  With the continuous development of industrial IoT (IIoT) technology, network
security is becoming more and more important. And intrusion detection is an
important part of its security. However, since the amount of attack traffic is
very small compared to normal traffic, this imbalance makes intrusion detection
in it very difficult. To address this imbalance, an intrusion detection system
called pretraining Wasserstein generative adversarial network intrusion
detection system (PWG-IDS) is proposed in this paper. This system is divided
into two main modules: 1) In this module, we introduce the pretraining
mechanism in the Wasserstein generative adversarial network with gradient
penalty (WGAN-GP) for the first time, firstly using the normal network traffic
to train the WGAN-GP, and then inputting the imbalance data into the
pre-trained WGAN-GP to retrain and generate the final required data. 2)
Intrusion detection module: We use LightGBM as the classification algorithm to
detect attack traffic in IIoT networks. The experimental results show that our
proposed PWG-IDS outperforms other models, with F1-scores of 99% and 89% on the
2 datasets, respectively. And the pretraining mechanism we proposed can also be
widely used in other GANs, providing a new way of thinking for the training of
GANs.
