Original Article

IoT Malware Detection Based on OPCODE Purification


Department of Computer Engineering, Science Institute of Istanbul Commerce University, İstanbul, Turkey


Department of Research and Development, NetRD Information Technologies and Telecommunications, İstanbul, Turkey


Department of Computer Engineering, Istanbul University-Cerrahpasa, Faculty of Engineering, İstanbul, Turkey


Department of Computer Engineering, Istanbul Commerce University, İstanbul, Turkey

ELECTRICA 2023; 23: 634-642
DOI: 10.5152/electrica.2023.23043
Read: 591 Downloads: 297 Published: 01 August 2023

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.

EISSN 2619-9831