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| Main Author: | |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.24446 |
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| _version_ | 1866911183596945408 |
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| author | Dötterl, Jeremias |
| author_facet | Dötterl, Jeremias |
| contents | Internet service providers monitor their networks to detect, triage, and remediate service impairments. When an incident is detected, it is important to determine whether similar incidents have occurred in the past or are happening concurrently elsewhere in the network. Manual correlation of such incidents is infeasible due to the scale of the networks under observation, making automated correlation a necessity. This paper presents a self-supervised learning method for similarity-based correlation of network situations. Using this method, a deep neural network is trained on a large unlabeled dataset of network situations using contrastive learning. High precision achieved in experiments on real-world network monitoring data suggests that contrastive learning is a promising approach to network incident correlation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_24446 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Contrastive Learning for Correlating Network Incidents Dötterl, Jeremias Networking and Internet Architecture Machine Learning Internet service providers monitor their networks to detect, triage, and remediate service impairments. When an incident is detected, it is important to determine whether similar incidents have occurred in the past or are happening concurrently elsewhere in the network. Manual correlation of such incidents is infeasible due to the scale of the networks under observation, making automated correlation a necessity. This paper presents a self-supervised learning method for similarity-based correlation of network situations. Using this method, a deep neural network is trained on a large unlabeled dataset of network situations using contrastive learning. High precision achieved in experiments on real-world network monitoring data suggests that contrastive learning is a promising approach to network incident correlation. |
| title | Contrastive Learning for Correlating Network Incidents |
| topic | Networking and Internet Architecture Machine Learning |
| url | https://arxiv.org/abs/2509.24446 |