Poster

Abstract

Over 8 million users rely on the Tor network each day to protect their anonymity online. Unfortunately, Tor has been shown to be vulnerable to the website fingerprinting attack, which allows an attacker to deduce the website a user is visiting based on patterns in their traffic. The state-of-the-art attacks leverage deep learning to achieve high classification accuracy using raw packet information. Work thus far, however, has examined only one type of media delivered over the Tor network: web pages, and mostly just home pages of sites. In this work, we instead investigate the fingerprintability of video content served over Tor. We collected a large new dataset of network traces for 50 YouTube videos of similar length. Our preliminary experiments utilizing a convolutional neural network model proposed in prior works has yielded promising classification results, achieving up to 55% accuracy. This shows the potential to unmask the individual videos that users are viewing over Tor, creating further privacy challenges to consider when defending against website fingerprinting attacks.

SCNN-Attack: A Side-Channel Attack to Identify YouTube Videos in a VPN and Non-VPN Network Traffic

“…For example, Shi investigates the flow statistics, such as packet arrival time, intervals, and inter-packets delay for identification of videos in VPN settings [28,29]. Rahman et al [30] evaluate the detection of videos in Tor traffic. A Tor is like a VPN; however, in a VPN, traffic is tunneled through a central server, whereas in a Tor, the traffic is tunneled through a decentralized layer of independent nodes.…”