Securing the Internet of Things using Machine Learning: A Review

Abstract

The Internet of Things facilitates integration of massive group of devices into networks to provide data for an ever-growing number of applications. The current and future IoT applications holds promise to improve the convenience and comfort for the user but are prone to various types of security threats namely Denial of Service (DoS), Man-in-the-Middle, spoofing, Jamming, Eavesdropping and software attacks. Therefore, it becomes crucial to address these security challenges. In this paper, we discuss major security threats that exist at IoT layers and review Machine Learning based IoT security systems with a focus on Supervised Learning.