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Generative AI for Cybersecurity and Privacy in Cyber-Physical Systems
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Author(s): Arul Kumar Natarajan (Samarkand International University of Technology, Uzbekistan), Yash Desai (Ramrao Adik Institute of Technology, India), Pravin R. Kshirsagar (J.D. College of Engineering and Management, India), Kamal Upreti (Christ University, India)and Tan Kuan Tak (Singapore Institute of Technology, Singapore)
Copyright: 2025
Pages: 26
Source title:
Navigating Cyber-Physical Systems With Cutting-Edge Technologies
Source Author(s)/Editor(s): Ramesh Chandra Poonia (Christ University, India)and Kamal Upreti (Christ University, India)
DOI: 10.4018/979-8-3693-5728-6.ch003
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Abstract
With the proliferation of Cyber-Physical Systems (CPS) across various domains, ensuring robust cybersecurity and privacy has become increasingly critical. Generative Artificial Intelligence (AI) presents innovative approaches to enhancing the security and privacy of these systems. This book chapter explores the intersection of Generative AI with cybersecurity and privacy within CPS environments. It examines how Generative AI techniques, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), can be leveraged to detect and mitigate cyber threats and vulnerabilities while protecting sensitive data and user privacy. The chapter provides an overview of CPS, addressing its unique security and privacy challenges, and demonstrates the practical application of Generative AI through a case study on phishing detection using BERT-based sequence classification. The experimental results highlight the effectiveness of Generative AI in strengthening CPS security.
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