  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.
