Detecting Malware In IoT Devices

Abstract

The rapid proliferation of Internet of Things (IoT) devices has led to significant security challenges, particularly with regard to malware attacks. Due to their limited computing resources, diverse architectures, and often weak security measures, IoT devices are increasingly vulnerable to malicious attacks. Traditional malware detection techniques, designed for traditional computing systems, are often ineffective in IoT environments. This paper explores modern approaches to malware detection on IoT devices, using the Random Forest algorithm in machine learning. The model was tested on two different sized datasets, yielding good results, demonstrating its effectiveness in detecting malware on these devices.