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Snort Rule Generation for Malware Detection Using the GPT2 Transformer

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.

Identiferoai:union.ndltd.org:uottawa.ca/oai:ruor.uottawa.ca:10393/43749
Date04 July 2022
CreatorsLaryea, Ebenezer Nii Afotey
ContributorsKnox, David A.
PublisherUniversité d'Ottawa / University of Ottawa
Source SetsUniversité d’Ottawa
LanguageEnglish
Detected LanguageEnglish
TypeThesis
Formatapplication/pdf

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