An FNN-LSTM ensemble detects both temporally correlated and uncorrelated attacks in a simulated SCADA network with a reported macro F1 of 99.68%, but the evaluation splits time-series data randomly, which risks inflated accuracy.
Research on accident inversion and analysis method of the oil and gas pipeline SCADA system,
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Omni SCADA Intrusion Detection Using Deep Learning Algorithms
An FNN-LSTM ensemble detects both temporally correlated and uncorrelated attacks in a simulated SCADA network with a reported macro F1 of 99.68%, but the evaluation splits time-series data randomly, which risks inflated accuracy.