UTILIZING A UNIQUE DEEP LEARNING TECHNIQUE FOR DETECTING ANOMALIES IN INDUSTRIAL AUTOMATION SYSTEMS
| Autor: | Ranganathaswamy Madihalli Kenchappa, Rakesh Kumar Yadav, Alka Singh Noida, Arvind Kumar Pandey |
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| Jazyk: | angličtina |
| Rok vydání: | 2024 |
| Předmět: | |
| Zdroj: | Proceedings on Engineering Sciences, Vol 6, Iss 1, Pp 241-250 (2024) |
| Druh dokumentu: | article |
| ISSN: | 2620-2832 2683-4111 |
| DOI: | 10.24874/PES.SI.24.02.007 |
| Popis: | 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. |
| Databáze: | Directory of Open Access Journals |
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