DNS censorship complexity varies sharply by country: Iran injects static forged IPs exclusively from 10.0.0.0/8 and Turkmenistan uses only 127.0.0.1, making detection trivial, while China's constant fake-IP churn across ASes demands dynamic ML approaches; models trained without country-specific ASN features still perform well, enabling transfer to countries where GFWatch-equivalent infrastructure does not exist.

By mapping ML-predicted censored probes back to their DNS response IPs, the authors discovered 748 forged IP addresses used by China's GFW as DNS blocking signatures that OONI's heuristics missed; supervised and unsupervised models also identified several ISP-specific injected IPs absent from even GFWatch's comprehensive signature list, demonstrating that static signature lists substantially undercount active GFW DNS censorship.

§4.2, Table 4 evaluation

OONI and Satellite (Censored Planet) agree on roughly 75% of tested Chinese domains as uncensored, but DNS anomaly agreement is poor: each platform flags fewer than 0.5% of domains as anomalous in any given biweekly window, and the two platforms frequently disagree on which domains are censored because China's GFW uses dynamic fake-IP injection that defeats static rule-based heuristics.

§2.2, Figure 1 evaluation

XGBoost supervised models trained on DNS probe features achieve TPRs of 100% (Satellite) and 99.8% (OONI) at FPRs of 0.0% and 0.2% respectively when using platform-native anomaly labels; cross-source training with GFWatch labels applied to the same records yields 99.4% TPR for Satellite and 86.7% TPR for OONI, with SHAP analysis confirming that ASN and organization name of the returned DNS response IPs are the dominant predictive signal.

§4.1, Table 2 evaluation

Unsupervised one-class SVM models trained only on clean (uncensored) records detect GFW DNS censorship with 99.1% TPR at 17.4% FPR on Satellite data; over half of apparent false negatives are truly uncensored probes where the GFW transiently failed to inject a forged response, confirming that GFW DNS injection is not perfectly consistent at the individual probe level.

§4.1, Table 3 evaluation

Internal Geedge documents confirm active contracts to deploy GFW-derived censorship and surveillance infrastructure in Myanmar, Pakistan, Ethiopia, Kazakhstan, and at least one additional unidentified country under the Belt and Road framework, in addition to domestic deployments in Xinjiang, Jiangsu, and Fujian. The exported product (the Tiangou Secure Gateway / TSG line) is not a stripped-down export variant — leaked TSG documentation shows DPI, active-probing, ML classifiers, and granular per-region traffic control rules that mirror the domestic GFW capability set.

2025-geedge-mesa-leak §5, §6 dpiactive-probingml-classifiersni-blockingdns-poisoningfully-encrypted-detecttraffic-shape

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