ViTWGAN: An Improved WGAN and Vision Transformer-Based Model for Intrusion Detection

This study proposes ViTWGAN, a novel and effective intrusion detection model designed to enhance data privacy protection by detecting malicious traffic within network flows. By improving the discriminator’s loss function, our approach reduces blind spots in the discriminator by explicitly reinforcing the learning of hard negative samples, thereby mitigating the forgetting of negative samples in the generative adversarial network. A Vision Transformer is employed as the backbone architecture for both the generator and the discriminator, while the Wasserstein distance is introduced to prevent mode collapse, enabling the generator to produce diverse normal traffic and consequently improving the discriminator’s detection capability. Extensive experiments on the NSL-KDD and CIC-DDoS2019 datasets demonstrate the superior performance of the proposed model, achieving accuracy rates of 96.45% and 99.37%, respectively. These results highlight the effectiveness of ViTWGAN as a high-performance solution for general intrusion detection systems.