Enhancing the Security of Industrial Cyber-Physical Systems using AI Algorithms

Abstract

Industrial cyber-physical systems (CPS) must be secure at a time when automation and linkages are crucial to business. This study uses powerful AI to make industrial CPS safer than previous approaches. The proposed response improves speed metrics, making it a good alternative for protecting these vital systems. RNNs detect unusual things, Kmeans grouping finds patterns, and Q-learning changes our responses. These systems are adept at identifying odd behavior, grouping tendencies, and altering threat responses. Our solution outperforms traditional security approaches in many key performance areas. The new method’s threat detection accuracy is $\mathbf{9 8. 2 \%}$, up from $\mathbf{9 4. 5 \%}$. The false positive rate drops by $\mathbf{4 0. 0 \%}$, reducing needless warnings and issues. This reduces the false negative rate by $\mathbf{4 3. 8 \%}$, making threat identification more sensitive. Our method’s response time has fallen by $\mathbf{3 3. 3} \%$, making it responsive to new threats. We utilize system resources more efficiently by $\mathbf{2 1. 4 \%}$. Scalability is “high,” meaning it adapts to business changes. We proposed a cutting-edge way to make commercial CPS safer. Its excellent AI algorithms defend critical industrial equipment from cyber-physical attacks. Threat detection outperforms previous techniques in accuracy, false alarms, reaction time, resource use, and growth. As businesses employ connected systems, our technique protects industrial operations effectively and preventatively.

Topics & Concepts