Website Fingerprinting - Last Level Cache Contention Traces

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 secure network. In this work we investigate these attacks under a different attack model, in which the adversary is capable of sending a small amount of malicious JavaScript code to the target user's computer. The malicious code mounts a cache side- channel attack, which exploits the effects of contention on the CPU's cache, to identify other websites being browsed. 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. We show that cache website fingerprinting attacks in JavaScript are highly feasible. 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 attack is more resistant than network-based fingerprinting to the effects of response caching, and 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. To protect against cache-based website fingerprinting, new defense mechanisms must be introduced to privacy-sensitive browsers and websites. We investigate one such mechanism, and show that generating artificial cache activity reduces the effectiveness of the attack and completely eliminates it when used in the Tor Browser.

网站指纹识别攻击，此类攻击通过分析网络流量以侵害用户隐私，即便在托尔（Tor）等匿名保护网络中传输的流量，也已被证明其有效性。传统的用于评估网站指纹识别攻击的攻击模型假设存在一种路径上的攻击者，该攻击者能够观察到用户计算机与安全网络之间传输的所有流量。在本研究中，我们针对不同的攻击模型对这些攻击进行了调查，其中攻击者能够向目标用户的计算机发送一小部分恶意JavaScript代码。恶意代码执行缓存旁路攻击，利用CPU缓存的竞争效应，以识别正在浏览的其他网站。这种攻击场景的有效性从未被系统性地分析过，尤其是在开放世界模型中，该模型假定用户正在访问敏感与非敏感网站的混合。我们证明了在JavaScript中实施缓存网站指纹识别攻击的高度可行性。具体而言，我们利用机器学习技术对缓存活动轨迹进行分类。与试图识别缓存冲突的先前工作不同，我们的工作测量了最后一级缓存的总体占用率。我们表明，我们的方法在开放世界和封闭世界模型中均实现了高分类精度。我们进一步表明，我们的攻击相对于基于网络的指纹识别对响应缓存的效应具有更高的抵抗力，并且我们的技术对基于网络的防御以及作为对Spectre攻击的响应引入现代浏览器的旁路攻击对策都具有适应性。为了防范基于缓存的网站指纹识别攻击，必须在隐私敏感的浏览器和网站上引入新的防御机制。我们研究了其中一种机制，并表明生成人工缓存活动降低了攻击的有效性，并在使用托尔浏览器时完全消除了该攻击。