AI-Driven Threat Detection in the Internet of Things (IOT), Exploring Opportunities and Vulnerabilities

The rapid growth of the Internet of Things (IoT) has brought about some serious security challenges, mainly because of the sheer number of connected devices and the intricate designs they come with.This paper dives into how Artificial Intelligence (AI) can boost IoT security by using advanced methods for detecting threats.Techniques powered by AI, like machine learning (ML) and deep learning (DL), show great promise in spotting anomalies, fending off attacks, and managing cyber risks in real-time IoT settings.By utilizing both supervised and unsupervised learning models, the study showcases how AI can help identify and tackle both familiar and unfamiliar threats within IoT networks.It also looks at reinforcement learning as a way to create adaptive security solutions in ever-changing IoT environments.Plus, blockchain technology is used to ensure that communications are authentic and that data integrity is maintained across IoT devices.The research includes case studies, such as smart home and industrial IoT applications, and introduces an AI-driven framework designed to protect critical IoT infrastructures.The paper wraps up by urging further investigation into distributed AI-based threat detection systems and emphasizes the need for privacy and security as we continue to develop AI-enhanced IoT systems.