${\text{C}}^2\text{BNVAE}$: Dual-Conditional Deep Generation of Network Traffic Data for Network Intrusion Detection System Balancing

Network Intrusion Detection Systems (NIDS) face challenges due to class imbalance, affecting their ability to detect novel and rare attacks. This paper proposes a Dual-Conditional Batch Normalization Variational Autoencoder (${\text{C}}^2\text{BNVAE}$) for generating balanced and labeled network traffic data. ${\text{C}}^2\text{BNVAE}$ improves the model’s adaptability to different data categories and generates realistic category-specific data by incorporating Conditional Batch Normalization (CBN) into the Conditional Variational Autoencoder (CVAE). Experiments on the NSL-KDD dataset show the potential of ${\text{C}}^2\text{BNVAE}$ in addressing imbalance and improving NIDS performance with lower computational overhead compared to some baselines.