Publication Date
Spring 2022
Degree Type
Master's Project
Degree Name
Master of Science (MS)
Department
Computer Science
First Advisor
Mark Stamp
Second Advisor
Fabio Di Troia
Third Advisor
Genya Ishigaki
Keywords
GANs, Malware classification
Abstract
Malware detection and analysis are important topics in cybersecurity. For efficient malware removal, determination of malware threat levels, and damage estimation, malware family classification plays a critical role. With the rise in computing power and the advent of cloud computing, deep learning models for malware analysis has gained in popularity. In this paper, we extract features from malware executable files and represent them as images using various approaches. We then focus on Generative Adversarial Networks (GAN) for multiclass classification and compare our GAN results to other popular machine learning techniques, including Support Vector Machine
(SVM), XGBoost, and Restricted Boltzmann Machines (RBM). We also evaluate the utility of the GANs generative models for adversarial attacks on image-based malware detection. We find that the AC-GAN discriminator is competitive with other machine learning techniques.
Recommended Citation
Nguyen, Huy, "Generative Adversarial Networks for Image-Based Malware Classification" (2022). Master's Projects. 1086.
DOI: https://doi.org/10.31979/etd.t84n-h6bb
https://scholarworks.sjsu.edu/etd_projects/1086
