Analyzing Cache Side Channels Using Deep Neural Networks
Analyzing Cache Side Channels Using Deep Neural Networks
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DOI：
10.1145/3274694.3274715
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发表时间：
2018-12
期刊：
影响因子：
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通讯作者：
Tianwei Zhang;Yinqian Zhang;R. Lee
中科院分区：
文献类型：
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作者：
Tianwei Zhang;Yinqian Zhang;R. Lee
缓存旁道攻击的目的是通过CPU缓存来破坏计算机系统的机密性并提取敏感秘密。在过去的几年中，针对各种缓存架构的不同类型的侧信道攻击已经被证明。同时，也设计了不同的防御方法和系统来减轻这些攻击。然而，定量评估这些攻击和防御的有效性一直具有挑战性。我们提出了一个通用的方法来评估缓存侧信道攻击和防御。具体来说，我们的方法构建了一个深度神经网络，其输入是对手的观察信息，其输出是受害者的执行轨迹。通过训练神经网络，可以自动发现输入和输出之间的关系。因此，神经网络的预测精度可以作为一个度量，以量化对手可以正确获取多少信息，以及在不同的攻击场景下，防御解决方案在减少信息泄漏方面的有效性。我们的评估表明，该方法可以有效地评估不同的攻击和防御。
Cache side-channel attacks aim to breach the confidentiality of a computer system and extract sensitive secrets through CPU caches. In the past years, different types of side-channel attacks targeting a variety of cache architectures have been demonstrated. Meanwhile, different defense methods and systems have also been designed to mitigate these attacks. However, quantitatively evaluating the effectiveness of these attacks and defenses has been challenging. We propose a generic approach to evaluating cache side-channel attacks and defenses. Specifically, our method builds a deep neural network with its inputs as the adversary's observed information, and its outputs as the victim's execution traces. By training the neural network, the relationship between the inputs and outputs can be automatically discovered. As a result, the prediction accuracy of the neural network can serve as a metric to quantify how much information the adversary can obtain correctly, and how effective a defense solution is in reducing the information leakage under different attack scenarios. Our evaluation suggests that the proposed method can effectively evaluate different attacks and defenses.