Malware detectors based on machine learning are vulnerable to adversarial attacks. Generative Adversarial Networks (GAN) are architectures based on Neural Networks that could produce successful adversarial samples. The interest towards this technology is quickly growing. In this paper, we propose a system that produces a feature vector for making an Android malware strongly evasive and then modify the malicious program accordingly. Such a system could have a twofold contribution: it could be used to generate datasets to validate systems for detecting GAN-based malware and to enlarge the training and testing dataset for making more robust malware classifiers.
@article{arxiv.2109.13297,
title = {GANG-MAM: GAN based enGine for Modifying Android Malware},
author = {Renjith G and Sonia Laudanna and Aji S and Corrado Aaron Visaggio and Vinod P},
journal= {arXiv preprint arXiv:2109.13297},
year = {2021}
}