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dc.contributor.supervisor Anderson, John (Computer Science) en_US
dc.contributor.author Wegner, Ryan
dc.date.accessioned 2012-10-24T20:40:49Z
dc.date.available 2012-10-24T20:40:49Z
dc.date.issued 2012-10-24
dc.identifier.uri http://hdl.handle.net/1993/9673
dc.description.abstract This 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.subject AI en_US
dc.subject Security en_US
dc.subject Malware en_US
dc.title Multi-agent malicious behaviour detection en_US
dc.degree.discipline Computer Science en_US
dc.contributor.examiningcommittee Scuse, David (Computer Science) McLeod, Robert (Electrical and Computer Engineering) Whyte, David (Government of Canada) en_US
dc.degree.level Doctor of Philosophy (Ph.D.) en_US
dc.description.note February 2013 en_US


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