A new cell counter based attack against tor

  • Authors:

  • Zhen Ling;Junzhou Luo;Wei Yu;Xinwen Fu;Dong Xuan;Weijia Jia

  • Affiliations:

  • Southeast University, Nanjing, China;Southeast University, Nanjing, China;Towson University, Towson, MD, USA;University of Massachusetts Lowell, Lowell, MA, USA;The Ohio State University, Columbus, OH, USA;City University of Hong Kong, Hong Kong, Hong Kong

  • Venue:
  • Proceedings of the 16th ACM conference on Computer and communications security
  • Year:
  • 2009

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Abstract

Various low-latency anonymous communication systems such as Tor and Anoymizer have been designed to provide anonymity service for users. In order to hide the communication of users, many anonymity systems pack the application data into equal-sized cells (e.g., 512 bytes for Tor, a known real-world, circuit-based low-latency anonymous communication network). In this paper, we investigate a new cell counter based attack against Tor, which allows the attacker to confirm anonymous communication relationship among users very quickly. In this attack, by marginally varying the counter of cells in the target traffic at the malicious exit onion router, the attacker can embed a secret signal into the variation of cell counter of the target traffic. The embedded signal will be carried along with the target traffic and arrive at the malicious entry onion router. Then an accomplice of the attacker at the malicious entry onion router will detect the embedded signal based on the received cells and confirm the communication relationship among users. We have implemented this attack against Tor and our experimental data validate its feasibility and effectiveness. There are several unique features of this attack. First, this attack is highly efficient and can confirm very short communication sessions with only tens of cells. Second, this attack is effective and its detection rate approaches 100% with a very low false positive rate. Third, it is possible to implement the attack in a way that appears to be very difficult for honest participants to detect (e.g. using our hopping-based signal embedding).