E-WACGAN: Enhanced Generative Model of Signaling Data Based on WGAN-GP and ACGAN

Abstract

In recent years, the generative adversarial network (GAN) has achieved outstanding performance in the image field and the derivatives of GAN, namely auxiliary classifier GAN (ACGAN) and Wasserstein GAN with gradient penalty (WGAN-GP) have also been widely used, but the GAN applications of nonimage domain are not wide. At the time when the telecommunication fraud is rampant, the signaling data of telephone contain a lot of useful information, which is helpful for distinguishing fraudulent and nonfraudulent calls. In this article, aiming at the problem of limited amount of data and information leakage in the research of telephone signaling data, we adopt WGAN-GP and ACGAN to generate analog data, which confirms distribution of true data. In order to solve the problem of category accuracy of analog data and to enhance the stability and speed of training, we proposed a new network structure for discriminator of GAN based on WGAN-GP and ACGAN. The experiments on telecommunication fraud dataset found that our method obtains better performance on signaling data than base model.