An anomaly-based censorship-detection system for Tor

Graduated censorship — limiting the suppression rate to remain within the typical weekly variance band — evades the weekly-interval detector entirely. The paper acknowledges that detecting slow-ramp blocking requires extending the observation window beyond seven days.

§6 detection throttlingmeasurement-platform generic

Per-jurisdiction user counts are modeled as a Poisson process; the detector infers the 99.99th-percentile credible interval for the underlying rate λ from the observed count via a Gamma-Poisson approximation rather than a Gaussian assumption, correctly treating small-jurisdiction zero-user days as non-anomalous.

§3–§4 defense measurement-platform generic

The detector constructs its 'typical ratio' baseline exclusively from the 50 largest jurisdictions, then discards outliers beyond four inter-quartile ranges of the median before fitting N(m,v). This ensures a jurisdiction undergoing active censorship cannot bias the global model and mask its own anomaly.

§4 defense measurement-platform generic

A censor can defeat the anomaly detector without triggering an alert by replacing blocked user traffic with synthetic requests from adversary-controlled machines, keeping per-jurisdiction connection counts within the typical range. The paper explicitly identifies this as an unaddressed active-attack vector.

§6 detection measurement-platform generic

The deployed system uses 7-day intervals and a baseline built from the 50 largest Tor jurisdictions; a jurisdiction's user-count ratio is flagged when it falls outside the 99.99th percentile of the fitted Normal distribution N(m,v), yielding an expected false-alarm rate of approximately 1 in 10,000 per jurisdiction-week.

§5 evaluation measurement-platform generic