  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 model is then employed to generate a
more extensive dataset consisting of realistic synthetic grayscale images.
Subsequently, this synthetic dataset is utilized to train a Convolutional
Neural Network (CNN) designed to identify previously unseen Android malware
applications. The study includes a comparative analysis of the CNN's
performance when trained on real images versus synthetic images generated by
the GAN. Furthermore, the research explores variations in performance between
the Wasserstein Generative Adversarial Network (WGAN) and the Deep
Convolutional Generative Adversarial Network (DCGAN). The investigation extends
to studying the impact of image size and malware obfuscation on the
classification model's effectiveness. The data augmentation approach
implemented in this study resulted in a notable performance enhancement of the
classification model, ranging from 1.5% to 7%, depending on the dataset. The
highest achieved F1 score reached 0.975.
  Keywords--Generative Adversarial Networks, Android Malware, Data
Augmentation, Wasserstein Generative Adversarial Network
