Generative Adversarial Networks and Image-Based Malware Classification. (arXiv:2207.00421v1 [cs.CR])

For efficient malware removal, determination of malware threat levels, and
damage estimation, malware family classification plays a critical role. 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 find that the
AC-GAN discriminator is generally competitive with …

adversarial classification generative adversarial networks malware malware classification networks