  As an active network security protection scheme, intrusion detection system
(IDS) undertakes the important responsibility of detecting network attacks in
the form of malicious network traffic. Intrusion detection technology is an
important part of IDS. At present, many scholars have carried out extensive
research on intrusion detection technology. However, developing an efficient
intrusion detection method for massive network traffic data is still difficult.
Since Generative Adversarial Networks (GANs) have powerful modeling
capabilities for complex high-dimensional data, they provide new ideas for
addressing this problem. In this paper, we put forward an EBGAN-based intrusion
detection method, IDS-EBGAN, that classifies network records as normal traffic
or malicious traffic. The generator in IDS-EBGAN is responsible for converting
the original malicious network traffic in the training set into adversarial
malicious examples. This is because we want to use adversarial learning to
improve the ability of discriminator to detect malicious traffic. At the same
time, the discriminator adopts Autoencoder model. During testing, IDS-EBGAN
uses reconstruction error of discriminator to classify traffic records.
