Metrics
1 Record Views
Conference proceeding
Convergence and Variability Assessment and Analysis of QGANs and GANs for Advanced Cybersecurity Applications: An Empirical Study
Web Information Systems Engineering - WISE 2025 PhD Symposium, Demos and Workshops: 26th International Conference, Marrakech, Morocco, December 15–17, 2025, Proceedings, Part I, pp.203-215
Web Information Systems Engineering - WISE 2025 PhD Symposium, Demos and Workshops, 26th (Marrakech, Morocco, 12/15/2025–12/17/2025)
05/2026
Abstract
In this paper, we present a comprehensive empirical study on the convergence and variability of Quantum Generative Adversarial Networks (QGANs) compared to classical Generative Adversarial Networks (GANs) for advanced cybersecurity applications. Our methodology leverages both real-life and GAN-generated synthetic datasets to systematically assess loss function stability across extensive training epochs. By integrating Quantum-inspired architectures with classical discriminators, our proposed framework enables a fine-grained investigation of Generator–Discriminator dynamics and entropy-based stability metrics. Experimental findings highlight that QGANs consistently achieve lower and more stable generator loss values than traditional GANs, demonstrating enhanced robustness and reliability for cybersecurity tasks.
Related links
Details
- Title
- Convergence and Variability Assessment and Analysis of QGANs and GANs for Advanced Cybersecurity Applications
- Publication Details
- Web Information Systems Engineering - WISE 2025 PhD Symposium, Demos and Workshops: 26th International Conference, Marrakech, Morocco, December 15–17, 2025, Proceedings, Part I, pp.203-215
- Resource Type
- Conference proceeding
- Conference
- Web Information Systems Engineering - WISE 2025 PhD Symposium, Demos and Workshops, 26th (Marrakech, Morocco, 12/15/2025–12/17/2025)
- Publisher
- Springer-Verlag; Berlin, Heidelberg
- Copyright
- © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.
- Identifiers
- 99381803746906600
- Academic Unit
- Center for Cybersecurity and AI; Hal Marcus College of Science and Engineering
- Language
- English