The bottleneck exhibits strong and consistent diurnal patterns: 80–95% of receiver–sender pairs show a standard deviation of less than 3 hours in daily slowdown duration, and the patterns persist unchanged across weekends and national holidays (May 1–2 and October 1 national day). Packet loss is strictly asymmetric — occurring only for traffic entering China (inbound data and outbound ACK packets), not for traffic leaving China.

Hop-by-hop bottleneck localization showed that in more than 71% of measured paths the first lossy hop is located deep inside China (beyond the border), with only 34.45% of bottleneck hops coinciding with the GFW hop as detected by RST injection probing — suggesting Chinese ISP infrastructure underprovisioning rather than GFW intervention as the primary cause.

§5, Figure 13 evaluation

The Great Bottleneck of China affects 79% of measured receiver–sender pairs, with more than 70% of those pairs suffering throughput below 1 Mbps for more than 5 hours every day. M-Lab NDT data independently corroborates this: in 64% of 75,464 tests from China, download speed was below 500 kbps.

§3, §4.2 Observation 1, Figure 5 evaluation

Hong Kong is the sole geographic exception: it experiences no measurable slowdowns when accessing the foreign Internet, and Chinese mainland receivers accessing Hong Kong as a sender averaged only ~3 hours of slowdown per day — compared to 5–17 hours for US, European, and most Asian senders — making it an effective high-performance relay between mainland China and the rest of the world.

§3, §4.2 Observation 1, Figure 7 evaluation

A/B testing across HTTP, HTTPS, VPN, and Shadowsocks traffic found no measurable difference in packet loss rates, ruling out censorship-targeted protocol throttling as the cause. Even at probe rates as low as one packet per 10 seconds, loss rates were similar across all protocol variants, indicating no per-connection or per-protocol speed throttling by the GFW.

§4.2 Observation 4, Experiment 3 detection

An infrastructure-level adversary must balance watermark detectability against connection stability: the paper's threat model requires a minimum shaping rate rmin to prevent Tor circuit stalls, timeouts, or circuit replacement, and notes that repeated poor-throughput events can cause the circuit to be abandoned before sufficient watermark evidence is accumulated. This detectability–stability trade-off constrains the practical attack to macroscopic (30-second) modulation periods rather than fine-grained packet-level timing manipulation.

2026-fan-activeflowmark-assessing-tor §III.A, §VII.A flow-correlationtraffic-shapethrottling

The report documents that Myanmar's military has used its TSG-based infrastructure to execute targeted throttling and selective shutdowns of specific services and platforms, not only blanket internet shutdowns. This includes selective disruption of VPNs and circumvention tools during periods of civil unrest, demonstrating that Myanmar's censors have operationalized the granular per-service traffic control capabilities documented in the Geedge/MESA leak.

2025-jfm-silk-road-surveillance §5 throttlingip-blockingtraffic-shape

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.

2025-wang-custom §4.4 throttlingtraffic-shape

Protozoa's covert channel throughput degrades gracefully under bandwidth constraints but remains usable for common applications: average throughput is 975 Kbps at 1500 Kbps cap, 460 Kbps at 750 Kbps, and 91 Kbps at 250 Kbps. Under 2% and 5% packet loss the channel sustains 1130 Kbps and 360 Kbps, respectively, while 10% loss (near WebRTC tear-down threshold) still yields 160 Kbps without breaking the connection. Traffic analysis resistance is preserved across all these conditions, with AUC peaking at 0.65.

2020-barradas-poking §6.3, Figures 9–10 throttlingtraffic-shape

Across eight combinations of traffic features (packet length, bi-gram packet length, inter-packet time, bi-gram inter-packet time) and two similarity metrics (EMD, KS), adversarial classification accuracy against DeltaShaper streams ranges from 72–90% in unperturbed conditions. Bi-gram inter-packet times with EMD achieves 88% accuracy, matching packet-length/EMD, but requires roughly 10x the computation (~64s vs ~6s). Bandwidth throttling to 300 Kbps degrades classifier accuracy from 88% to 75%, but also drops Skype frame rate from 30 to 5 FPS, creating collateral damage that limits censor deployment of throttling as a detection aid.

2017-barradas-deltashaper §6.4, §6.5, Table 3 traffic-shapeml-classifierthrottling

SSH transfers utilized only 15% of available bandwidth versus 85–89% for HTTP/HTTPS. When SSH was obfuscated by XORing payloads with a constant key (hiding the plaintext handshake), throughput dropped to near-zero during all trials. Applying the same obfuscation to HTTP transfers produced the same near-zero result, supporting the hypothesis that Iran whitelists known-approved protocols rather than blacklisting specific ones, which would preemptively block any unrecognized or randomized transport including Tor's obfsproxy.

2013-aryan-internet §4.4 traffic-shapethrottlingrandom-payload-detect