Cyber intrusion detection in port bulk handling operations /

dc.contributor.authorMonks, Kimberley
dc.contributor.schoolUniversity of South Australia. UniSA Stem.
dc.contributor.schoolUniSA Stem
dc.date.issued2020
dc.description1 ethesis (ix, 67 pages) :
dc.descriptioncolour illustrations.
dc.descriptionIncludes bibliographical references (pages 59-67)
dc.description.abstractPorts 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.dissertationThesis (Masters by research(Computer and Information Science))--University of South Australia, 2020.
dc.identifier.urihttps://hdl.handle.net/11541.2/146555
dc.language.isoen
dc.provenanceCopyright 2020 Kimberley Monks.
dc.subjectindustrial control system;intrusion detection
dc.subject.lcshAutomatic control
dc.subject.lcshIntrusion detection systems (Computer security)
dc.subject.lcshMarine terminals
dc.titleCyber intrusion detection in port bulk handling operations /
dc.typethesis
dcterms.accessRights506 0#$fstar $2Unrestricted online access
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ror.mmsid9916502811501831

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