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The Role of Machine Learning in Industrial Cybersecurity: A Paradigm Shift
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Author(s): P. Selvakumar (Department of Science and Humanities, Nehru Institute of Technology, Coimbatore, India), S. Sasikala (PSGR Krishnammal College for Women, India), Shweta Singh (United College of Engineering and Research, India), Amitava Kar (Gopal Narayan Singh University, India), Rajkumar Mandal (Gopal Narayan Singh University, India)and T. C. Manjunath (Rajarajeswari College of Engineering, India)
Copyright: 2025
Pages: 20
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.ch001
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Abstract
Transforming how industrial systems detect, mitigate, and respond to cyber threats. As industrial environments become increasingly interconnected through IoT, operational technology (OT), and cloud-based-driven cybersecurity solutions offer advanced capabilities, including real-time anomaly detection, predictive threat analysis, automated incident response, and adaptive learning, enabling organizations to transition from reactive to proactive both known and unknown cyber threats, significantly improving detection accuracy and reducing response times.This shift also enhances the scalability and efficiency of cybersecurity frameworks, making it possible to monitor complex, large-scale industrial networks with minimal human intervention.
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