Is Custom Congestion Control a Bad Idea for Circumvention Tools?

Active mid-connection bandwidth throttling (e.g., 100 Mbps → 50 Mbps) cleanly separates BBR from Hysteria and TCP-Brutal: BBR converges to the new rate within a few probing cycles, while Hysteria and Brutal interpret reduced bandwidth as increased packet loss and raise their sending rate further. This active probing technique resolves the BBR ambiguity that passive measurement alone cannot.

§4.4 detection throttlingtraffic-shape cnirru

BBR, a rate-based CCA already available in the Linux kernel, comes close to Hysteria's throughput performance when packet loss is below 20% — the typical range for cross-border Chinese links (5–15%, peak up to 50% per prior studies). Above 20% loss, Hysteria and Brutal maintain a significant throughput advantage over BBR, but the paper finds no compelling justification for custom CCAs given the marginal gains in that regime versus the fingerprinting cost.

§4.2, §6 defense traffic-shape cn

Custom CCAs that deviate from standard TCP/QUIC congestion response fundamentally contradict the core circumvention principle of traffic indistinguishability: by failing to back off under congestion signals, they produce traffic patterns that diverge from the vast majority of Internet flows that censors value, eliminating the collateral-damage protection that makes circumvention tools hard to block wholesale.

§1, §6 detection traffic-shapefully-encrypted-detect generic

Hysteria and TCP-Brutal maintain fixed sending rates regardless of packet loss, causing them to transmit at rates several orders of magnitude higher than loss-based CCAs (TCP/QUIC Cubic) at a 5% packet loss rate on a 100 Mbps link with 60ms RTT. This non-compliance with standard congestion backoff is reliably detectable across RTTs from 15ms to 300ms and loss rates from 0.1% to 20%.

§4.1 detection traffic-shapeml-classifier cn

A two-stage threshold classifier evaluated on 10,080 synthetic flows across 1,260 network condition combinations (20 RTTs × 21 loss rates × 3 bandwidths) achieved 100% accuracy in Stage 1 separating loss-based from non-loss-based CCAs, and produced only 16 false positives from BBR flows in Stage 2, correctly flagging all 1,257 Hysteria and 1,257 Brutal flows as custom CCAs.

§5, Table 1 evaluation ml-classifiertraffic-shape generic