Publication Date

Spring 2019

Degree Type

Master's Project

Degree Name

Master of Science (MS)


Computer Science

First Advisor

Mark Stamp

Second Advisor

Katerina Potika

Third Advisor

Fabio Di Troia


Multi-model malware detection, machine learning


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.