Advancements in training and deployment strategies for AI-based intrusion detection systems in IoT: a systematic literature review

As the Internet of Things (IoT) grows, ensuring robust security is crucial. Intrusion Detection Systems (IDS) protect IoT networks from various cyber threats. This systematic literature review (SLR) explores the advancements in training and deployment strategies of Artificial Intelligence (AI) based IDS within IoT environments. The study begins by outlining prevalent IoT attacks and developing an updated taxonomy to enhance understanding of these threats. It then examines various IDS architectures and delves into the integration of machine learning (ML) and deep learning (DL) technologies that improve detection capabilities and system responsiveness. Significant emphasis is placed on analyzing different IDS training paradigms-centralized, distributed, and federated learning-and deployment strategies, including in cloud, fog, and edge layers. Their effectiveness within IoT contexts is evaluated comprehensively. Moreover, the review assesses the datasets commonly used for training and validating IDS and discusses key performance metrics and validation measures critical for assessing IDS effectiveness. The study concludes by synthesizing the major challenges facing current IDS implementations in IoT and suggesting future research directions aimed at overcoming these hurdles. This review highlights the technological advancements in IDS and sets the stage for future enhancements in IoT security, emphasizing the integration of innovative training and deployment strategies.