Abstract
The growing importance of cybersecurity, particularly in network environments, necessitates advanced incident detection systems. Machine learning-based classification algorithms are crucial for identifying security breaches within network traffic. However, datasets representing this problem are typically highly imbalanced, which hinders the performance of standard classifiers. To address this issue, various oversampling techniques are commonly employed to increase the representation of the minority class, combined with an undersampling algorithm designed to refine the dataset by selecting the most informative minority class representations. This paper proposes extending this hybrid approach with a Transformer-based model. The effectiveness of the proposed approach is validated through computational experiments, demonstrating its potential for improving incident detection performance in imbalanced security scenarios.
Paper Type
Short Paper
DOI
10.62036/ISD.2026.104
Transformer-Enhanced Hybrid Sampling for Synthetic Data Generation in Imbalanced Network Security Datasets
The growing importance of cybersecurity, particularly in network environments, necessitates advanced incident detection systems. Machine learning-based classification algorithms are crucial for identifying security breaches within network traffic. However, datasets representing this problem are typically highly imbalanced, which hinders the performance of standard classifiers. To address this issue, various oversampling techniques are commonly employed to increase the representation of the minority class, combined with an undersampling algorithm designed to refine the dataset by selecting the most informative minority class representations. This paper proposes extending this hybrid approach with a Transformer-based model. The effectiveness of the proposed approach is validated through computational experiments, demonstrating its potential for improving incident detection performance in imbalanced security scenarios.
Recommended Citation
Dampc, M., Czarnowski, I. & Jędrzejowicz, P.(2026). Transformer-Enhanced Hybrid Sampling for Synthetic Data Generation in Imbalanced Network Security Datasets. In M. Valenta, B. Mannová, R. Pergl, A. Przybylek, M. Lang, H. Linger, C. Schneider, N. Iivari, & E. Insfran (Eds.), Making ISD Sustainable: Reloaded with AI and Automation (ISD2026 Proceedings). Prague, Czech Republic: Czech Technical University in Prague. ISBN: 978-80-01-07585-2. https://doi.org/10.62036/ISD.2026.104