A Hybrid Wasserstein GAN and Autoencoder Model for Robust Intrusion Detection in IoT

Alshehri, Mohammed S.; Saidani, Oumaima; Al Malwi, Wajdan; Asiri, Fatima; Latif, Shahid; Khattak, Aizaz Ahmad; Ahmad, Jawad

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, the model achieved an average area under the precision-recall curve is 99.8% using five-fold cross-validation and demonstrated a high detection accuracy of % when tested on independent test data. Additionally, the model is well-suited for deployment on resource-limited Internet-of-Things (IoT) devices due to its ability to detect attacks within microseconds and its small memory footprint of kB. Similarly, when trained on the IDSIoT2024 dataset, the model achieved an average PR-AUC of % and an attack detection accuracy of % on independent test data, with a memory requirement of kB. Extensive simulation results demonstrate that the proposed hybrid model effectively addresses the shortcomings of traditional GAN-based IDS approaches in terms of detection accuracy, computational efficiency, and applicability to real-world IoT environments.

Citation

Alshehri, M. S., Saidani, O., Al Malwi, W., Asiri, F., Latif, S., Khattak, A. A., & Ahmad, J. (online). A Hybrid Wasserstein GAN and Autoencoder Model for Robust Intrusion Detection in IoT. Computer Modeling in Engineering & Sciences, https://doi.org/10.32604/cmes.2025.064874

• Journal Article Type: Article
• Acceptance Date: Apr 23, 2025
• Online Publication Date: May 8, 2025
• Deposit Date: May 22, 2025
• Publicly Available Date: May 22, 2025
• Journal: Computer Modeling in Engineering & Sciences
• Print ISSN: 1526-1492
• Electronic ISSN: 1526-1506
• Publisher: Tech Science Press
• Peer Reviewed: Peer Reviewed
• DOI: https://doi.org/10.32604/cmes.2025.064874
• Keywords: Autoencoder; cybersecurity; generative adversarial network; Internet of Things; intrusion detection system