Flow Correlation Attacks on Tor Onion Service Sessions with Sliding Subset Sum

Abstract

Tor is one of the most popular anonymity networks in use today. Its ability to defend against flow correlation attacks is essential for providing strong anonymity guarantees. However, the feasibility of flow correlation attacks against Tor onion services (formerly known as "hidden services") has remained an open challenge. In this paper, we present an effective flow correlation attack that can deanonymize onion service sessions in the Tor network. Our attack is based on a novel distributed technique named Sliding Subset Sum (SUMo), which can be deployed by a group of colluding ISPs worldwide in a federated fashion. These ISPs collect Tor traffic at multiple vantage points in the network, and analyze it through a pipelined architecture based on machine learning classifiers and a novel similarity function based on the classic subset sum decision problem. These classifiers enable SUMo to deanonymize onion service sessions effectively and efficiently. We also analyze possible countermeasures that the Tor community can adopt to hinder the efficacy of these attacks.