Publication Date
Spring 2021
Degree Type
Master's Project
Degree Name
Master of Science in Computer Science (MSCS)
Department
Computer Science
First Advisor
Mark Stamp
Second Advisor
Navrati Saxena
Third Advisor
Fabio Di Troia
Keywords
image based malware detection, deep fakes, AC-GAN
Abstract
A generative adversarial network (GAN) is a powerful machine learning concept where both a generative and discriminative model are trained simultaneously. A recent trend in malware research consists of treating executables as images and employing image-based analysis techniques. In this research, we generate fake malware images using GANs, and we also consider the effectiveness of GANs for malware classification. Specifically, we consider auxiliary classifier GAN (AC-GAN), which enables us to work with multiclass data. We find that AC-GAN generates malware images that cannot be reliably distinguished from real malware images. In addition, we find that the detection capabilities of AC-GAN exceeds other image-based techniques that have appeared in the literature.
Recommended Citation
Nagaraju, Rakesh, "Malware Analysis with Auxiliary-Classifier GAN" (2021). Master's Projects. 1005.
DOI: https://doi.org/10.31979/etd.6vcn-ngzc
https://scholarworks.sjsu.edu/etd_projects/1005
