Breaking Tor anonymity with game theory and data mining

Attacking anonymous communication networks is very tempting, and many types of attacks have already been observed. In the case for Tor, a widely used anonymous overlay network is considered. Despite the deployment of several protection mechanisms, an attack originated by just one rogue exit node is proposed. The attack is composed of two elements. The first is an active tag injection scheme. The malicious exit node injects image tags into all HTTP replies, which will be cached for upcoming requests and allow different users to be distinguished. The second element is an inference attack that leverages a semi-supervised learning algorithm to reconstruct browsing sessions. Captured traffic flows are clustered into sessions, such that one session is most probably associated to a specific user. The clustering algorithm uses HTTP headers and logical dependencies encountered in a browsing session. A prototype has been implemented and its performance evaluated on the Tor network. The article also describes several countermeasures and advanced attacks, modeled in a game theoretical framework, and their effectiveness assessed with reference to the Nash equilibrium. Copyright (c) 2011 John Wiley & Sons, Ltd.