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paperAugust 2026Unreviewed

The Surprising Effectiveness of LLMs in BGP Security: Mining An Unprecedented Amount of Incidents and Boosting Anomaly Detection

Libin Liu, Wenzhou Yang, Li Chen, Dan Li, Xiuting Xu

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Abstract

Border Gateway Protocol (BGP) security is critical to Internet infrastructure, yet progress in routing anomaly detection has been limited by the scarcity of publicly available incident datasets, which contain only 18 recorded cases. We observe that public operator mailing lists, e.g., NANOG and AusNOG, contain abundant yet largely untapped reports of real-world routing anomalies. To leverage this source, we develop an LLM-assisted extraction pipeline that identifies 244 candidate incidents from

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Monitoring & Detection

Cite

@misc{liu2026surprising,
  title = {{The Surprising Effectiveness of LLMs in BGP Security: Mining An Unprecedented Amount of Incidents and Boosting Anomaly Detection}},
  author = {Libin Liu and Wenzhou Yang and Li Chen and Dan Li and Xiuting Xu},
  year = {2026},
  month = aug,
  eprint = {2608.22812},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2608.22812}
}