Application of machine learning to computer network security
| dc.contributor.author | Jason Haydaman | |
| dc.contributor.examiningcommittee | Ferens, Ken (Electrical and Computer Engineering) Mohammed, Noman (Computer Science) | en_US |
| dc.contributor.supervisor | Gilmore, Colin (Electrical and Computer Engineering) McLeod, Bob (Electrical and Computer Engineering) | en_US |
| dc.date.accessioned | 2017-09-11T19:41:59Z | |
| dc.date.available | 2017-09-11T19:41:59Z | |
| dc.date.issued | 2017 | |
| dc.degree.discipline | Electrical and Computer Engineering | en_US |
| dc.degree.level | Master of Science (M.Sc.) | en_US |
| 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.description.note | October 2017 | en_US |
| dc.identifier.uri | http://hdl.handle.net/1993/32543 | |
| dc.language.iso | eng | en_US |
| dc.rights | open access | 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.type | master thesis | en_US |