Quack (which probes censorship on port 7/echo servers) detected substantially less blocking than Satellite (DNS-based): approximately 50% of Quack vantage points observed no blocking and ~90% observed only minor blocking, whereas Satellite observed major interference at most vantage points; the authors attribute this gap to Russian ISPs applying filtering predominantly on ports 80 and 443, leaving non-standard ports largely unfiltered.

Of 44,797 CDN-served domains on the April 2019 Roskomnadzor blocklist, 99.6% (44,615) were hosted on Cloudflare—attributable to Cloudflare's free tier with minimal vetting enabling rapid mirror-domain creation by blocked operators; the blocklist also contained 1,769 responsive circumvention-related domains, confirming that circumvention infrastructure is an active and documented blocklist target.

§V-B, Table III–IV deployment

Seven years of Roskomnadzor blocklist history (Nov 2012–April 2019) show the list grew to 132,798 unique domains and 324,695 unique IPs, with a dramatic spike in 2018 when Russia blocked Telegram by adding subnets covering approximately 16 million IP addresses—producing major collateral damage to co-hosted Google and Amazon services and illustrating that subnet-level blocking is the blunt instrument of last resort for CDN-hosted targets.

§VI-A, §II-B evaluation

Data center VPSes predominantly experienced TCP connection timeouts and resets—with the highest-blocking VPS censoring 96.8% of tested domains—while residential ISPs were substantially more likely to inject explicit blockpages citing Roskomnadzor's registry, confirming that blocking mechanism varies significantly by network tier even when blocking rates are similar.

§VI-B evaluation

Despite Russia's decentralized ISP ecosystem, 9 of 14 residential probes observed more than 90% of 98,098 tested blocklist domains blocked, and all 14 probes observed at least 49% blocked—demonstrating that coordinated nationwide censorship without centralized choke-points is achievable through legal mandates and commodity equipment alone.

§VI-B evaluation

The June 2025 Iran shutdown—carried out during the Iran-Israel war beginning ~June 19—did not use BGP route withdrawals as in 2019. Instead, authorities applied service-level restrictions at the national border: DNS poisoning of foreign destinations, protocol whitelisting permitting only pre-approved domestic services, and DPI to block circumvention-tool traffic. Iran's international traffic fell roughly 90% while the country's BGP routes remained advertised, making the shutdown invisible to BGP-based monitoring systems. OONI measurement volume, which totalled 121,333 in June 2025, collapsed to under 200 submissions on June 19-20.

2025-iran-shutdown-measurement Executive Summary / §2 dpidns-poisoningport-blockingip-blocking

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

Majority-vote ML inference (OCSVM + IF) over OONI data uncovered at least 5 previously undocumented DNS injection IPs active in Russia (e.g., 195.19.90.226, 95.167.13.51, 61.95.167.13.50, 188.19.132.154, 144.85.142.29.248) absent from OONI's existing blocking-fingerprints database, along with novel fingerprints in Italy, Czech Republic, and the UK. Records with fewer than 50 instances were excluded as a conservative false-positive filter.

2024-calle-toward §4.3, Table 5 dns-poisoningml-classifiermeasurement-platform

XGBoost trained on a single month of OONI data achieves near-optimal performance; expanding the training window to 24 months produces deviations of only 0–5 percentage points for FNR, 0.07 PP for FPR, and 0.10 PP for accuracy — suggesting that larger windows introduce noise and overfitting rather than improving detection. Isolation Forest performance degrades more sharply, with accuracy dropping ~5 PP as training data grows beyond 6 months.

2024-calle-toward §4.2, Figure 3 dns-poisoningml-classifiermeasurement-platform

For the Isolation Forest model, resolver ASN (SHAP importance 0.237) and probe ASN (0.220) are the two most predictive features for DNS tampering, reflecting that censorship is topologically concentrated at specific network vantage points. For XGBoost, headers_match dominates (0.317), followed by asn_control_match (0.177), indicating that supervised models rely more on cross-layer consistency signals. DNS tampering represents only 0.5–0.8% of all OONI measurements across 2022–2023 (Figure 2), creating severe class imbalance in any training set.

2024-calle-toward §4.1, Table 4, Figure 2 dns-poisoningml-classifiermeasurement-platform

XGBoost achieves a False Positive Rate of 0.0005, True Positive Rate of 0.9403, and overall accuracy of 0.9991 on OONI global DNS measurement data (2.5% stratified sample), vastly outperforming unsupervised alternatives: Isolation Forest achieves FPR 0.1321 / ACC 0.8699, and One-Class SVM degrades to FPR 0.9711 / ACC 0.0598, making OCSVM effectively unusable for this task.

2024-calle-toward §4.1, Table 3 dns-poisoningml-classifiermeasurement-platform