Correlation-Based Traffic Analysis Attacks on Anonymity Networks

Abstract

Mixes have been used in many anonymous communication systems and are supposed to provide countermeasures to defeat traffic analysis attacks. In this project, we focus on a particular class of traffic analysis attacks, flow correlation attacks, by which an adversary attempts to analyze the network traffic and correlate the traffic of a flow over an input link with that over an output link. Two classes of correlation methods are considered, namely time-domain methods and frequency-domain methods. Based on our threat model and known strategies in existing mix networks, we perform extensive experiments to analyze the performance of mixes. We find that all but a few batching strategies fail against flow-correlation attacks, allowing the adversary to either identify ingress or egress points of a flow or to reconstruct the path used by the flow. Counter intuitively, some batching strategies are actually detrimental against attacks. The empirical results provided in this project give an indication to designers of Mix networks about appropriate configurations and mechanisms to be used to counter flow-correlation attacks.