Evaluation of machine learning and deep learning methods for early detection of internet of things botnets

Abstrak

The internet of things (IoT) represents a rapidly expanding sector within computing, facilitating the interconnection of myriad smart devices autonomously. However, the complex interplay of IoT systems and their interdisciplinary nature has presented novel security concerns (e.g. privacy risks, device vulnerabilities, Botnets). In response, there has been a growing reliance on machine learning and deep learning methodologies to transition from conventional connectivity-centric IoT security paradigms to intelligence-driven security frameworks. This paper undertakes a comprehensive comparative analysis of recent advancements in the creation of IoT botnets. It introduces a novel taxonomy of attacks structured around the attack life-cycle, aiming to enhance the understanding and mitigation of IoT botnet threats. Furthermore, the paper surveys contemporary techniques employed for early-stage detection of IoT botnets, with a primary emphasis on machine learning and deep learning approaches. This elucidates the current landscape of the issue, existing mitigation strategies, and potential avenues for future research.