Abstract

The rapid adoption of Internet of Things (IoT) technologies in smart manufacturing has revolutionized production processes but has also introduced significant cybersecurity challenges. Cyber-physical systems, integral to modern manufacturing, are increasingly vulnerable to unauthorized access, data breaches, and operational disruptions. This paper explores the role of artificial intelligence (AI) in enhancing intrusion detection and threat modeling to secure these networks. By leveraging machine learning, deep learning, and predictive analytics, AI-driven solutions offer adaptive and real-time responses to evolving threats. The study highlights cybersecurity challenges, reviews state-of-the-art AI methodologies, and examines real-world implementations in diverse manufacturing environments. It also identifies key insights from successful deployments and discusses the potential for future advancements in scalability, real-time responsiveness, and resilience. This paper concludes by emphasizing the transformative potential of AI in building robust, secure, and efficient smart manufacturing systems while addressing the critical need for ongoing research and collaboration.