Boundary-Seeking GAN-Augmented TabTransformer for Adversarially Robust Intrusion Detection
Machine learning-based intrusion detection systems (IDSs) often suffer from class imbalance and vulnerability to adversarial attacks, leading to degraded detection performance and reduced robustness. This study proposes a TabTransformer framework augmented by the Boundary-Seeking Generative Adversarial Network (BGAN) for flow-based intrusion detection using the CICIDS2017 dataset. BGAN serves a dual purpose by generating synthetic minority-class samples to mitigate data imbalance and producing adversarial samples to evaluate model robustness. Experimental results demonstrate that BGAN augmentation improves TabTransformer's Macro-F1 score from 82.96% to 86.50%, with the largest class-wise ...
| Authors | Raihan Sultan Pasha Basuki, Aliyah Kurniasih |
|---|---|
| Primary category | cs.CR |
| Published | 17 July 2026 |
| Last updated at source | 17 July 2026 |
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