Machine Learning-Assisted Intrusion Detection for Enhancing Internet of Things Security

TL;DR

This paper reviews machine learning-based intrusion detection methods for IoT security, emphasizing real-time detection, accuracy, and efficiency, while identifying research gaps and future directions.

Contribution

It provides a comprehensive taxonomy of existing approaches and highlights limitations in current IoT security frameworks for future research.

Findings

Identifies key research gaps in IoT intrusion detection

Highlights limitations of current security frameworks

Provides a taxonomy of machine learning approaches

Abstract

Attacks against the Internet of Things (IoT) are rising as devices, applications, and interactions become more networked and integrated. The increase in cyber-attacks that target IoT networks poses a considerable vulnerability and threat to the privacy, security, functionality, and availability of critical systems, which leads to operational disruptions, financial losses, identity thefts, and data breaches. To efficiently secure IoT devices, real-time detection of intrusion systems is critical, especially those using machine learning to identify threats and mitigate risks and vulnerabilities. This paper investigates the latest research on machine learning-based intrusion detection strategies for IoT security, concentrating on real-time responsiveness, detection accuracy, and algorithm efficiency. Key studies were reviewed from all well-known academic databases, and a taxonomy was provided for the existing approaches. This review also highlights existing research gaps and outlines the limitations of current IoT security frameworks to offer practical insights for future research directions and developments.