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.
Improvingsiemforcriticalscadawaterinfrastructuresusingmachine learning,in:InternationalWorkshoponSecurityandPrivacyRequire- ments Engineering, Springer
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
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.