Mohammed S. Alshehri1,*, Oumaima Saidani2, Wajdan Al Malwi3, Fatima Asiri3, Shahid Latif 4, Aizaz Ahmad Khattak5, Jawad Ahmad6
CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3899-3920, 2025, DOI:10.32604/cmes.2025.064874
- 30 June 2025
Abstract The emergence of Generative Adversarial Network (GAN) techniques has garnered significant attention from the research community for the development of Intrusion Detection Systems (IDS). However, conventional GAN-based IDS models face several challenges, including training instability, high computational costs, and system failures. To address these limitations, we propose a Hybrid Wasserstein GAN and Autoencoder Model (WGAN-AE) for intrusion detection. The proposed framework leverages the stability of WGAN and the feature extraction capabilities of the Autoencoder Model. The model was trained and evaluated using two recent benchmark datasets, 5GNIDD and IDSIoT2024. When trained on the 5GNIDD dataset,… More >