Machine Learning Techniques to Mitigate Security Attacks in IoT

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Author(s): Kavi Priya S. (Mepco Schlnek Engineering College, India), Vignesh Saravanan K. (Ramco Institute of Technology, Rajapalayam, India)and Vijayalakshmi K. (Ramco Institute of Technology, Rajapalayam, India)
Copyright: 2022
Pages: 22
Source title: Research Anthology on Machine Learning Techniques, Methods, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-6684-6291-1.ch034

Keywords: Artificial Intelligence / Engineering Science Reference / Knowledge Management and Library Science / Machine Learning

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Abstract

Evolving technologies involve numerous IoT-enabled smart devices that are connected 24-7 to the internet. Existing surveys propose there are 6 billion devices on the internet and it will increase to 20 billion devices within a few years. Energy conservation, capacity, and computational speed plays an essential part in these smart devices, and they are vulnerable to a wide range of security attack challenges. Major concerns still lurk around the IoT ecosystem due to security threats. Major IoT security concerns are Denial of service(DoS), Sensitive Data Exposure, Unauthorized Device Access, etc. The main motivation of this chapter is to brief all the security issues existing in the internet of things (IoT) along with an analysis of the privacy issues. The chapter mainly focuses on the security loopholes arising from the information exchange technologies used in internet of things and discusses IoT security solutions based on machine learning techniques including supervised learning, unsupervised learning, and reinforcement learning.

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Machine Learning Techniques to Mitigate Security Attacks in IoT

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