Intrusion detection systems (IDS) often struggle to detect rare but high-impact attack behaviors due to severe class imbalance in real-world network traffic. This work proposes a class-specific GAN-based augmentation framework that explicitly targets sparsity in the minority-class in structured cybersecurity datasets. Unlike prior GAN-based approaches that employ global augmentation or anomaly-driven synthesis, separate Generative Adversarial Networks (GANs) are trained independently for each MITRE ATT&CK tactic using only real minority-class samples, enabling focused distribution learning without contamination from benign traffic. Using a relatively new network traffic dataset, UWF-ZeekData22, the proposed framework augments minority classes under conditions of extreme sample sparsity, where traditional classifiers and interpolation-based oversampling methods are ineffective or statistically unreliable. Five traditional classifiers—Logistic Regression, Support Vector Machine (SVM), k-Nearest Neighbors (KNN), Decision Tree, and Random Forest—are evaluated before and after augmentation using stratified 5-fold cross-validation. Experimental results show that class-specific GAN augmentation consistently improves recall and F1-score for rare attack tactics, with the largest gains observed under extreme sparsity where pre-augmentation evaluation was infeasible. Notably, false-negative rates are substantially reduced without degrading majority-class performance, demonstrating that the proposed approach enhances minority-class separability rather than inflating evaluation metrics. These findings demonstrate that class-specific GAN-based augmentation is a practical and robust data-level strategy for improving the detection of rare MITRE ATT&CK-aligned attack behaviors in machine-learning-based IDSs.
Debelie et al. (Thu,) studied this question.