Noise Robust One-Class Intrusion Detection on Dynamic Graphs

• URL: http://arxiv.org/abs/2508.14192v1
• Date: Tue, 19 Aug 2025 18:36:11 GMT
• Title: Noise Robust One-Class Intrusion Detection on Dynamic Graphs
• Authors: Aleksei Liuliakov, Alexander Schulz, Luca Hermes, Barbara Hammer,
• Abstract summary: 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.<n>Our experiments on a modified CIC-IDS 2017 data set with synthetic noise demonstrate significant improvements in detection performance compared to the baseline TGN-SVDD model, especially as noise levels increase.
• Score: 46.453758431767724
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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