IoT Security Techniques Based on Machine Learning

TL;DR

This paper reviews machine learning-based security techniques for IoT systems, focusing on authentication, access control, offloading, and malware detection to enhance privacy and defend against attacks.

Contribution

It provides a comprehensive review of ML-based IoT security solutions, highlighting challenges and potential for practical implementation.

Findings

ML techniques improve IoT security measures

Various ML methods are applied for authentication and malware detection

Challenges include data privacy and resource constraints

Abstract

Internet of things (IoT) that integrate a variety of devices into networks to provide advanced and intelligent services have to protect user privacy and address attacks such as spoofing attacks, denial of service attacks, jamming and eavesdropping. In this article, we investigate the attack model for IoT systems, and review the IoT security solutions based on machine learning techniques including supervised learning, unsupervised learning and reinforcement learning. We focus on the machine learning based IoT authentication, access control, secure offloading and malware detection schemes to protect data privacy. In this article, we discuss the challenges that need to be addressed to implement these machine learning based security schemes in practical IoT systems.