A survey on intrusion detection system in IoT networks

Abstract

As the Internet of Things (IoT) expands, the security of IoT networks has becoming more critical. Intrusion Detection Systems (IDS) are essential for protecting these networks against malicious activities. Artificial intelligence, with its adaptive and self-learning capabilities, has emerged as a promising approach to enhancing intrusion detection in IoT environments. Machine learning facilitates dynamic threat identification, reduces false positives, and addresses evolving vulnerabilities. This survey provides an analysis of contemporary intrusion detection techniques, models, and their performances in IoT networks, offering insights into IDS design and implementation. It reviews data extraction techniques, useful matrices, and loss functions in IDS for IoT networks, ranking top-cited algorithms and categorizing IDS studies based on different approaches. The survey evaluates various datasets used in IoT intrusion detection, examining their attributes, benefits, and drawbacks, and emphasizes performance metrics and computational efficiency, providing insights into IDS effectiveness and practicality. Standardized evaluation metrics and real-world testing are stressed to ensure reliability. Additionally, the survey identifies significant challenges and open issues in ML and DL-based IDS for IoT networks, such as computational complexity and high false positive rates, and recommends potential research directions, emerging trends, and perspectives for future work. This forward-looking perspective aids in shaping the future direction of research in this dynamic field, emphasizing the need for lightweight, efficient IDS models suitable for resource- constrained IoT devices and the importance of comprehensive, representative datasets.