Ti-iLSTM optimizes LSTM for TinyDL to detect logic-layer deception anomalies in PLC-based IWTS, reporting F1=0.983 and AUC=0.998 on SWaT with validation on WADI.
To- wardsEnhancedCybersecurityinIndustrialControlSystems:ASys- tematic Review of Context-Based Modeling, Digital Twins, and Ma- chineLearningApproaches
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Ti-iLSTM: A TinyDL Approach for Logic-Level Anomaly Detection in Industrial Water Treatment Systems
Ti-iLSTM optimizes LSTM for TinyDL to detect logic-layer deception anomalies in PLC-based IWTS, reporting F1=0.983 and AUC=0.998 on SWaT with validation on WADI.