Abstract
Industrial Control Systems (ICS) play a critical role in managing infrastructure but are vulnerable to cyber-attacks. This paper presents an AI-driven Intrusion Detection System (IDS) specifically designed for ICS, utilizing a combination of supervised and unsupervised machine learning algorithms. By incorporating real-time anomaly detection and pattern recognition, the proposed IDS identifies potential intrusions while maintaining high accuracy. The experimental results show the system’s effectiveness in detecting cyber threats in real-world ICS environments, providing a scalable solution for enhancing cybersecurity in critical infrastructure.