Byeongcheon Lee1, Sangmin Kim1, Muazzam Maqsood2, Jihoon Moon3,*, Seungmin Rho1,4,*
CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 1275-1300, 2024, DOI:10.32604/cmc.2024.054826
- 15 October 2024
Abstract In the context of rapid digitization in industrial environments, how effective are advanced unsupervised learning models, particularly hybrid autoencoder models, at detecting anomalies in industrial control system (ICS) datasets? This study is crucial because it addresses the challenge of identifying rare and complex anomalous patterns in the vast amounts of time series data generated by Internet of Things (IoT) devices, which can significantly improve the reliability and safety of these systems. In this paper, we propose a hybrid autoencoder model, called ConvBiLSTM-AE, which combines convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) to More >
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