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arXiv · 1908.01974

Omni SCADA Intrusion Detection Using Deep Learning Algorithms

Jun Gao·Luyun Gan·Fabiola Buschendorf·Liao Zhang·Hua Liu·Peixue Li·Xiaodai Dong·Tao Lu
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Abstract

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.

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Jun Gao, Luyun Gan, Fabiola Buschendorf, Liao Zhang, Hua Liu, Peixue Li, Xiaodai Dong, Tao Lu. 2019-08-06. Omni SCADA Intrusion Detection Using Deep Learning Algorithms. https://arxiv.org/abs/1908.01974

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