A review on machine learning based intrusion detection system for internet of things enabled environment

Abstract

Within an internet of things (IoT) environment, the fundamental purpose of various devices is to gather the abundant amount of data that is being generated and then transmit this data to the predetermined server over the internet. IoT connects billions of objects and the internet to communicate without human intervention. But network security and privacy issues are increasing very fast, in today's world. Because of the prevalence of technological advancement in regular activities, internet security has evolved into a necessary requirement. Because technology is integrated into every aspect of contemporary life, cyberattacks on the internet of things represent a bigger danger than attacks against traditional networks. Researchers have found that combining machine learning techniques into an intrusion detection system (IDS) is an efficient way to get beyond the limitations of conventional IDSs in an IoT context. This research presents a comprehensive literature assessment and develops an intrusion detection system that makes use of machine learning techniques to address security problems in an IoT environment. Along with a comprehensive look at the state of the art in terms of intrusion detection systems for IoT-enabled environments, this study also examines the attributes of approaches, common datasets, and existing methods utilized to construct such systems.