OONI's traffic manipulation test suite uses bidirectional traceroute comparison: asymmetry between inbound and outbound paths for specific source/destination port pairs is treated as an indicator that traffic is being diverted to an interception device. Additional per-flow indicators include timing differences in packets directed at specific ports and layer-7 header field manipulation detectable at the receiving endpoint.

OONI pairs client-submitted test reports with data independently collected at the OONIB backend TestHelper, providing both connection endpoints' viewpoints in a single unified report. The backend is designed to be run by anyone and exposed both over HTTPS and as Tor Hidden Services to resist simplistic denial-of-service and reduce fingerprint-ability of the reporting infrastructure.

§5 Architecture evaluation

OONI's experiment-control methodology explicitly favors false positives over false negatives: it is preferable to generate more censorship candidate events for further investigation than to miss genuine interference. Mismatch between experiment and control data is not always a definitive signal of manipulation but is treated as sufficient cause for flagging, and data collection and analysis are treated as distinct phases.

§4 Methodology evaluation

OONI observes that many interception devices deployed in the wild advertise their vendor and model information, making passive device identification feasible from probe-level observations alone. The framework is designed to locate interception devices and then apply probing techniques to fingerprint the specific vendor and product in use.

§2 Goals detection

OONI's threat model assumes an adversary capable of country-wide traffic manipulation who may actively fingerprint and identify measurement probes. Prior measurement tools (e.g., ONI's rTurtle) used easily fingerprinted centralized DNS and HTTPS traffic, which the authors flag as a pattern to avoid. The authors acknowledge that anti-fingerprinting measures will likely reduce measurement accuracy — a trade-off unresolved at publication.

§3 Threat Model; §10 Limits and future work detection

The GFI's HTTP and HTTPS filters are now stateful (requiring initial SYN packet with matching sequence numbers) and have been activated on all TCP ports—not only standard ports 80 and 443 as reported by prior studies. This is a significant departure from previous work that found stateless HTTP/HTTPS blocking limited to standard ports. The HTTP filter injects a 403 Forbidden blockpage (not RST packets as used by the GFW), while HTTPS injects a single RST+ACK packet. The GFI also exhibits TCP non-compliance (not requiring a full three-way handshake to trigger filtering), enabling outside-in measurement without in-country servers.

2025-tai-irblock §2.1, §3.2 dpirst-injectionpacket-injectionmiddlebox-interference

Censoring middleboxes respond to non-compliant TCP sequences because they must handle asymmetric routing and cannot rely on observing both sides of a connection. The hSYN; PSH+ACKi sequence elicited responses from 69.6% of 184 tested censoring middleboxes with a maximum amplification of 7,455×, while a lone PSH+ACK with no prior handshake elicited responses from 33.2% of middleboxes.

2021-bock-weaponizing §2, §3.3 middlebox-interferencedpipacket-injection

Marionette is the first programmable obfuscation system to simultaneously satisfy all five threat-model dimensions evaluated in Figure 2: resistance to blacklist DPI, whitelist DPI, statistical-test DPI, protocol-enforcing proxy traversal, and multi-layer traffic control, while sustaining throughput above 1 Mbps (up to 6.7 Mbps). Every prior system (obfs4, ScrambleSuit, SkypeMorph, StegoTorus, FTE, JumpBox, etc.) fails at least one dimension, most commonly stateful proxy traversal or statistical-feature control.

2015-dyer-marionette §2, Figure 2 dpitraffic-shapemiddlebox-interferenceactive-probing

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

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.

2026-almutairi-server §2.4, §4 traffic-shapedpisni-blocking

Simulations extending the ENEM19 game-theory framework show that ephemeral proxy schemes (modeled on Snowflake/Lantern) effectively neutralize both the "optimal" and "aggressive" censors from the original framework. In overprovisioned settings (proxies arriving at 250/step vs. 200 clients/step), even the null censor scenario outperforms either censor in equal-arrival settings. Over 90% of waiting users receive a proxy within 1 time step. The critical variable is not censor sophistication but proxy arrival rate relative to client demand—high proxy churn combined with high arrival rate defeats both enumeration strategies tested.

2026-fares-game §4, Fig 1, Fig 2, Table 1 dpitraffic-shape