Abstract
Machine learning-based Network Intrusion Detection Systems (NIDS) are critical for identifying threats in largescale networks but are severely compromised by the class imbalance inherent in real-world traffic. While Conditional Tabular GANs (CTGANs) provide a generative approach to data augmentation, existing frameworks often fail to effectively capture complex class relationships and minority patterns. This paper introduces Adaptive CTGAN, an enhanced framework improving upon the conditioning mechanism and training process of conventional CTGANs. The proposed model integrates a learnable class embedding layer to encode semantic relationships among attack categories and employs a dynamic conditional sampling strategy to adaptively prioritize difficult samples. Evaluated on the CIC-IDS-2017 benchmark using the Train-on-Synthetic, Test-on-Real (TSTR) paradigm, Adaptive CTGAN demonstrates superior synthetic data fidelity and diversity. Experimental results confirm significant improvements in minority-class detection and downstream classifier F1-scores, validating the method for robust NIDS data augmentation.