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Malware threat for Internet of Things (IoT) devices is increasing day by day. The constrained nature of IoT devices makes it impossible to apply high-resource-demand ing anti-malware tools for these devices. Therefore there is an enormous need for lightweight and efficient anti-malware solutions for IoT devices. In this study, machine learning-based malware detection is performed using purified OPCODE analysis for IoT devices with MIPS architecture. The proposed methodology reduced the runtime of IoT malware detection up to 7.2 times without reducing the accuracy ratio.
Cite this article as: İ. Gülataş, H.H. Kılınç, M.A. Aydın and A.H. Zaim, "IoT malware detection based on OPCODE purification," Electrica, 23(3), 634-642, 2023.