Machine Learning-Based Malware Detection for IoT Devices: Understanding the Evolving Threat Landscape and Strategies for Protection

Abstract The Internet of Things (IoT) has experienced significant growth in recent years, with IoT connections surpassing those of traditional connected devices in the last few years. However, with this growth comes an increased risk of IoT security breaches as cybercriminals take advantage of lax security measures at the endpoint level. One of the main challenges in IoT security is the lack of proper protocols, policies, and procedures to protect these devices from malware and malicious software. This review article focuses on the recent advancements in machine learning-based malware detection techniques for IoT devices. We discuss the challenges of detecting malware in IoT devices, including the limited resources and processing power, as well as the diversity of device types and operating systems. We also review recent machine learning-based malware detection techniques for IoT devices, including deep learning, ensemble learning, and transfer learning, and evaluate their efficiency. This review aims to provide a comprehensive understanding of the current state-of-the-art machine learning-based malware detection techniques for IoT devices, highlighting the potential and limitations of these techniques and the role of analytics in future research directions.