An improved BiGAN based approach for anomaly detection

Anomaly detection is considered as a challenging task due to its imbalanced and unlabelled nature. To overcome this challenge, the combination of different machine learning approaches such as supervised, unsupervised, semi-supervised learning are proposed in the literature. With the advent of neural networks and generative models, different methodologies derived from neural networks are applied to anomaly detection tasks. In this study, we use the KDDCUP99 data set, consider it as an anomaly detection task, and implement BiGAN, considering it as a one-class anomaly detection algorithm. Since generator and discriminator are highly dependent on each other in the training phase, to reduce this dependency, in this paper, we propose two different training approaches for BiGAN by adding extra training steps to it. We also demonstrate that proposed approaches increased the performance of BiGAN on anomaly detection task.