6 dated technical articles. These pages record the methods and claims as published; follow each article's source links and update notes for its current scope.
How the Federal Regulatory Data Hub ingests the OFAC Specially Designated Nationals list — daily conditional GET with ETag, XML parsing across 12K SDN entries with alias explosion, name normalization pipeline, FTS5 + Jaro-Winkler three-pass screening, and p50 8ms / p99 28ms screening latency against the SDN list alone.
Federal data · Sanctions and illicit finance · Engineering and infrastructure
A deep dive into the feature engineering behind Voidly's 7-day internet shutdown forecasting model: political calendar integration (election dates, protest intensity via GDELT), OFAC sanctions timeline features, BGP withdrawal rate, probe measurement rate drops as precursor signals, historical shutdown patterns, and XGBoost SHAP feature importance across 200 countries.
Censorship and information control · Machine learning and OSINT
How we build a 7-day predictive model for internet shutdowns across 200 countries: political calendar features, network telemetry, ARIMA + XGBoost ensemble, and per-country reliability scoring.
Censorship and information control · Machine learning and OSINT
How Voidly aggregates calibrated per-measurement censorship probabilities into country-level shutdown risk signals: a three-stage aggregation hierarchy (ASN-domain hourly → domain → country), exponential decay weighting with 48-hour half-life over a 14-day window, a 28-feature forecast vector with risk score time series and ASN block concentration, and the Kafka voidly.forecast.features topic handoff to the Bayesian shutdown forecasting service.
Censorship and information control · Machine learning and OSINT
How Voidly calibrates its anomaly classifier separately for each country — Platt scaling logistic regression fitted on per-country holdout predictions, F2-weighted threshold tuning per class, 30-day rolling calibration windows, and calibration case studies: Iran DNS tampering fires at threshold 0.62 (consistent single-authority blocking); China DNS tampering requires 0.74 (CDN split-horizon noise).
Censorship and information control · Machine learning and OSINT
How Voidly retrains its five-class censorship anomaly classifier on a weekly cadence: time-based train/val/test splits to prevent temporal leakage, SMOTE resampling for class imbalance, PSI drift detection, champion/challenger shadow deployment, and the canary rollout process.
Censorship and information control · Machine learning and OSINT