Utilizing a unique deep learning technique for detecting anomalies in industrial automation systems
Proceedings on Engineering Sciences(2024)
Abstract
Industrial automation systems (IASs) are utilized in vital facilities to sustain society's fundamental services. As a consequence, protecting them against terrorist operations, natural catastrophes and cyber-threats is essential. The research on techniques for identifying cyber-attacks in IAS environments is lacking. The study proposed the Stochastic Turbulent water flow optimization based restricted Boltzmann machine (STWFO-RBM) to overcome the challenges. The proposed STWFO-RBM integrates anomaly detection into the fabric of industrial automation, enhancing system resilience and responsiveness. We collected datasets from the water industry and preprocessed them through min-max normalization, and then principal component analysis was used for feature extraction. The results show that the suggested technique applies to a real-world IAS situation, with state-of-the-art accuracy of 97%, F1 score of 96%, precision of 98%, recall of 95% and 6.1s of computational time. Our proposed method is better than the average of earlier endeavors.
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Key words
industry,stochastic turbulent water flow optimization based restricted boltzmann machine (stwfo-rbm),anomalies,real-time monitoring.
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