AI-Driven Threat Detection in the Internet of Things (IoT), Exploring

Opportunities and Vulnerabilities.

Abstract

The rapid proliferation of the Internet of Things (IoT) has introduced significant security challenges due to the increasing number of connected devices and the complexity of their architectures. This paper explores the role of Artificial Intelligence (AI) in enhancing IoT security through advanced threat detection methodologies. AI-driven techniques, such as machine learning (ML) and deep learning (DL), provide promising solutions for detecting anomalies, mitigating attacks, and managing cyber risks in real-time IoT environments. By leveraging both supervised and unsupervised learning models, the study highlights the potential of AI to identify and counter both known and unknown threats in IoT networks. Reinforcement learning is also examined as a strategy for adaptive security solutions in dynamic IoT ecosystems. Additionally, blockchain technology is employed to verify communication authenticity and safeguard data integrity across IoT devices. The research discusses case studies, such as smart home and industrial IoT setups, and presents an AI-powered framework for securing critical IoT infrastructures. The paper concludes with a call for further exploration of distributed AI-based threat detection architectures and highlights the importance of privacy and security in the future development of AI-driven IoT systems.

Keywords: AI-driven threat detection, Internet of Things (IoT), machine learning (ML), deep learning (DL), cybersecurity, anomaly detection, supervised learning, unsupervised learning, reinforcement learning, blockchain technology, IoT security, AIoT, cyber-physical systems (CPS), edge computing, privacy, data integrity, AI in IoT