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Machine Learning and Federated Learning in Industrial Cybersecurity
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Author(s): Rampelli Manojkumar (BVRIT HYDERABAD College of Engineering for Women, India), Sanivarapu Prasanth Vaidya (BVRIT HYDERABAD College of Engineering for Women, India), Prasanta Kumar Jena (BVRIT HYDERABAD College of Engineering for Women, India), Sarabu Ashok (BVRIT HYDERABAD College of Engineering for Women, India)and Sandeep Dasari (Woxsen University, India)
Copyright: 2025
Pages: 32
Source title:
AI-Enhanced Cybersecurity for Industrial Automation
Source Author(s)/Editor(s): Hari Mohan Pandey (Bournemouth University, UK)and Pawan Kumar Goel (Raj Kumar Goel Institute of Technology, India)
DOI: 10.4018/979-8-3373-3241-3.ch020
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Abstract
The integration of Artificial Intelligence and Machine Learning into industrial cybersecurity addresses the limitations of traditional methods against evolving cyber threats. This chapter explores supervised, unsupervised, and reinforcement learning techniques for threat detection, anomaly detection, and adaptive response mechanisms. It emphasizes Federated Learning (FL) as a privacy-preserving approach in the Industrial Internet of Things, enabling collaborative model training without data exposure. Ethical and legal challenges, including bias, accountability, and regulatory compliance, are analyzed to ensure responsible AI deployment. Case studies in automotive, energy, and industrial control systems demonstrate FL's effectiveness in predictive maintenance. Future trends such as Quantum AI and Explainable AI are discussed for enhancing cryptographic security and transparency. The chapter concludes with managerial implications for adopting AI-driven solutions, emphasizing privacy, and cross-industry collaboration to safeguard critical infrastructure in Industry 5.0.
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