Article: Research on network intrusion detection model that integrates WGAN-GP algorithm and stacking learning module Journal: International Journal of Computational Systems Engineering (IJCSYSE) 2024 Vol.8 No.6 pp.1 - 10 Abstract: 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. Inderscience Publishers - linking academia, business and industry through research

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

Authors: Xiaoli Zhou

Addresses: School of Information Engineering, Sichuan Top IT Vocational Institute, Chengdu, 610000, China

Abstract: 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.

Keywords: stacking algorithm; WGAN-GP algorithm; network ID model; WGAN algorithm; SMOTE algorithm; ADASYN algorithm.

DOI: 10.1504/IJCSYSE.2024.140760

International Journal of Computational Systems Engineering, 2024 Vol.8 No.6, pp.1 - 10

Received: 09 Jun 2023
Accepted: 22 Jul 2023
Published online: 02 Sep 2024 *

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

Keep up-to-date