) is the largest known document disclosure from the GFW vendor ecosystem. It establishes a direct lineage: MESA Lab (founded 2012 by Fang Binxing's team, annual contracted revenue >35M RMB by 2016) spun out Geedge Networks in 2018, with MESA alumni filling key engineering roles (e.g. Zheng Chao as CTO). The leak includes ~64 GB of MESA git repositories, ~35 GB of MESA internal documents, ~15 GB of Geedge internal documents, and a ~3 GB Jira export — providing direct access to source code, work logs, and internal communications behind GFW R&D.

2025-geedge-mesa-leak §4 dpiactive-probingml-classifiersni-blockingfully-encrypted-detect