Graph neural network‐enhanced auxiliary classifier generative adversarial network framework for robust intrusion detection

Abstract To address the challenges of traffic diversity and data imbalance in network intrusion detection, we propose GraphACGAN, a novel detection framework that integrates auxiliary classifier generative adversarial networks (ACGANs) with graph neural networks (GNNs). In this architecture, the GNN is embedded in the ACGAN discriminator to exploit the latent graph structures inherent to network traffic, thereby improving the model's capacity to distinguish between benign and malicious behaviors. Simultaneously, the ACGAN generator was leveraged to synthesize minority‐class attack traffic, effectively mitigating class imbalances and enhancing generalization. Comprehensive experiments conducted on three benchmark datasets, namely, NF‐BoT‐IoT‐v2, NF‐ToN‐IoT‐v2, and NF‐UNSW‐NB15‐v2, demonstrate that GraphACGAN consistently outperforms all baselines, including E‐GraphSAGE, GCN, ACGAN, LSTM, and KNN in terms of accuracy, precision, recall, and F 1 ‐score. An evaluation of resource‐limited computing platforms further demonstrates GraphACGAN's inference efficiency for real‐world deployment.