ELECTRICA
Original Article

IoT Malware Detection Based on OPCODE Purification

1.

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

2.

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

3.

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

4.

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.

Files
EISSN 2619-9831