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A Contemporary Survey on the Effectiveness of Machine Learning for Detection and Classification of Cyber Attacks in IoT Systems

P. Suresh (Vellore Institute of Technology, India), P. Keerthika (Vellore Institute of Technology, India), Manjula Devi R. (KPR Institute of Engineering and Technology, India), S. Maheswaran (Kongu Engineering College, India), Kishor Kumar Sadasivuni (Qatar University, Qatar), N. Anusha (Vellore Institute of Technology, India), and Shreeya Sanjeev Gokhale (Vellore Institute of Technology, India)
Copyright: © 2025 |Pages: 64
EISBN13: 9798337300955|DOI: 10.4018/979-8-3693-2675-6.ch003
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Abstract

The interconnection of less secure devices in a network is known as the internet of things (IoT). Data and systems may be better protected with the aid of cyber security in the IoT. Cyber security violations occur most frequently when an attacker uses many systems connected to multiple networks or systems to conduct an offence. These cyber dangers can do more than just steal or corrupt data; they can also temporarily or permanently disable network infrastructure. Because it is always changing, manually detecting cyber-attacks becomes expensive and tiresome. Therefore, they may be identified and categorized using machine learning methods. Internet of things devices may now maintain connections for long durations without any intervention from a person. This chapter extensively covers cyberattack detection and categorization in IoT systems using machine learning approaches.
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