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首页> 外文期刊>International Journal of Information Security >SpyDetector: An approach for detecting side-channel attacks at runtime
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SpyDetector: An approach for detecting side-channel attacks at runtime

机译:Spydetector:一种在运行时检测侧通道攻击的方法

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摘要

In this work, we first present a low-cost, anomaly-based semi-supervised approach, which is instrumental in detecting the presence of ongoing side-channel attacks at runtime. We are, in particular, concerned with attacks that are carried out by creating intentional contentions in shared resources with cryptographic applications using a spy process. At a very high level, the approach quantifies contentions in shared resources, associates these contentions with processes, such as with a victim process, and issues a warning at runtime whenever the contentions reach a suspicious level. We then adapt this approach to detect the presence of four different types of cache-based side-channel attacks, namely prime-and-probe attacks on advanced encryption standard (AES), flush-and-reload attacks on AES and elliptic curve digital signature algorithm with Montgomery ladder algorithm, and Flush+Flush attacks on AES. To this end, we vary the shared resources monitored, the level of granularity at which the contentions in these resources are quantified, and the way the suspicious levels of contentions are detected. We evaluate the proposed approach also in cross-virtual machine setups (when applicable). The results of our experiments support our basic hypothesis that spy processes, which leverage information leaked by cryptographic applications through some shared resources, ironically leak information by themselves through the same or related channels, which can be analyzed to detect the presence of ongoing attacks at runtime.
机译:在这项工作中,我们首先提出了一种低成本,基于异常的半监督方法,这是在检测运行时在运行时检测持续侧通道攻击的影响。特别是我们涉及通过使用间谍过程的加密应用程序创建共享资源中的有意符号来执行的攻击。在一个非常高的级别,该方法量化共享资源中的争论,将这些争论与诸如受害者进程的进程相关联,并且只要争议达到可疑级别,就会在运行时发出警告。然后,我们调整这种方法来检测四种不同类型的基于高速缓存的侧通道攻击,即对高级加密标准(AES)的主要和探测攻击,对AES和椭圆曲线数字签名进行刷新和重新加载攻击带蒙哥马利梯形算法的算法,对AES的冲洗+冲洗攻击。为此,我们改变监视的共享资源,量化这些资源中的群体的粒度水平,以及检测到争议的可疑水平的方式。我们也在交叉虚拟机设置中评估所提出的方法(适用时)。我们的实验结果支持我们的基本假设,即通过一些共享资源利用加密应用程序泄露的信息,通过相同或相关频道讽刺地泄漏信息,这可以分析以检测运行时的持续攻击的存在。 。

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