One-Class Intrusion Detection with Dynamic Graphs
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866916905337487360 |
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| author | Liuliakov, Aleksei Schulz, Alexander Hermes, Luca Hammer, Barbara |
| author_facet | Liuliakov, Aleksei Schulz, Alexander Hermes, Luca Hammer, Barbara |
| contents | With the growing digitalization all over the globe, the relevance of network security becomes increasingly important. Machine learning-based intrusion detection constitutes a promising approach for improving security, but it bears several challenges. These include the requirement to detect novel and unseen network events, as well as specific data properties, such as events over time together with the inherent graph structure of network communication. In this work, we propose a novel intrusion detection method, TGN-SVDD, which builds upon modern dynamic graph modelling and deep anomaly detection. We demonstrate its superiority over several baselines for realistic intrusion detection data and suggest a more challenging variant of the latter. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_12885 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | One-Class Intrusion Detection with Dynamic Graphs Liuliakov, Aleksei Schulz, Alexander Hermes, Luca Hammer, Barbara Machine Learning Artificial Intelligence With the growing digitalization all over the globe, the relevance of network security becomes increasingly important. Machine learning-based intrusion detection constitutes a promising approach for improving security, but it bears several challenges. These include the requirement to detect novel and unseen network events, as well as specific data properties, such as events over time together with the inherent graph structure of network communication. In this work, we propose a novel intrusion detection method, TGN-SVDD, which builds upon modern dynamic graph modelling and deep anomaly detection. We demonstrate its superiority over several baselines for realistic intrusion detection data and suggest a more challenging variant of the latter. |
| title | One-Class Intrusion Detection with Dynamic Graphs |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2508.12885 |