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Industrial Control Systems (ICS) Security: AI-Based Threat Detection and Prevention
Abstract
Industrial Control Systems (ICS) are crucial to modern infrastructure but remain susceptible to sophisticated cyber attacks due to legacy architectures, limited security patches, and increasing connectivity. The chapter presents AI-based solutions for enhancing ICS security through anomaly detection, intrusion prevention, and predictive threat modeling. Machine learning (ML) and deep learning (DL) algorithms, including supervised, unsupervised, and reinforcement learning approaches, are examined for their effectiveness in identifying zero-day attacks, network anomalies, and incident response automation. Artificial intelligence-based threat intelligence, drawing on real-time data from sensors, logs, and network traffic, increases proactive defense against advanced persistent threats (APTs). The chapter also discusses how AI is employed in safeguarding ICS components such as SCADA systems, programmable logic controllers (PLCs), and remote terminal units (RTUs).
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