AI-Driven IoT Security: A Comprehensive Review on Current Trends and Future Perspectives

The rapid expansion of Internet of Things (IoT) usage has introduced multiple security challenges, necessitating strong protection strategies. Artificial Intelligence (AI) has emerged as a powerful solution to strengthen IoT network security through intelligent threat management. This paper aims to present an in-depth review of recent research on AI-driven IoT security, with a particular emphasis on the latest developments and techniques. Through the analysis of Intrusion Detection Systems (IDS) and AI-based security approaches, we show how they can be applied to detect and counter cyberattacks. Furthermore, we assess the advantages and limitations of these approaches by examining performance indicators such as accuracy, precision, recall, and Fl-score. From our findings, we suggest that it is crucial to combine advanced techniques like autoencoders and hybrid learning strategies to improve the security features of IoT environments. At last, we discuss the future research perspectives, and underline the importance of the design of adaptive and lightweight AI models for IoT security.