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ITI Seminar: Matthew Wright, "Deep Fingerprinting: Undermining Website Fingerprinting Defenses with Deep Learning"

Event Type
Seminar/Symposium
Sponsor
Information Trust Institute
Location
CSL Auditorium (B02)
Date
Nov 13, 2018   3:00 pm  
Speaker
Matthew Wright, Rochester Institute of Technology
Contact
Tonia Siuts
E-Mail
tsiuts@illinois.edu
Views
58

ABSTRACT: 

 

Website fingerprinting enables a local eavesdropper to determine which websites a user is visiting over an encrypted connection. State-of-the-art website fingerprinting attacks have been shown to be effective even against Tor. Recently, lightweight website fingerprinting defenses for Tor have been proposed that substantially degrade existing attacks: WTF-PAD and Walkie-Talkie. In this work, we present Deep Fingerprinting (DF), a new website fingerprinting attack against Tor that leverages a type of deep learning called Convolutional Neural Networks (CNN) with a sophisticated architecture design, and we evaluate this attack against WTF-PAD and Walkie-Talkie. The DF attack attains over 98% accuracy on Tor traffic without defenses, better than all prior attacks, and it is also the only attack that is effective against WTF-PAD with over 90% accuracy. Walkie-Talkie remains effective, holding the attack to just 49.7% accuracy. In the more realistic open-world setting, our attack remains effective, with 0.99 precision and 0.94 recall on undefended traffic. Against traffic defended with WTF-PAD in this setting, the attack still can get 0.96 precision and 0.68 recall. These findings highlight the need for effective defenses that protect against this new attack and that could be deployed in Tor.

 

BIOGRAPHY:

 

Matt Wright is the Director of the Center for Cybersecurity at RIT and a Professor of Computing Security. He graduated with his PhD from the Department of Computer Science at the University of Massachusetts in May, 2005, where he earned his MS in 2002. His dissertation work examined attacks and defenses for systems that provide anonymity online. His other interests include adversarial machine learning and understanding the human element of security. Previously, he earned his BS degree in Computer Science at Harvey Mudd College. He has been the lead investigator on over $5.7 million in funded projects, including an NSF CAREER award, and he has published 89 peer-reviewed papers, including numerous contributions in the most prestigious venues focused on computer security and privacy.

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