Gülataş, İbrahimKılınç, Hacı HakanAydın, Muhammed AliZaim, Abdul Halim2024-03-272024-03-272023https://hdl.handle.net/11467/7202https://doi.org/10.5152/electrica.2023.23043Malware 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.eninfo:eu-repo/semantics/openAccessInternet of Things Malware detection, malware analysis, Operation Code analysisArticle233634642Q4WOS:001093363400020126487510.5152/electrica.2023.23043