Robust Website Fingerprinting Through the Cache Occupancy Channel
Robust Website Fingerprinting Through the Cache Occupancy Channel
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发表时间：
2018-11
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通讯作者：
A. Shusterman;Lachlan Kang;Yarden Haskal;Yosef Meltser;Prateek Mittal;Yossef Oren;Y. Yarom
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作者：
A. Shusterman;Lachlan Kang;Yarden Haskal;Yosef Meltser;Prateek Mittal;Yossef Oren;Y. Yarom
网站指纹攻击使用网络流量的统计分析来损害用户隐私，即使流量是通过匿名网络（例如TOR）发送的，也已被证明是有效的。用于评估网站指纹攻击的经典攻击模型假设了一个对手，他可以观察到用户计算机和TOR网络之间的所有流量。在这项工作中，我们在不同的攻击模型下调查了这些攻击，在该模型中，对手能够在目标用户的计算机上运行少量的无私人代码。在此模型下，攻击者可以安装高速缓存侧通道攻击，从而利用争论对CPU的缓存的影响，以识别浏览的网站。在此攻击模型的重要特殊情况下，当目标用户访问由攻击者控制的网站时，将启动JavaScript攻击。这种攻击方案的有效性从未系统地分析，尤其是在开放世界模型中，该模型假设用户正在访问敏感和非敏感站点的混合。在这项工作中，我们表明JavaScript中的Cache网站指纹攻击是非常可行的，即使它们是从高度限制性的环境（例如TOR浏览器）运行的。具体来说，我们使用机器学习技术来对缓存活动的痕迹进行分类。与试图识别缓存冲突的先前工作不同，我们的工作衡量了最后一级缓存的整体占用。我们表明，我们的方法在开放世界和封闭世界模型中都达到了高分类的精度。我们进一步表明，我们的技术对基于网络的防御和对现代浏览器引入的侧渠道对策既具有韧性，又是对幽灵攻击的反应。
Website fingerprinting attacks, which use statistical analysis on network traffic to compromise user privacy, have been shown to be effective even if the traffic is sent over anonymity-preserving networks such as Tor. The classical attack model used to evaluate website fingerprinting attacks assumes an on-path adversary, who can observe all traffic traveling between the user's computer and the Tor network. In this work we investigate these attacks under a different attack model, in which the adversary is capable of running a small amount of unprivileged code on the target user's computer. Under this model, the attacker can mount cache side-channel attacks, which exploit the effects of contention on the CPU's cache, to identify the website being browsed. In an important special case of this attack model, a JavaScript attack is launched when the target user visits a website controlled by the attacker. The effectiveness of this attack scenario has never been systematically analyzed, especially in the open-world model which assumes that the user is visiting a mix of both sensitive and non-sensitive sites. In this work we show that cache website fingerprinting attacks in JavaScript are highly feasible, even when they are run from highly restrictive environments, such as the Tor Browser. Specifically, we use machine learning techniques to classify traces of cache activity. Unlike prior works, which try to identify cache conflicts, our work measures the overall occupancy of the last-level cache. We show that our approach achieves high classification accuracy in both the open-world and the closed-world models. We further show that our techniques are resilient both to network-based defenses and to side-channel countermeasures introduced to modern browsers as a response to the Spectre attack.