A Comparative Analysis for Designing Security Mechanism for Resource-Constrained Internet of Things Devices

Abstract

In today’s digital age, the rise of Internet of Things (IoT) devices has remarkably transformed technological interactions, offering unprecedented convenience and efficiency across various domains. However, this rapid increase in IoT adoption has also introduced significant network security challenges, as these interconnected devices offer a broader surface vulnerable to cyber threats. Intrusion Detection Systems (IDS) aim to monitor network traffic, identify suspicious patterns, and promptly respond to potential security breaches. This paper provides a comprehensive review of various machine learning algorithms employed in IDS specifically designed for IoT devices. Our primary objective is to critically evaluate the efficiency, strengths, and limitations of these algorithms in detecting and countering threats within the unique constraints of IoT ecosystems. This review encompasses a thorough analysis of emerging ML technologies, including but not limited to Decision Trees, Support Vector Machines, Random Forests, Neural Networks, and Deep Learning models.