Research on network intrusion detection model that integrates WGAN-GP algorithm and stacking learning module

With the development of network technology, current network intrusion detection models have effectively detected some network intrusion methods. In order to improve the detection performance of network intrusion detection models, a new network intrusion detection model combining data augmentation technology is proposed. The model incorporates the WGAN-GP data augmentation module for data balance enhancement and a stacking learning module for model classification accuracy. In the performance comparison analysis of the WGAN-GP algorithm, it was found that the accuracy and F1 value of the WGAN-GP algorithm were 98.25% and 0.792, respectively, which were superior to the comparison algorithm. The above results indicate that the detection performance of the WGAN-GP algorithm is superior to that of the comparison algorithm. Therefore, integrating the WGAN-GP algorithm into network intrusion detection models can effectively improve its intrusion detection performance and promote the development of the field of network intrusion detection. Copyright © The Author(s) 2024.