A three-stage detection pipeline exploiting the "dual-role" behavioral fingerprint of single-IP circumvention relays achieved 23.2% recall (96/414 ground-truth relays) with a 0.18% false-positive rate against 97,651 benign TLS servers, for an overall accuracy of 99.5%. The ground-truth set covered OpenVPN, WireGuard, and SOCKS relays identified in a 17 TB single-day backbone trace (WIDE Project, April 9, 2025).

The paper identifies a fundamental architectural vulnerability in single-IP circumvention designs: a relay must generate new observable flows (via DNS or TLS SNI) to reach end services after receiving client connections, creating a detectable server-and-client behavioral contrast. A relay accessing user-facing domains (news, social media) scores high on a Relay Suspicion Score (w=0.9) versus infrastructure domains (w=0.1). The paper argues this host-level signal is censorship-invariant and cannot be concealed by link obfuscation.

§2.4, §4 detection

Stage 1 of the detection pipeline uses a lightweight heuristic: restrict analysis to IP addresses in "VPS-dense ASNs," which censors already target for resource-intensive inspection of fully-encrypted traffic. This pre-filter dramatically reduces the search space before applying the more expensive dual-role behavioral analysis. The evaluation was conducted without Stages 1 and 3 due to dataset limitations, meaning the reported 23% recall and 0.18% FPR are conservative lower bounds on the full pipeline's performance.

§2.1, §2.2, §3.4 detection

The original Slitheen appended covert upstream data directly to overt HTTP requests, significantly changing upstream traffic patterns and enabling censor identification even when traffic is encrypted. This upstream traffic analysis vulnerability—absent from Slitheen's original threat model—is the primary weakness Slitheen++ addresses.

2020-birtel-slitheen §1, §4 traffic-shapeflow-correlationdpi

AnyTLS's default padding scheme operates across 8 levels (stop=8), with initial padding fixed at 30 bytes, small-data padding 100–400 bytes, and medium-to-large data padding chains of 400–500 bytes continuing through multiple 500–1000 byte segments. The 'c' (continue) marker allows multi-stage padding sequences within a single connection burst.

2026-anon-anytls-anytls-sing-box-2026 §2.3 traffic-shapedpi

AnyTLS is a TLS-based proxy protocol maintained by the sing-box team, designed in 2024 and first released in the sing-box dev-next branch. Its core mechanism wraps arbitrary proxy traffic in standard TLS and applies a configurable padding scheme (Padding Scheme) to enhance traffic concealment while maintaining compatibility with standard TLS infrastructure.

2026-anon-anytls-anytls-sing-box-2026 §1.1–1.3 tls-fingerprinttraffic-shapedpi

NATA (Non-invasive Active Traffic-correlation Analysis) injects low-frequency bandwidth waveforms (sinusoidal, square-wave, triangular) into Tor TCP connections at an upstream gateway without endpoint compromise, payload decryption, or Tor-browser modification. BM-Net, a selective state-space classifier trained on the exit-side observations, achieves a 99.65% binary detection F1 score distinguishing watermarked from natural traffic on a 20,000-trace real-world dataset.

2026-fan-activeflowmark-assessing-tor §IV, §VI.D, Table III flow-correlationtraffic-shapeml-classifier

BM-Net achieves a 99.65% binary detection F1 score for distinguishing bandwidth-watermarked Tor flows from natural traffic, outperforming all evaluated baselines (next best: TikTok at 75.96% F1). The accuracy gap stems from active perturbation imposing a deterministic low-frequency throughput constraint rather than relying on subtle natural metadata, making the detection task fundamentally easier than passive website fingerprinting.

2026-fan-activeflowmark-assessing-tor §VI-D, Table III–IV flow-correlationtraffic-shapeml-classifier

BM-Net achieves a 99.65% binary detection F1 score distinguishing watermarked from natural Tor flows, and a 97.5% macro-F1 score for fine-grained modulation classification across sinusoidal, square-wave, and triangular patterns. The fine-grained test set contains 201 held-out samples collected from ten clients across five geographic regions (Europe, North America, Australia, Southeast Asia, East Asia), with training traces including traffic collected under WTF-PAD and Walkie-Talkie defenses.

2026-fan-activeflowmark-assessing-tor §VI-D, Table III, Table V flow-correlationml-classifiertraffic-shape