The Empowerment and Countermeasures of Artificial Intelligence in Internet of Things Security

Abstract

Amid the backdrop of a surging number of Internet of Things (IoT) devices, a rapidly expanding attack surface, and the extension of security threats from the virtual realm to the physical world, traditional security mechanisms are confronted with severe challenges of insufficient scalability, real-time performance, and adaptability. Concurrently, the rapid development of artificial intelligence (AI) technology, particularly its advantages in pattern recognition, anomaly detection, and automated decision-making, offers new empowerment paths for IoT security protection. This research systematically reviews the key applications of AI in IoT security, including intelligent intrusion detection based on deep learning, continuous identity authentication based on behavior and radio frequency fingerprints, and privacy-enhanced threat detection relying on federated learning. These technologies have significantly improved the accuracy of threat identification, the real-time response, and the feasibility of cross-device collaborative defense. However, the research also delves into the new threat of "aggressive AI" formed by the exploitation of AI technology by attackers, including evading detection through generative adversarial networks, automated penetration based on reinforcement learning, and direct adversarial attacks against AI models themselves. This reveals that while AI enhances defense capabilities, it also introduces new vulnerabilities and adversarial dimensions. The conclusion of this study emphasizes that AI plays a dual role in IoT security, both empowering and attacking, driving the security game into a new stage of rapid escalation.