DEFENSES
reality REALITY (TLS handshake forwarding)
2 papers on file
- 2026-pooribitwise-iran-advanced-dpi [Iran] Advanced DPI is reassembling TCP fragments to extract SNI on VLESS/WS + CDN
- 2025-hyperion-cs-censor-has-new Censor has a new method of blocking
10 findings tagged here
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Compared to peer protocols, AnyTLS rates 'medium' performance (vs. VLESS 'high', Hysteria2 'very high', TUIC 'high'), uses TCP/TLS transport (vs. UDP/QUIC for Hysteria2 and TUIC), and relies on padding-based obfuscation vs. REALITY/WebSocket (VLESS) or HTTP/3 framing (Hysteria2). Client ecosystem support is currently limited primarily to sing-box, vs. broad cross-client support for VLESS, Trojan, and Hysteria2.
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AEGIS, a flow-physics-only ML classifier using a Hyperbolic Liquid State Space Model evaluated on a 400GB adversarial corpus including VLESS Reality, GhostBear, and AMOI-morphed traffic, achieves F1-score 0.9952, 99.50% TPR, and 0.2141% FPR at 262.27 µs inference latency on an RTX 4090. The system discards all payload bytes and classifies traffic exclusively on 6-dimensional flow physics: packet size, inter-arrival time, directionality, TCP window size, TCP flags, and payload ratio.
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Automated proxy engines (e.g., Xray-core running VLESS Reality in automated mode) generate deterministically rigid inter-arrival time distributions because they cannot synthesize the stochastic variance of human-driven IAT, even when volumetrically anchored to benign distributions ('Fat Middle' anchoring via AMOI). The AEGIS Thermodynamic Variance Detector identifies this rigidity via Shannon Entropy of hidden states across 1,000-packet causal windows, rendering volumetric anchoring mathematically distinguishable from genuine human traffic.
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Flow-physics classifiers face a fundamental 'Human Entropy Horizon': when VLESS Reality multiplexes true human entropy (a human actively browsing web applications), AEGIS achieves a detection rate of only 1.17%, because XTLS wrappers impart near-zero mechanical overhead and the temporal physics remain entirely stochastic. This implies adversaries operating at human interaction speeds can evade flow-based detection, but must abandon automated high-throughput C2 scripts.
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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.
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Community members propose a two-hop architecture as a potential mitigation for Iran's current DPI upgrade: a first-hop server inside Iran's whitelisted datacenter ranges (where outbound traffic faces lighter inspection) proxying to a foreign exit node, with Reality masquerading as a legitimate Iranian organization's domain. The architecture's effectiveness under active TCP reassembly and handshake probing is explicitly unverified.
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The Russian DPI maintains two whitelists that exempt flows from the freeze: (1) a SNI-based whitelist covering select domains (visible in the TLS ClientHello), and (2) a CIDR-based whitelist of IP subnets for trusted destination servers. The SNI whitelist can be exploited by VLESS+Reality clients using an allowed SNI value as the apparent destination; the CIDR whitelist requires routing through an IP from a whitelisted prefix, making circumvention 'extremely difficult' without an intermediate node in a whitelisted subnet.
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LZR, built on top of ZMap, can identify 99% of unexpected Internet services in five handshakes by acting as a shim between ZMap and ZGrab. This gives censors and researchers alike an efficient active-probing primitive to fingerprint proxy protocols at scale.
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Obfuscated proxy traffic (including Shadowsocks, VMess, VLESS, Trojan, obfs4, and REALITY) can be reliably fingerprinted by detecting encapsulated TLS handshakes — the inner TLS ClientHello that appears inside an outer encrypted tunnel. This fingerprint is protocol-agnostic: any proxy that wraps TLS-bearing application traffic will produce it. The authors deployed a similarity-based classifier within a mid-size ISP serving over one million users and demonstrated detection with minimal collateral damage.
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While stream multiplexing reduces the visibility of encapsulated TLS handshakes by merging inner connections, the paper cautions that multiplexing plus random padding alone is "inherently limited" as a long-term countermeasure. Censors can adapt by monitoring burst sizes and round-trip counts at the outer-connection level, which remain correlated with the number of inner TLS sessions regardless of padding.