Oversampling Imbalanced Data Based on Convergent WGAN for Network Threat Detection

Abstract

Class imbalance is a common problem in network threat detection. Oversampling the minority class is regarded as a popular countermeasure by generating enough new minority samples. Generative adversarial network (GAN) is a typical generative model that can generate any number of artificial minority samples, which are close to the real data. However, it is difficult to train GAN, and the Nash equilibrium is almost impossible to achieve. Therefore, in order to improve the training stability of GAN for oversampling to detect the network threat, a convergent WGAN-based oversampling model called convergent WGAN (CWGAN) is proposed in this paper. The training process of CWGAN contains multiple iterations. In each iteration, the training epochs of the discriminator are dynamic, which is determined by the convergence of discriminator loss function in the last two iterations. When the discriminator is trained to convergence, the generator will then be trained to generate new minority samples. The experiment results show that CWGAN not only improve the training stability of WGAN on the loss smoother and closer to 0 but also improve the performance of the minority class through oversampling, which means that CWGAN can improve the performance of network threat detection.