Censors can evade external DNS measurement systems like Satellite by injecting spoofed DNS responses only for resolvers located within the censored country, returning correct answers to external probes. This targeted injection would be 'much less visible to Satellite' while remaining fully effective against in-country users; the paper flags this as a fundamental limitation of single-vantage external measurement.

Naive interference measurement systematically misclassifies CDN geographic routing as blocking (and vice versa): when China or Russia resolves twitter.com to a non-US IP, a naive detector must decide whether that is a CDN point of presence or interference. Joint iterative analysis of DomainSimilarity and IPTrust scores is required to separate authentic CDN footprints from block-page redirections.

§1, §2.4, §2.4.1 detection

The top 10 CDNs collectively host nearly 20% of the Alexa top 10,000 domains (1,967 domains); CloudFlare alone accounts for ~10% of those sites (726 domains) and operates across 75 ASes with 107,008 IP addresses. CDN-hosted domains receive disproportionate interference relative to their 20% share, suggesting censors target popular shared-infrastructure sites as a high-leverage blocking strategy.

§4.2, Table 3, Table 4, §4.3 evaluation

Satellite detected a spike in anomalous DNS resolutions across Iranian ISPs in the second half of 2015, correlating with Iranian authorities' public statements about beginning a 'second phase of filtering,' followed by additional newly inaccessible domains in the lead-up to the February 2016 elections — demonstrating longitudinal DNS measurement can detect and time censor policy escalations.

§4.4, Figure 12 evaluation

Satellite's single-node measurement methodology, probing 1/10th of 12 million discovered open DNS resolvers across 20,000 ASes and 169 countries, detected 4,819 instances of ISP-level DNS hijacking across 117 countries while measuring 10,000 domains with weekly precision from a single external vantage point.

§1 Abstract, §2.2 evaluation

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

Brown et al. (2023) combined supervised ML models trained on expert-labeled data with unsupervised models establishing a baseline of 'normal' behavior to detect DNS-based censorship from Satellite and OONI datasets, achieving high true-positive rates for both known and new DNS censorship instances. The hybrid supervised/unsupervised approach is proposed as a template for the LLM-based system.

2024-gao-extended §2 Related Works dns-poisoningmeasurement-platformml-classifier