PWG-IDS: An Intrusion Detection Model for Solving Class Imbalance in IIoT Networks Using Generative Adversarial Networks

TL;DR

This paper introduces PWG-IDS, a novel intrusion detection system for IIoT networks that uses a pretraining Wasserstein GAN with gradient penalty to address class imbalance, improving detection accuracy.

Contribution

It proposes a new pretraining mechanism for WGAN-GP and integrates LightGBM for effective intrusion detection in imbalanced IIoT traffic datasets.

Findings

Achieved F1-scores of 99% and 89% on two datasets.

Pretraining WGAN-GP improves data generation for minority classes.

Pretraining mechanism can be applied to other GAN models.

Abstract

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.