Abstract: The proliferation of IoT devices across industries has revolutionized efficiency but created an expansive, complex attack surface characterized by heterogeneous devices, weak protocols, and low physical security. Conventional security solutions are often ineffective due to IoT’s resource constraints and unique latency and scalability needs. Artificial Intelligence approaches—spanning deep learning, federated, and edge-based frameworks—address these gaps through adaptive, autonomous, and privacy-aware threat detection using real-time telemetry and behavioural analytics. Techniques such as intrusion detection, device fingerprinting, and anomaly detection enable timely response against known and novel threats. This review surveys leading AI strategies for IoT security, explores dataset benchmarks, adversarial resilience, resource allocation, explainable AI, and privacy safeguards. Ongoing challenges include defending against advanced persistent threats, ensuring robust operation across diverse environments, optimizing efficiency, and providing standardized datasets. The findings advise stakeholders on building scalable, trustworthy, and resilient AI-powered IoT security systems.

Keywords: Edge AI Security, IoT Anomaly Detection, Device Fingerprinting, Botnet Detection, Bot-IoT Dataset, N-BaIoT Dataset, Privacy-Preserving Machine Learning, Adversarial Machine Learning for IoT, Explainable AI (XAI) for IoT, Hybrid Edge-Cloud Security Architecture, IoT Threat Modelling, Distributed Intrusion Detection, Secure Federated Aggregation, and real-time threat mitigation for IoT.

Downloads: | DOI: 10.17148/IJIREEICE.2025.131004