Hybrid Quantum-Classical GANs for the Generation of Adversarial Network Flows

Problem
This preprint addresses the limitations of classical generative adversarial networks (GANs) in generating adversarial network traffic for testing intrusion detection systems (IDS). Classical GANs often require extensive high-dimensional datasets, suffer from mode collapse, and incur significant computational overhead. The authors propose a hybrid quantum-classical GAN (QC-GAN) framework to overcome these challenges by leveraging quantum computing capabilities to enhance the expressiveness of latent representations.

Method
The proposed QC-GAN framework integrates a variational quantum generator with a classical discriminator. The generator encodes the latent vector as a quantum state rather than sampling from classical noise vectors, which is posited to yield more expressive representations and reduce computational demands. The generator’s objective is to minimize the discriminator’s ability to differentiate between real and generated network traffic, while the discriminator aims to maximize its classification accuracy. The training utilizes the UNSW-NB15 dataset, a well-known benchmark for network traffic classification. The authors evaluate the generated flows against classical IDS models, specifically a random forest classifier and a convolutional neural network (CNN)-based classifier, to assess their effectiveness in evading detection.

Results
The QC-GAN framework demonstrated a significant improvement in generating adversarial traffic flows compared to traditional GANs. The generated flows were able to bypass the random forest classifier with a detection rate reduction of approximately 30% and the CNN-based classifier with a reduction of about 25%. These results indicate that the QC-GAN can produce more sophisticated adversarial examples that challenge existing IDS models, thereby highlighting the potential of quantum-enhanced generative models in cybersecurity applications.

Limitations
The authors acknowledge several limitations, including the reliance on a classical discriminator, which may not fully exploit the potential of quantum-generated data. Additionally, the framework assumes that the attacker has limited quantum computing resources, which may not reflect all real-world scenarios. The evaluation is also constrained to the UNSW-NB15 dataset, which may not encompass the full diversity of network traffic patterns. Furthermore, the impact of hardware-based noise on the quantum generator’s performance is not exhaustively explored, which could affect the robustness of the generated flows.

Why it matters
This work underscores the potential of hybrid quantum-classical approaches in enhancing the capabilities of generative models for cybersecurity applications. By demonstrating that quantum computing can improve the generation of adversarial network traffic, the authors pave the way for future research into quantum-resilient defense mechanisms. The findings suggest that as quantum computing technology matures, it could fundamentally alter the landscape of cybersecurity, necessitating the development of new strategies for IDS and threat detection.

Authors: Prateek Paudel, Nitin Jha, Abhishek Parakh, Mahadevan Subramaniam
Source: arXiv:2605.06629
URL: https://arxiv.org/abs/2605.06629v1