LSTM for SCADA Intrusion Detection
Jun Gao, Luyun Gan, Fabiola Buschendorf, Liao Zhang, Hua Liu, Peixue Li, Xiaodai Dong, Tao Lű · 2019
We present recurrent neural networks (RNN) for supervisory control and data acquisition (SCADA) Intrusion Detection System (IDS). Using long short term memory (LSTM) with many-to-many (MTM) and a novel many-to-one (MTO) architectures, both IDSs display excellent performance in detecting temporal uncorrelated attacks while MTO showing superior performance on temporal correlated attacks.