  This paper presents a simple yet efficient method for an anomaly-based
Intrusion Detection System (IDS). In reality, IDSs can be defined as a
one-class classification system, where the normal traffic is the target class.
The high diversity of network attacks in addition to the need for
generalization, motivate us to propose a semi-supervised method. Inspired by
the successes of Generative Adversarial Networks (GANs) for training deep
models in semi-unsupervised setting, we have proposed an end-to-end deep
architecture for IDS. The proposed architecture is composed of two deep
networks, each of which trained by competing with each other to understand the
underlying concept of the normal traffic class. The key idea of this paper is
to compensate the lack of anomalous traffic by approximately obtain them from
normal flows. In this case, our method is not biased towards the available
intrusions in the training set leading to more accurate detection. The proposed
method has been evaluated on NSL-KDD dataset. The results confirm that our
method outperforms the other state-of-the-art approaches.
