Laryea, Ebenezer Nii Afotey2022-07-042022-07-042022-07-04http://hdl.handle.net/10393/43749http://dx.doi.org/10.20381/ruor-27963Natural 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.enGPT-2Snortmalware detectionNLPThesis