AI RESEARCH
Hybrid Quantum-Classical GANs for the Generation of Adversarial Network Flows
arXiv CS.LG
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ArXi:2605.06629v1 Announce Type: new Classical generative adversarial networks (GANs) have been applied to generate adversarial network traffic capable of attacking intrusion detection systems, but they suffer from shortcomings such as the need for large amounts of high-dimensional datasets, mode collapse, and high computational overhead. In this work, we propose a hybrid quantum-classical GAN (QC-GAN) framework where a variational quantum generator is used to generate synthetic network traffic flows mimicking malicious traffic using latent representations.