Publication Date
Spring 2019
Degree Type
Master's Project
Degree Name
Master of Science (MS)
Department
Computer Science
First Advisor
Mark Stamp
Second Advisor
Katerina Potika
Third Advisor
Fabio Di Troia
Keywords
Multi-model malware detection, machine learning
Abstract
When training a machine learning model, there is likely to be a tradeoff between the accuracy of the model and the generality of the dataset. Previous research has shown that if we train a model to detect one specific malware family, we obtain stronger results as compared to a case where we train a single model on multiple diverse families. During the detection phase, it would be more efficient to have a single model that could detect multiple families, rather than having to score each sample against multiple models. In this research, we conduct experiments to quantify the relationship between the generality of the training dataset and the accuracy of the resulting model within the context of the malware detection problem.
Recommended Citation
Basole, Samanvitha, "Multifamily Malware Models" (2019). Master's Projects. 698.
DOI: https://doi.org/10.31979/etd.5uay-f34n
https://scholarworks.sjsu.edu/etd_projects/698
