Auxiliary-Classifier GAN for Malware Analysis
Publication Date
1-1-2022
Document Type
Contribution to a Book
Publication Title
Advances in Information Security
Volume
54
DOI
10.1007/978-3-030-97087-1_2
First Page
27
Last Page
68
Abstract
Generative adversarial networks (GAN) are a class of powerful machine learning techniques, where both a generative and discriminative model are trained simultaneously. GANs have been used, for example, to successfully generate “deep fake” images. 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 auxiliary classifier GANs (AC-GAN), and we consider the effectiveness of various techniques for classifying the resulting images. Our results indicate that the resulting multiclass classification problem is challenging, yet we can obtain strong results when restricting the problem to distinguishing between real and fake samples. While the AC-GAN generated images often appear to be very similar to real malware images, we conclude that from a deep learning perspective, the AC-GAN generated samples do not rise to the level of deep fake malware images.
Department
Computer Science
Recommended Citation
Rakesh Nagaraju and Mark Stamp. "Auxiliary-Classifier GAN for Malware Analysis" Advances in Information Security (2022): 27-68. https://doi.org/10.1007/978-3-030-97087-1_2
