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