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dc.contributor.supervisor Gilmore, Colin (Electrical and Computer Engineering) McLeod, Bob (Electrical and Computer Engineering) en_US
dc.contributor.author Jason Haydaman
dc.date.accessioned 2017-09-11T19:41:59Z
dc.date.available 2017-09-11T19:41:59Z
dc.date.issued 2017
dc.identifier.uri http://hdl.handle.net/1993/32543
dc.description.abstract Computer Security covers a wide array of topics, with much of the development in the field happening outside academia. We look at intrusion detection, and evaluate the effectiveness of machine learning in the development of a commercial intrusion detection system (IDS), and compare it with conventional IDS design approaches. We attempt to create novel data sets, and examine the difficulties of extracting new features from network traffic to aid machine learning based systems. Finally, we propose a novel, near-zero overhead method of associating network packets with the process identifier (pid) of their source in real-time and demonstrate a significant performance improvement over existing methods of pid labeling. en_US
dc.subject Machine learning en_US
dc.subject Computer network security en_US
dc.title Application of machine learning to computer network security en_US
dc.degree.discipline Electrical and Computer Engineering en_US
dc.contributor.examiningcommittee Ferens, Ken (Electrical and Computer Engineering) Mohammed, Noman (Computer Science) en_US
dc.degree.level Master of Science (M.Sc.) en_US
dc.description.note October 2017 en_US


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