Improving Android Malware Detection Through Data Augmentation Using Wasserstein Generative Adversarial Networks

arXiv:2403.00890v1 Announce Type: new
Abstract: Generative Adversarial Networks (GANs) have demonstrated their versatility across various applications, including data augmentation and malware detection. This research explores the effectiveness of utilizing GAN-generated data to train a model for the detection of Android malware. Given the considerable storage requirements of Android applications, the study proposes a method to synthetically represent data using GANs, thereby reducing storage demands. The proposed methodology involves creating image representations of features extracted from an existing dataset. A GAN …

adversarial android android malware applications arxiv augmentation cs.ai cs.cr data detection gan gans generated generative generative adversarial networks malware malware detection networks requirements research storage train