  Traffic analysis attacks to identify which web page a client is browsing,
using only her packet metadata --- known as website fingerprinting --- has been
proven effective in closed-world experiments against privacy technologies like
Tor. However, due to the base rate fallacy, these attacks have failed in large
open-world settings against clients that visit sensitive pages with a low base
rate. We find that this is because they have poor precision as they were
designed to maximize recall.
  In this work, we argue that precision is more important than recall for
open-world website fingerprinting. For this reason, we develop three classes of
{\em precision optimizers}, based on confidence, distance, and ensemble
learning, that can be applied to any classifier to increase precision. We test
them on known website fingerprinting attacks and show significant improvements
in precision. Against a difficult scenario, where the attacker wants to monitor
and distinguish 100 sensitive pages each with a low mean base rate of 0.00001,
our best optimized classifier can achieve a precision of 0.78; the highest
precision of any known attack before optimization was 0.014. We use precise
classifiers to tackle realistic objectives in website fingerprinting, including
selection, identification, and defeating website fingerprinting defenses.
