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 F1 of 99.967±0.005% but correlated attacks as low as 58±2%. In
contrast, long-short term memory (LSTM) detects correlated attacks at 99.56±0.01%
while uncorrelated attacks at 99.3±0.1%. Combining LSTM and FNN through an
ensemble approach further improves the IDS performance with F1 of 99.68±0.04%
regardless the temporal correlations among the data packets. / Graduate
| Identifer | oai:union.ndltd.org:uvic.ca/oai:dspace.library.uvic.ca:1828/11745 |
| Date | 11 May 2020 |
| Creators | Gao, Jun |
| Contributors | Lu, Tao |
| Source Sets | University of Victoria |
| Language | English, English |
| Detected Language | English |
| Type | Thesis |
| Format | application/pdf |
| Rights | Available to the World Wide Web |
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