Snort Rule Generation for Malware Detection Using the GPT2 Transformer
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Date
2022-07-04
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Université d'Ottawa / University of Ottawa
Abstract
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.
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Keywords
GPT-2, Snort, malware detection, NLP