Multi-agent malicious behaviour detection

dc.contributor.authorWegner, Ryan
dc.contributor.examiningcommitteeScuse, David (Computer Science) McLeod, Robert (Electrical and Computer Engineering) Whyte, David (Government of Canada)en_US
dc.contributor.supervisorAnderson, John (Computer Science)en_US
dc.date.accessioned2012-10-24T20:40:49Z
dc.date.available2012-10-24T20:40:49Z
dc.date.issued2012-10-24
dc.degree.disciplineComputer Scienceen_US
dc.degree.levelDoctor of Philosophy (Ph.D.)en_US
dc.description.abstractThis research presents a novel technique termed Multi-Agent Malicious Behaviour Detection. The goal of Multi-Agent Malicious Behaviour Detection is to provide infrastructure to allow for the detection and observation of malicious multi-agent systems in computer network environments. This research explores combinations of machine learning techniques and fuses them with a multi-agent approach to malicious behaviour detection that effectively blends human expertise from network defenders with modern artificial intelligence. Success of the approach depends on the Multi-Agent Malicious Behaviour Detection system's capability to adapt to evolving malicious multi-agent system communications, even as the malicious software agents in network environments vary in their degree of autonomy and intelligence. This thesis research involves the design of this framework, its implementation into a working tool, and its evaluation using network data generated by an enterprise class network appliance to simulate both a standard educational network and an educational network containing malware traffic.en_US
dc.description.noteFebruary 2013en_US
dc.identifier.urihttp://hdl.handle.net/1993/9673
dc.language.isoengen_US
dc.rightsopen accessen_US
dc.subjectAIen_US
dc.subjectSecurityen_US
dc.subjectMalwareen_US
dc.titleMulti-agent malicious behaviour detectionen_US
dc.typedoctoral thesisen_US
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