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Generative Adversarial Networks and Image-Based Malware Classification
[article]
2022
arXiv
pre-print
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
arXiv:2207.00421v1
fatcat:aomfb7mx2zb4nhrxvtv5fkqlhm