A censor hosting Skype supernodes can perform passive traffic-flow analysis on relayed streams even without breaking encryption, since supernode-relayed conversations expose traffic metadata. However, with thousands of supernodes in the Skype network, the probability that any censor-controlled supernode relays a specific SkyF2F tunnel is low, making large-scale correlation high-cost.

Website fingerprinting attacks that match file sizes and access patterns against a database of known sites remain applicable to SkyF2F, but are limited to the granularity of 512-byte fixed-size stream cells, since streams are multiplexed within a single tunnel circuit. The authors note this is less effective than against SafeWeb, where full request/response sizes are directly observable.

§V.A detection

Because Skype relies on a central login server, it is technically possible for a censor to block Skype, but the paper observes that blocking widely-deployed services like Skype or Google inflicts real economic harm, making it a credible deterrent. Additionally, Skype's proprietary, closed-source protocol and P2P architecture make it harder to characterize and selectively filter than open protocols.

§V.C defense

SkyF2F's friend-to-friend service model, where a server publishes its appid only to trusted contacts rather than publicly, provides significant resistance to both sybil attacks (malicious censor-controlled servers) and DoS exhaustion attacks. A censor posing as a client can establish many tunnels to exhaust a public server's resources; restricting service to a trusted friend list eliminates most of this attack surface.

§V.A defense

SkyF2F tunnels censored traffic through Skype's encrypted overlay network, forcing the censor into an all-or-nothing dilemma: blocking SkyF2F requires blocking Skype entirely, which causes actual economic damage to businesses and users who depend on it. Because Skype users are identified by pseudonym and all messages are routed to overlay addresses rather than Internet addresses, IP-based blocking, DNS filtering, port blocking, and keyword filtering are all rendered ineffective.

§V.A defense

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).

2026-almutairi-server §3.4, Table 1 traffic-shapedpiflow-correlation

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

Active bandwidth perturbation has an inherent detectability–stability trade-off: overly aggressive low-rate phases cause Tor SENDME-based flow control stalls, retransmissions, timeouts, or circuit replacement before sufficient correlation evidence is collected. The paper selects a 30-second modulation period and an empirically determined minimum shaping rate; the usable shaping range varies with relay load, path length, TCP congestion control behavior, and Tor multiplexing.

2026-fan-activeflowmark-assessing-tor §VII-A flow-correlationtraffic-shape

BM-Net achieves a 97.5% macro-F1 score for fine-grained classification of three modulation geometries (sinusoidal, square-wave, triangular) from noisy exit-side Tor observations using only 201 labeled test samples collected across cross-continental Tor paths. Residual errors concentrate between natural traffic and square-wave modulation, as abrupt low-rate transitions are partially smoothed by Tor multiplexing and network jitter.

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