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AI-Powered Digital Forensics for Industrial Cyber Incidents
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Author(s): P. Selvakumar (Department of Science and Humanities, Nehru Institute of Technology, Coimbatore, India), K. Gandhimathi (PSGR Krishnammal College for Women, India), Anjali (United College of Engineering and Research, Prayagraj, India), P. Sudheer (CVR College of Engineering, India), Nilesh Anute (Sri Balaji University, India)and T. C. Manjunath (Rajarajeswari College of Engineering, India)
Copyright: 2025
Pages: 22
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.ch022
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Abstract
The increasing digitization of industrial control systems (ICS) and critical infrastructure has made these environments prime targets for cyber threats. Traditional digital forensic methodologies (AI) and machine learning (ML) to automate data analysis, accelerate threat detection, and enhance forensic accuracy. and mitigating cyber incidents across industrial sectors, including energy, manufacturing, utilities, and transportation AI-powered digital forensics enhances industrial cybersecurity by automating threat detection, identifying anomalous behavior in ICS networks, and uncovering, and operational technology (OT) environments to identify malicious and real-time incident response to prevent operational downtime. Additionally, AI-powered behavioral analytics help detect insider threats by correlating multi-source industrial data to identify suspicious user activity, unauthorized command execution, and compromised credentials
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