Security Solutions for the Internet of Things Using Machine Learning and Deep Learning: Current Trends and Future Directions

ABSTRACT The sudden increase in adoption of the Internet of Things (IoT) has revolutionized modern living but also brought unprecedented security challenges due to its distributed, heterogeneous, and resource‐constrained nature. This review paper offers a comprehensive examination of machine learning (ML) and deep learning (DL) approaches tailored for intrusion detection and threat mitigation in IoT ecosystems. It explores the landscape of anomaly detection and classification techniques while analyzing their suitability, limitations, and deployment feasibility across IoT layers. The study also investigates the significance of feature engineering, model selection, and system scalability. A novel addition to this review is the integration of emerging trends such as explainable AI (XAI), which enhances transparency and trust in black‐box ML/DL models, and federated learning (FL), a privacy‐preserving paradigm that allows decentralized model training without raw data sharing. The synergy between FL and Edge AI is discussed to highlight real‐time, low‐latency security analytics at the network's edge. Comparative tables, domain‐specific applications (e.g., smart homes, healthcare, and industrial IoT), and architectural illustrations support the discourse, providing readers with an up‐to‐date understanding of current capabilities and ongoing research challenges. This paper concludes with practical implications, research gaps, and future directions for building intelligent, secure, and explainable IoT security frameworks that respect user privacy and enable scalable deployment. This article is categorized under: Fundamental Concepts of Data and Knowledge > Explainable AI Technologies > Internet of Things Technologies > Machine Learning