Noise Robust One-Class Intrusion Detection on Dynamic Graphs
Fuente:
arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866915452118106112 |
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| author | Liuliakov, Aleksei Schulz, Alexander Hermes, Luca Hammer, Barbara |
| author_facet | Liuliakov, Aleksei Schulz, Alexander Hermes, Luca Hammer, Barbara |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_14192 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Noise Robust One-Class Intrusion Detection on Dynamic Graphs Liuliakov, Aleksei Schulz, Alexander Hermes, Luca Hammer, Barbara Machine Learning 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. |
| title | Noise Robust One-Class Intrusion Detection on Dynamic Graphs |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2508.14192 |