Designing a Secure Integration of IoT Ecosystem and Intrusion Detection Using Machine Learning Approaches

Abstract

The Internet has become an indistinguishable part of human life, and the number of associated devices is also expanding exponentially. Specifically, the Internet of Things (IoT) devices has become a regular and indispensable part of human life which has now intruded in every corner, be it home, office or industry. However, security is still the primary concern, which is deemed to be solved by applying different machine learning techniques. Moreover, IoT and machine learning are a deadly combination and can help achieve many of these security issues. Machine learning techniques can help find real solutions to the most typical problems faced in the IoT ecosystem. It can also achieve the result with very high accuracy from the anonymous data provided to it. Besides, machine learning techniques have solid generalisability, so that they are additionally ready to distinguish obscure attacks. The authors here discuss the security issues in IoT ecosystems and how they can be solved using machine learning techniques. A modular architecture using machine learning and different intrusion detection techniques is also proposed in this paper.