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Flow correlation is the core technique used in a multitude of deanonymizationattacks on Tor. Despite the importance of flow correlation attacks on Tor,existing flow correlation techniques are considered to be ineffective andunreliable in linking Tor flows when applied at a large scale, i.e., theyimpose high rates of false positive error rates or require impractically longflow observations to be able to make reliable correlations. In this paper, weshow that, unfortunately, flow correlation attacks can be conducted on Tortraffic with drastically higher accuracies than before by leveraging emerginglearning mechanisms. We particularly design a system, called DeepCorr, thatoutperforms the state-of-the-art by significant margins in correlating Torconnections. DeepCorr leverages an advanced deep learning architecture to learna flow correlation function tailored to Tor's complex network this is incontrast to previous works' use of generic statistical correlation metrics tocorrelated Tor flows. We show that with moderate learning, DeepCorr cancorrelate Tor connections (and therefore break its anonymity) with accuraciessignificantly higher than existing algorithms, and using substantially shorterlengths of flow observations. For instance, by collecting only about 900packets of each target Tor flow (roughly 900KB of Tor data), DeepCorr providesa flow correlation accuracy of 96% compared to 4% by the state-of-the-artsystem of RAPTOR using the same exact setting.  We hope that our work demonstrates the escalating threat of flow correlationattacks on Tor given recent advances in learning algorithms, calling for thetimely deployment of effective countermeasures by the Tor community.

DeepCorr: Strong Flow Correlation Attacks on Tor Using Deep Learning