Poster: Video Fingerprinting in Tor
Poster: Video Fingerprinting in Tor
复制标题
DOI：
10.1145/3319535.3363273
复制
发表时间：
2019-11
期刊：
影响因子：
--
通讯作者：
Mohammad Saidur Rahman;Nate Mathews;M. Wright
中科院分区：
文献类型：
--
作者：
Mohammad Saidur Rahman;Nate Mathews;M. Wright
每天有超过800万用户依赖Tor网络来保护他们的在线匿名性。不幸的是，Tor已被证明容易受到网站指纹攻击，该攻击允许攻击者根据用户流量的模式推断用户正在访问的网站。最先进的攻击利用深度学习来使用原始数据包信息实现高分类精度。然而，到目前为止，这项工作只研究了Tor网络上提供的一种媒体类型：网页，而且大多只是网站的主页。在这项工作中，我们研究了Tor上提供的视频内容的指纹可打印性。我们为50个长度相似的YouTube视频收集了一大批新的网络跟踪数据。我们利用前人工作中提出的卷积神经网络模型进行了初步实验，得到了令人满意的分类结果，达到了55%的准确率。这表明有可能揭开用户在Tor上观看的个人视频的面纱，从而在防御网站指纹攻击时带来更多的隐私挑战。
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.