Enhancement of IoT-Based Intrusion Detection Systems by AI and ML Methodologies
Shatarupa Bandyopadhyay, Jaya Subalakshmi Ramamoorthi · Auerbach Publications eBooks · 2026
Intrusion Detection Systems have an important role in detecting cyber attacks and generating alerts to reduce the response time to such threats. Hence, its usage in IoT systems is crucial as these systems suffer from various vulnerabilities like unauthorized access, data breaches or malware attacks, due to its dynamic and resource constrained nature. While AI solutions have been incorporated into current IDS in IoT systems, their efficacy is subject to factors like the dataset used for training and evaluation, processing power available and their robustness to unseen threats. This chapter attempts to review the datasets used to train ML models for IDSs current AI solutions for it and the research gaps. Further, it also aims to propose a hybrid theoretical IDS model for detecting and classifying attacks and overcoming the shortcomings of contemporary models. Finally, it concludes by discussing the limitations and future directions of the proposed framework.