Dawei Xu1,2,3, Yue Lv1, Min Wang1, Baokun Zheng4,*, Jian Zhao1,3, Jiaxuan Yu5
CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1731-1746, 2025, DOI:10.32604/cmc.2025.064833
- 09 June 2025
Abstract Network intrusion detection systems (IDS) are a prevalent method for safeguarding network traffic against attacks. However, existing IDS primarily depend on machine learning (ML) models, which are vulnerable to evasion through adversarial examples. In recent years, the Wasserstein Generative Adversarial Network (WGAN), based on Wasserstein distance, has been extensively utilized to generate adversarial examples. Nevertheless, several challenges persist: (1) WGAN experiences the mode collapse problem when generating multi-category network traffic data, leading to subpar quality and insufficient diversity in the generated data; (2) Due to unstable training processes, the authenticity of the data produced by… More >