AI-driven real-time anomaly detection in industrial cyber-physical systems using hybrid deep learning with SWaT and WADI benchmarks

Cyber-Physical Systems (CPSs) are increasingly exposed to sophisticated cyberattacks, necessitating the development of robust, real-time anomaly detection solutions to maintain system reliability and operational continuity. This study proposes a hybrid deep learning approach that combines CNN, BiLST...

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主要な著者:	Smilarubavathy G, Nidhya R, Pavithra D, V B Thurai Raaj, Keerthana S M
フォーマット:	Artigo
言語:	Inglês
出版事項:	Elsevier 2026-06-01
シリーズ:	Franklin Open
主題:	Cyber-physical systems (CPSs) Convolutional neural network (CNN) Bidirectional long short-term memory (BiLSTM) Attention mechanism SWaT WADI
オンライン･アクセス:	http://www.sciencedirect.com/science/article/pii/S2773186326001295
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