Name
Abstract | ||
|---|---|---|
In this paper, we address attacks that exploit the timing behavior of TCP and other protocols and applications in low-latency anonymity networks. Mixes have been used in many anonymous communication systems and are supposed to provide countermeasures to defeat traffic analysis attacks. In this paper, 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 and egress points of a flow or to reconstruct the path used by the flow. Counterintuitively, some batching strategies are actually detrimental against attacks. The empirical results provided in this paper give an indication to designers of Mix networks about appropriate configurations and mechanisms to be used to counter flow-correlation attacks. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/TPDS.2009.146 | IEEE Trans. Parallel Distrib. Syst. |
Keywords | Field | DocType |
mix network,low-latency anonymity networks,mixes,anonymity,correlation-based traffic analysis attacks,anonymous communication system,network traffic,input link,anonymous communication,privacy,tcp,frequency-domain analysis,computer network security,time-domain methods,batching strategy,flow-correlation attacks,appropriate configuration,time-domain analysis,internet,anonymity networks,adversary attempt,output link,flow-correlation attack.,frequency-domain methods,telecommunication traffic,correlation methods,flow-correlation attack,traffic analysis attack,protocols,low latency,frequency domain analysis,correlation,time domain,frequency domain | Traffic analysis,Robust random early detection,Threat model,Computer science,Computer security,Network security,Computer network,Exploit,Anonymity,Adversary,The Internet | Journal |
Volume | Issue | ISSN |
21 | 7 | 1045-9219 |
Citations | PageRank | References |
19 | 0.87 | 30 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ye Zhu | 1 | 225 | 21.76 |
| Xinwen Fu | 2 | 1054 | 86.64 |
| Bryan Graham | 3 | 171 | 10.75 |
| Riccardo Bettati | 4 | 744 | 72.39 |
| Wei Zhao | 5 | 3532 | 404.01 |