  We develop a means to detect ongoing per-country anomalies in the daily usage
metrics of the Tor anonymous communication network, and demonstrate the
applicability of this technique to identifying likely periods of internet
censorship and related events. The presented approach identifies contiguous
anomalous periods, rather than daily spikes or drops, and allows anomalies to
be ranked according to deviation from expected behaviour.
  The developed method is implemented as a running tool, with outputs published
daily by mailing list. This list highlights per-country anomalous Tor usage,
and produces a daily ranking of countries according to the level of detected
anomalous behaviour. This list has been active since August 2016, and is in use
by a number of individuals, academics, and NGOs as an early warning system for
potential censorship events.
  We focus on Tor, however the presented approach is more generally applicable
to usage data of other services, both individually and in combination. We
demonstrate that combining multiple data sources allows more specific
identification of likely Tor blocking events. We demonstrate the our approach
in comparison to existing anomaly detection tools, and against both known
historical internet censorship events and synthetic datasets. Finally, we
detail a number of significant recent anomalous events and behaviours
identified by our tool.
