Using Generative Adversarial Networks to Simulate System Calls of Malicious Android Processes.

Gathering the training malware traces is restive and can be a nuisance depending on the type of malware, such as behavioral polymorphism. Generative Adversarial Networks (GANs) are well suited for these issues because they can generate synthetic data that mimic actual data. This treatise sheds detailed and thorough insights into the GAN model implemented to generate a proper training mechanism for binary classification. This paper tested tabular-based and pictorial-based models in multiple trials to determine the better one for classification. Furthermore, multiple ML-based classification techniques, such as ensemble learning, Support Vector Machines (SVMs), and Linear Regression, were tested and recorded on tabular and pictorial GANs. Tabular-wise, the ∆ RMSE data collected for Random Forest Tree with the Vanilla LeakyReLU-based GAN provided the optimal classification results. Feature interactions and a biological-inspired activation function were considered for optimizing the model. However, they were only additional tests that were not considered part of the leading paper, as testing quantity was insufficient for definitive evidence of optimization.