Date of Award

5-2010

Document Type

Thesis

Degree Name

Master of Science (MS)

Legacy Department

Electrical Engineering

Advisor

Brooks, Richard R

Committee Member

Burg , Timothy

Committee Member

Griffin , Christopher

Abstract

This research applies statistical methods in pattern recognition to test the privacy capabilities of a very popular anonymity tool used on the Internet known as Tor.
Using a recently developed algorithm known as Causal State Splitting and Reconstruction (CSSR), we can create hidden Markov models of network processes proxied through Tor. In contrast to other techniques, our CSSR extensions create a minimum entropy model without any prior knowledge of the underlying state structure. The inter-packet time delays of the network process, preserved by Tor, can be symbolized into ranges and used to construct the models.
After the construction of training models, detection is performed using Confidence Intervals. New test data can be fed through a model to determine the intervals and estimate how well the data matches the model. If a match is found, the state sequence, or path, can be used to uniquely describe the data with respect to the model. It is by comparing these paths that Tor users can be identified.
Packet data from any two computers using the Tor network can be matched to a model and their state sequences can be compared to give a statistical likelihood that the two systems are actually communicating together over Tor. We perform experiments on a private Tor network to validate this. Results showed that communicating systems could be identified with a 95% accuracy in our test scenario.
This attack differs from previous maximum likelihood-based approaches in that it can be performed between just two computers using Tor. The adversary does not need to be a global observer. The attack can also be performed in real-time provided that a matching model had already been constructed.

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