提出噪声鲁棒的动态图入侵检测模型，提升异常识别准确率。
Noise Robust One-Class Intrusion Detection on Dynamic Graphs
- 用高斯分布参数建模网络事件，自动应对输入噪声
- 在加噪的CIC-IDS2017数据集上，噪声越高效果越优
- 适合对抗性攻击频发的实时网络监控场景
在网络入侵检测领域，输入数据受污染和噪声影响仍是关键挑战。本文提出一种概率版本的时序图网络支持向量数据描述（TGN-SVDD）模型，旨在提升噪声环境下的检测精度。通过为每个网络事件预测高斯分布参数，该模型能自然处理噪声对抗样本，相比基线模型显著增强鲁棒性。在添加合成噪声的改进版CIC-IDS2017数据集上的实验表明，随着噪声水平升高，本模型检测性能优于基线TGN-SVDD模型，验证了其在复杂干扰条件下的有效性。
原文摘要 · Abstract (English)
In the domain of network intrusion detection, robustness against contaminated and noisy data inputs remains a critical challenge. This study introduces a probabilistic version of the Temporal Graph Network Support Vector Data Description (TGN-SVDD) model, designed to enhance detection accuracy in the presence of input noise. By predicting parameters of a Gaussian distribution for each network event, our model is able to naturally address noisy adversarials and improve robustness compared to a baseline model. Our experiments on a modified CIC-IDS2017 data set with synthetic noise demonstrate significant improvements in detection performance compared to the baseline TGN-SVDD model, especially as noise levels increase.
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