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Convergence and Variability Assessment and Analysis of QGANs and GANs for Advanced Cybersecurity Applications
Conference proceeding

Convergence and Variability Assessment and Analysis of QGANs and GANs for Advanced Cybersecurity Applications

Md Abdur Rahman, Alfredo Cuzzocrea and Hossain Shahriar
Web Information Systems Engineering - WISE 2025 PhD Symposium, Demos and Workshops, pp.203-215
Lecture Notes in Computer Science
Web Information Systems Engineering - WISE 2025 (2025)
2026
DOI: https://doi.org/10.1007/978-981-95-7394-3_14

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Abstract

Entropy Generative Adversarial Networks Loss Functions Quantum Computing Cybersecurity
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.

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