Machine Learning-based Anomaly Detection for IoT Security: Challenges, Techniques, and Future Directions

Abstract

The Internet of Things (IoT) has revolutionized numerous industries by interconnecting devices and enabling seamless communication. However, the proliferation of IoT devices has raised significant security concerns due to their inherent vulnerabilities. Traditional security mechanisms are often inadequate to protect against evolving threats in IoT environments. Machine learning-based anomaly detection has emerged as a promising approach to enhance IoT security by identifying abnormal behavior indicative of potential attacks. This paper provides a comprehensive review of the challenges, techniques, and future directions of machine learning-based anomaly detection for IoT security. We explore various machine learning algorithms, data sources, feature selection methods, and evaluation metrics commonly employed in anomaly detection for IoT environments. Furthermore, we discuss the unique challenges associated with implementing anomaly detection in IoT, including resource constraints, heterogeneous data formats, and scalability issues. Finally, we highlight current research trends and future directions to address the evolving landscape of IoT security threats.