Open Access

ARTICLE

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

Mohammed S. Alshehri^1,*, Oumaima Saidani², Wajdan Al Malwi³, Fatima Asiri³, Shahid Latif ⁴, Aizaz Ahmad Khattak⁵, Jawad Ahmad⁶

1 Department of Computer Science, College of Computer Science and Information Systems, Najran University, Najran, 61441, Saudi Arabia
2 Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P. O. Box 84428, Riyadh, 11671, Saudi Arabia
3 Department of Informatics and Computer Systems, College of Computer Science, King Khalid University, Abha, 62521, Saudi Arabia
4 School of Computing and Creative Technologies, University of the West of England, Bristol, BS16 1QY, UK
5 School of Computing, Engineering & The Built Environment, Edinburgh Napier University, 10 Colinton Road, Edinburgh, EH10 5DT, UK
6 Cybersecurity Center, Prince Mohammad Bin Fahd University, Al Khobar, 31952, Saudi Arabia

* Corresponding Author: Mohammed S. Alshehri. Email:

(This article belongs to the Special Issue: Emerging Technologies in Information Security )

Computer Modeling in Engineering & Sciences 2025, 143(3), 3899-3920. https://doi.org/10.32604/cmes.2025.064874

Received 26 February 2025; Accepted 23 April 2025; Issue published 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, 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.

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