  The network intrusion detection task is challenging because of the imbalanced
and unlabeled nature of the dataset it operates on. Existing generative
adversarial networks (GANs), are primarily used for creating synthetic samples
from reals. They also have been proved successful in anomaly detection tasks.
In our proposed method, we construct the trained encoder-discriminator as a
one-class classifier based on Bidirectional GAN (Bi-GAN) for detecting
anomalous traffic from normal traffic other than calculating expensive and
complex anomaly scores or thresholds. Our experimental result illustrates that
our proposed method is highly effective to be used in network intrusion
detection tasks and outperforms other similar generative methods on the NSL-KDD
dataset.
