Mobile Application Network Behavior Detection and Evaluation with WGAN and Bi-LSTM

Abstract

In this paper, we present a modeling and learning method to analyze the network behavior of mobile applications based on the Android platform. Various application system sand environmental factors are simulated in order to trigger different categories of application behaviors. The sequence of network event behavior is retrieved and classified according to the behavior sequence combination using a Bi-directional Long Short-term Memory network. The trained classifier is applied to separate Android apps in eight different categories for normal behaviors, and achieves an optimal classification accuracy of 96.89%. The trained model can further be extended for the purpose of malware detection. In addition, we use Wasserstein Generative Adversarial networks to enhance the data and thus efficiently magnify the underlying behavior features in the training dataset. This solves the problem of limited data samples and time overhead and increases the diversity of data. The accuracy of the original Bi-LSTM model is further improved by 9% across the tested categories of Android apps.