论文标题
朝着低功耗的物联网设备混淆的恶意软件检测
Towards Obfuscated Malware Detection for Low Powered IoT Devices
论文作者
论文摘要
随着物联网和边缘设备在商业和用户网络中的部署增加，这些设备已成为恶意软件作者的新威胁向量。当这些设备在商业和个人网络中变得更加普遍时，必须保护这些设备。但是，由于其计算功率和存储空间有限，尤其是在电池供电的设备的情况下，将最新的恶意软件探测器部署到这些系统上是不可行的。在这项工作中，我们建议使用和提取从Opcode Traces构建的Markov矩阵中的功能，作为无焦点和混淆的恶意软件检测的低成本功能。我们从经验上表明，我们的方法保持高检测率，而消耗的功率比类似工作少。
With the increased deployment of IoT and edge devices into commercial and user networks, these devices have become a new threat vector for malware authors. It is imperative to protect these devices as they become more prevalent in commercial and personal networks. However, due to their limited computational power and storage space, especially in the case of battery-powered devices, it is infeasible to deploy state-of-the-art malware detectors onto these systems. In this work, we propose using and extracting features from Markov matrices constructed from opcode traces as a low cost feature for unobfuscated and obfuscated malware detection. We empirically show that our approach maintains a high detection rate while consuming less power than similar work.
