AER-DCWGAN: Adversarial Encoder-Regularized Dual-Conditional Wasserstein GAN for Imbalanced Network Intrusion...

Class imbalance remains a major obstacle to reliable network intrusion detection, particularly in Internet of Things (IoT) and sensor-network monitoring scenarios where rare attack categories are represented by only a small number of high-dimensional traffic samples. To improve minority-class augmentation, we propose an adversarial encoder-regularized dual-conditional Wasserstein generative adversarial network (AER-DCWGAN), a class-aware latent-consistency framework operating in a normalized, feature-selected space. Unlike label-only conditional generation, AER-DCWGAN jointly models traffic features, latent codes, and class embeddings and is designed to encourage feature-, latent-, and label-conditioned consistency. The framework integrates a latent-code- and label-aware Wasserstein critic, encoder-guided reconstruction, adversarial prior alignment, and label-consistency filtering to reduce latent drifting and suppress semantically ambiguous generated samples. Experiments on NSL-KDD and CIC-IDS2017 show class-dependent effects rather than uniform improvement. On NSL-KDD, the Remote-to-Local (R2L) F1-score increases from 0.501 to 0.823, whereas the User-to-Root (U2R) F1-score increases only from 0.124 to 0.204 with a recall of 0.270, indicating that U2R detection remains weak. On CIC-IDS2017, Web Attack improves from 0.952 to 0.983, but Bot and PortScan decrease slightly from 0.828 to 0.817 and from 0.996 to 0.994, respectively. The improvement reported for Infiltration should also be interpreted cautiously because the test support is only seven samples. The controlled head-to-head comparison is restricted to the closely related WGAN-GP and AE-WGAN baselines, and the generated samples are evaluated and used only in the processed feature space; therefore, the study does not claim broad superiority over all imbalance-handling strategies or protocol-level validity of reconstructed raw traffic. Overall, AER-DCWGAN alleviates moderate class imbalance for several classes with sufficient representation, but it does not fully solve ultra-rare attack detection.