Semi-Supervised Quantum Generative Adversarial Network (sQGAN) for Attack Detection

• Diego Abreu UFPA http://orcid.org/0000-0001-9221-7853
• David Moura UNICAMP https://orcid.org/0000-0002-1153-3879
• Christian Rothenberg UNICAMP https://orcid.org/0000-0003-3109-4305
• Antônio Abelém UFPA https://orcid.org/0000-0003-4085-6674

DOI: https://doi.org/10.5753/sbrc.2025.5901

Abstract

The evolution of cybersecurity threats demands efficient and accurate attack detection systems, yet the scarcity of labeled data limits the use of conventional supervised models. This paper proposes a Semi-Supervised Quantum Generative Adversarial Network (sQGAN) for attack detection, combining semi-supervised learning with quantum adversarial architectures to leverage labeled and unlabeled data for improved detection in data-scarce scenarios. Key contributions include (1) a semi-supervised quantum architecture effective with limited labeled data, (2) integration of quantum-based generator and discriminator networks to enhance attack detection, and (3) an experimental study comparing sQGAN’s performance with quantum architectures. Results indicate that sQGAN offers a high F1 score and robustness in detecting attacks under challenging labeling conditions.

Keywords: Quantum Machine Learning, Generative Adversarial Network, Attack Detection

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