Omni SCADA intrusion detection
| dc.contributor.author | Gao, Jun | |
| dc.contributor.supervisor | Lu, Tao | |
| dc.date.accessioned | 2020-05-11T19:16:51Z | |
| dc.date.available | 2020-05-11T19:16:51Z | |
| dc.date.copyright | 2020 | en_US |
| dc.date.issued | 2020-05-11 | |
| dc.degree.department | Department of Electrical and Computer Engineering | |
| dc.degree.level | Master of Applied Science M.A.Sc. | en_US |
| dc.description.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 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. | en_US |
| dc.description.scholarlevel | Graduate | en_US |
| dc.identifier.uri | http://hdl.handle.net/1828/11745 | |
| dc.language | English | eng |
| dc.language.iso | en | en_US |
| dc.rights | Available to the World Wide Web | en_US |
| dc.subject | SCADA | en_US |
| dc.subject | Industrial control system | en_US |
| dc.subject | Modbus | en_US |
| dc.subject | LSTM | en_US |
| dc.subject | IDS | en_US |
| dc.subject | Deep learning | en_US |
| dc.subject | Recurrent neural network | en_US |
| dc.title | Omni SCADA intrusion detection | en_US |
| dc.type | Thesis | en_US |