Cyber intrusion detection in port bulk handling operations /
| dc.contributor.author | Monks, Kimberley | |
| dc.contributor.school | University of South Australia. UniSA Stem. | |
| dc.contributor.school | UniSA Stem | |
| dc.date.issued | 2020 | |
| dc.description | 1 ethesis (ix, 67 pages) : | |
| dc.description | colour illustrations. | |
| dc.description | Includes bibliographical references (pages 59-67) | |
| dc.description.abstract | Ports that process material in a raw or bulk form are critical to the financial well-being of many nations. This study presents a case study in the application of intrusion detection to a port’s unique context. The thesis extends the analysis of Industrial Control System (ICS) intrusion detection from that applied in other area. Machine Learning and Process Mining approaches are studied. Process Mining uses the physical movement of material through a port to monitor for cyber intrusions. Accuracy is tested using production data. Statistical correlation techniques applied to a calibration data record normal behaviour which is used to monitor a test data set for deviations. The approach provides another view into the ICS, which will complement other types of intrusion detection. | |
| dc.description.dissertation | Thesis (Masters by research(Computer and Information Science))--University of South Australia, 2020. | |
| dc.identifier.uri | https://hdl.handle.net/11541.2/146555 | |
| dc.language.iso | en | |
| dc.provenance | Copyright 2020 Kimberley Monks. | |
| dc.subject | industrial control system;intrusion detection | |
| dc.subject.lcsh | Automatic control | |
| dc.subject.lcsh | Intrusion detection systems (Computer security) | |
| dc.subject.lcsh | Marine terminals | |
| dc.title | Cyber intrusion detection in port bulk handling operations / | |
| dc.type | thesis | |
| dcterms.accessRights | 506 0#$fstar $2Unrestricted online access | |
| ror.fileinfo | 12217499740001831 13217499730001831 Monks, Kimberley - Thesis | |
| ror.mmsid | 9916502811501831 |
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