Natural Language machine learning methods are applied to rules generated to identify malware at the network level. These rules use a computer-based signature specification "language" called Snort. Using Natural Language processing techniques and other machine learning methods, new rules are generated based on a training set of existing Snort rule signatures for a specific type of malware family. The performance is then measured, in terms of the detection of existing types of malware and the number of "false positive" triggering events.
| Identifer | oai:union.ndltd.org:uottawa.ca/oai:ruor.uottawa.ca:10393/43749 |
| Date | 04 July 2022 |
| Creators | Laryea, Ebenezer Nii Afotey |
| Contributors | Knox, David A. |
| Publisher | Université d'Ottawa / University of Ottawa |
| Source Sets | Université d’Ottawa |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | application/pdf |
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