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.

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 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

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.

§5.5 Traffic Manipulation detection

Extending Geneva's genetic algorithm to the application layer automatically discovered 77 unique HTTP evasion strategies and 9 DNS evasion strategies against censors in China, India, and Kazakhstan — all requiring only unprivileged usermode modifications with no TCP/IP header access. Against India's Airtel censor, 56 of the 77 strategies succeeded; 29 worked against Kazakhstan; 22 evaded China's keyword-based HTTP censorship and 27 evaded its Host-header censorship.

2022-harrity-get §5.1, §6 dpikeyword-filteringrst-injectiondns-poisoning

Table 1 of the survey documents that by 2013–2014 censors were deploying simultaneous blocking across BGP, DNS, IP/port filtering, TCP disruption, TLS, and application-layer keyword filtering. No single detection tool in the survey covers all six layers; the most comprehensive, OONI (2012), covers DNS, IP/port, TCP, TLS, keyword, and HTTP but notes only partial BGP coverage.

2015-aceto-internet Table 1 bgp-hijackdns-poisoningport-blockingip-blockingrst-injectiontls-fingerprintkeyword-filteringdpi

Of 6,915,266 domains extracted from the 572 GiB Geedge Networks Leak (GNL), 298,955 censored domains (93.7% of all GNL-censored domains) appear in neither Tranco top-1M nor CitizenLab test lists. Measurements across China (Guangzhou/Nanjing), Myanmar, Pakistan, and Algeria confirmed censorship via DNS injection and SNI-based TLS connection termination. The GNL covers 25–62% of Tranco-censored domains across countries, showing substantial but incomplete overlap. This vendor-side ground truth reveals a censorship surface roughly two orders of magnitude larger than curated academic test lists.

2026-sheffey-geedge §4.1, Table 2 dpisni-blockingdns-poisoningmeasurement-platform

TTL-based path analysis showed that all censorship actions (DNS poisoning, HTTP injection, TLS resets) in the June 2025 shutdown occurred at the same network hop across all tested ISPs, indicating a single centralized national border gateway—likely TCI AS gateways—rather than per-ISP enforcement. Global BGP announcements were kept intact throughout, making the shutdown invisible to routing monitors while domestic connectivity collapsed.

2025-aryapour-stealth-blackout §4.5 dpidns-poisoningrst-injection

TLS connections to blocked services (instagram.com, telegram.org) were terminated by TCP RST immediately after the client's ClientHello, before any certificate exchange, confirming SNI-based DPI that reads the plaintext SNI extension and aborts the handshake. HTTP filtering additionally matched Host headers and URL keywords case-sensitively, with injected HTTP 403 pages or TCP RST responses, and case-change evasions were sometimes effective.

2025-aryapour-stealth-blackout §4.2, §4.3 sni-blockingdpirst-injectionkeyword-filtering

Iran's HTTP censor exhibits several parsing inconsistencies exploitable for evasion: (1) it is case-sensitive and ignores lowercase method variant "gET"; (2) it does not censor the Host header for HTTP version strings "HTTP", "1.1", and "example" (suggests a version regex of HTTP/.*); (3) when the Host header is absent, the path is not censored for versions "HTTP" and "HTTP/1"; (4) the body is never analyzed regardless of version. All HTTP and DNS censorship occurs at the same last-hop border node, suggesting centralized architecture.

2025-lange-i-ra-nconsistencies §3.2, Table 1 dpikeyword-filteringrst-injection