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
Nada Attar
Third Advisor
Fabio Di Troia
Keywords
Malwate data augmentation techniques, AC-GAN
Abstract
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.
Recommended Citation
Walia, Aditi, "Data Augmentation with Malware as Images" (2021). Master's Projects. 1009.
DOI: https://doi.org/10.31979/etd.v8ty-mhxt
https://scholarworks.sjsu.edu/etd_projects/1009
