Utilizing a Unique Deep Learning Technique for Detecting Anomalies in Industrial Automation Systems

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-theart 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.