A Hybrid Global-Split WGAN-GP Framework for Addressing Class Imbalance in IDS Datasets

The continuously evolving cyber threat landscape necessitates not only resilient defense mechanisms but also the sustained capacity development of security personnel. However, conventional training pipelines are predominantly dependent on static real-world datasets, which fail to adequately reflect the diversity and dynamics of emerging attack tactics. To address these limitations, this study employs a Wasserstein GAN with Gradient Penalty (WGAN-GP) to synthesize realistic network traffic that preserves both temporal and statistical characteristics. Using the CIC-IDS-2017 dataset, which encompasses diverse attack scenarios including brute-force, Heartbleed, botnet, DoS/DDoS, web, and infiltration attacks, two training methodologies are proposed. The first trains a single conditional WGAN-GP on the entire dataset to capture the global distribution. The second employs multiple generators tailored to individual attack types, while sharing a discriminator pretrained on the complete traffic set, thereby ensuring consistent decision boundaries across classes. The quality of the generated traffic was evaluated using a Train on Synthetic, Test on Real (TSTR) protocol with LSTM and Random Forest classifiers, along with distribution similarity measures in the embedding space. The proposed approach achieved a classification accuracy of 97.88% and a Fréchet Inception Distance (FID) score of 3.05, surpassing baseline methods by more than one percentage point. These results demonstrate that the proposed synthetic traffic generation strategy provides advantages in scalability, diversity, and privacy, thereby enriching cyber range training scenarios and supporting the development of adaptive intrusion detection systems that generalize more effectively to evolving threats.