Publication Date

Spring 2021

Degree Type

Master's Project

Degree Name

Master of Science in Computer Science (MSCS)


Computer Science

First Advisor

Mark Stamp

Second Advisor

Nada Attar

Third Advisor

Fabio Di Troia


Malwate data augmentation techniques, AC-GAN


Machine learning and deep learning techniques for malware detection and classifi- cation play an important role in the mitigation of cybersecurity threats. However, such techniques are often limited by a lack of data. Previous research has shown promising classification results by treating malware executables as images. In this research, we consider data augmentation using noise addition, geometric transforma- tions, and Auxiliary Classifier Generative Adversarial Networks (AC-GAN) for data augmentation of malware images. We train convolution neural networks (CNN) to verify that our generated images accurately model the original malware samples.