通过EM信号分析和SVD去噪检测噪声环境中的码注入
Detecting Code Injections in Noisy Environments Through EM Signal Analysis and SVD Denoising
摘要：
嵌入式设备在支持关键应用程序的网络中的渗透使它们成为攻击者和作恶者有利可图的目标。然而，由于这些系统的存储器和计算限制，可能不支持传统的保护机制。最近，电磁（EM）发射的分析引起了研究界的兴趣。因此，类似的保护系统已经成为可行的解决方案，用于为资源受限的设备提供外部的、非侵入式的控制流证明。不幸的是，目前的大多数工作未能考虑到现实生活中的因素，主要是环境噪声的影响的影响。在这项工作中，我们引入了一个框架，集成了奇异值分解（SVD）以及离群值检测发现恶意修改的嵌入式软件，即使在可变的噪声条件下。我们提出的框架实现了高检测精度，即，对于未知发作，AUC评分高于93\%，即使对于极端噪声条件，即，-10 SNR。据我们所知，这是第一次这个现实的限制因素，即，环境噪声，成功地解决了嵌入式设备的基于EM的异常检测的上下文中。
英文摘要：
The penetration of embedded devices in networks that support critical applications has rendered them a lucrative target for attackers and evildoers. However, traditional protection mechanisms may not be supported due to the memory and computational limitations of these systems. Recently, the analysis of electromagnetic (EM) emanations has gathered the interest of the research community. Thus, analogous protection systems have emerged as a viable solution e.g., for providing external, non-intrusive control-flow attestation for resource-constrained devices. Unfortunately, the majority of current work fails to account for the implications of real-life factors, predominantly the impact of environmental noise. In this work, we introduce a framework that integrates singular value decomposition (SVD) along with outlier detection for discovering malicious modifications of embedded software even under variable conditions of noise. Our proposed framework achieves high detection accuracy i.e., above 93\% AUC score for unknown attacks, even for extreme noise conditions i.e., -10 SNR. To the best of our knowledge, this is the first time this realistic limiting factor, i.e., environmental noise, is successfully addressed in the context of EM-based anomaly detection for embedded devices.
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