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We investigate deep learning based omni intrusion detection system (IDS) forsupervisory control and data acquisition (SCADA) networks that are capable ofdetecting both temporally uncorrelated and correlated attacks. Regarding theIDSs developed in this paper, a feedforward neural network (FNN) can detecttemporally uncorrelated attacks at an {F$_{1}$} of {99.967${\pm}$0.005\%} butcorrelated attacks as low as {58${\pm}$2\%}. In contrast, long-short termmemory (LSTM) detects correlated attacks at {99.56${\pm}$0.01\%} whileuncorrelated attacks at {99.3${\pm}$0.1\%}. Combining LSTM and FNN through anensemble approach further improves the IDS performance with {F$_{1}$} of{99.68${\pm}$0.04\%} regardless the temporal correlations among the datapackets.

Omni SCADA Intrusion Detection Using Deep Learning Algorithms