发表机构
Department of Computer Science, Universitas Ary Ginanjar（阿里甘jar大学计算机科学系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对机器学习入侵检测系统的问题，提出用BGAN增强的表格变换器框架，通过生成合成样本缓解不平衡并评估鲁棒性，实验表明该框架提升了宏F1分数、增强了鲁棒性且保持低误触发率，为对抗网络环境提供有效入侵检测方案。
AI 中文摘要
基于机器学习的入侵检测系统常受类别不平衡和对抗攻击影响，导致检测性能下降和鲁棒性降低。本研究提出一种由边界寻求生成对抗网络（BGAN）增强的表格变换器框架，用于基于CICIDS2017数据集的基于流的入侵检测。BGAN通过生成合成少数类样本缓解数据不平衡，并生成对抗样本评估模型鲁棒性。实验结果表明，BGAN增强将表格变换器的宏F1分数从82.96%提高到86.50%，对Web_Attack的F1分数提升最大。鲁棒性评估显示，所有未增强模型在对抗测试中性能下降率为100%，而所有BGAN增强模型的性能下降率为负，表明弹性增强。此外，增强的表格变换器在所有噪声水平上保持稳定且低的误触发率（1.51%-2.92%）。这些发现表明，BGAN持续增强类别平衡和对抗鲁棒性，所提出的BGAN-表格变换器框架为对抗网络环境提供了有效且自适应的入侵检测解决方案。
英文摘要
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 improvement observed for Web_Attack (F1 score: 0.29 to 0.61). Robustness evaluation shows that all non-augmented models experienced a 100% Performance Drop Rate (PDR) under adversarial testing, whereas all BGAN-augmented models achieved negative PDR values, indicating improved resilience. Furthermore, the augmented TabTransformer maintained stable and low False Triggered Rate (FTR) values (1.51%-2.92%) across all noise levels, compared with the BGAN-augmented Decision Tree, which reached 49.09% under benign perturbations. These findings demonstrate that BGAN consistently enhances both class balance and adversarial robustness, while the proposed BGAN-TabTransformer framework provides an effective and adaptive intrusion detection solution for adversarial network environments.
CommentsPreprint. Submitted to Kuwait Journal of Science
