CenDTect uses cross-classification accuracy — how well a decision tree trained on one domain's blocking pattern predicts another domain's blocking — as a distance metric to cluster domains that share the same blocking policy. This metric outperforms prior time-series approaches because it is interpretable (the resulting decision tree directly reveals the blocking mechanism: which ISP, which port, which protocol) rather than producing opaque anomaly scores. The approach scales to planetary-measurement volumes without requiring labelled training data.

CenDTect, an unsupervised decision-tree system using iterative parallel DBSCAN, analyzed more than 70 billion Censored Planet data points (January 2019 – December 2022) and discovered 15,360 HTTP(S) censorship event clusters across 192 countries and 1,166 DNS event clusters across 77 countries. Manual validation against 38 known censorship events from news reports confirmed all human-identified events were recoverable from CenDTect's output. The system additionally identified more than 100 ASes in 32 countries with persistent ISP-level blocking and 11 temporary blocking events in 2022 correlated with elections, protests, and armed conflict.

Abstract, §5, §6 evaluation

The paper proposes a black-box methodology for detecting censorship bias in LLMs by comparing responses to identical prompts in Simplified vs. Traditional Chinese — scripts for the same spoken language — controlling for translation quality while exploiting that Simplified Chinese training data is disproportionately sourced from mainland China's censored internet. Each prompt is repeated ten times and scored for similarity to censored text using an XLM-RoBERTa classifier fine-tuned on Baidu Baike (censored) vs. Chinese Wikipedia (uncensored) with scores from 0 to 1.

2024-ahmed-extended §2 / §2.4 keyword-filteringml-classifiermeasurement-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

CenDTect (Tsai et al., NDSS 2024) uses decision trees and a novel clustering method on Censored Planet plus OONI data to identify blocking policies and provide interpretable insights at local and country levels. A separate approach (Duncan & Chen, 2023) applies sequence-to-sequence models and CNN image classification — treating network reachability data as grayscale images — to distinguish censored from uncensored content.

2024-gao-extended §2 Related Works measurement-platformml-classifier