Always be Pre-Training: Representation Learning for Network Intrusion Detection with GNNs
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866913248080560128 |
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| author | Gu, Zhengyao Lopez, Diego Troy Alrahis, Lilas Sinanoglu, Ozgur |
| author_facet | Gu, Zhengyao Lopez, Diego Troy Alrahis, Lilas Sinanoglu, Ozgur |
| contents | Graph neural network-based network intrusion detection systems have recently demonstrated state-of-the-art performance on benchmark datasets. Nevertheless, these methods suffer from a reliance on target encoding for data pre-processing, limiting widespread adoption due to the associated need for annotated labels--a cost-prohibitive requirement. In this work, we propose a solution involving in-context pre-training and the utilization of dense representations for categorical features to jointly overcome the label-dependency limitation. Our approach exhibits remarkable data efficiency, achieving over 98% of the performance of the supervised state-of-the-art with less than 4% labeled data on the NF-UQ-NIDS-V2 dataset. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_18986 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Always be Pre-Training: Representation Learning for Network Intrusion Detection with GNNs Gu, Zhengyao Lopez, Diego Troy Alrahis, Lilas Sinanoglu, Ozgur Cryptography and Security Graph neural network-based network intrusion detection systems have recently demonstrated state-of-the-art performance on benchmark datasets. Nevertheless, these methods suffer from a reliance on target encoding for data pre-processing, limiting widespread adoption due to the associated need for annotated labels--a cost-prohibitive requirement. In this work, we propose a solution involving in-context pre-training and the utilization of dense representations for categorical features to jointly overcome the label-dependency limitation. Our approach exhibits remarkable data efficiency, achieving over 98% of the performance of the supervised state-of-the-art with less than 4% labeled data on the NF-UQ-NIDS-V2 dataset. |
| title | Always be Pre-Training: Representation Learning for Network Intrusion Detection with GNNs |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2402.18986 |