BEAR: BGP Event Analysis and Reporting

TL;DR

BEAR is a framework that uses large language models to automatically analyze and generate detailed reports on BGP anomalies, improving interpretability and operational insights in network management.

Contribution

This paper introduces BEAR, the first system leveraging LLMs for automated BGP anomaly explanation and synthetic data generation, enhancing interpretability and detection accuracy.

Findings

BEAR achieves 100% accuracy on real and synthetic datasets.

It outperforms Chain-of-Thought and in-context learning baselines.

BEAR provides detailed, automated BGP anomaly reports.

Abstract

The Internet comprises of interconnected, independently managed Autonomous Systems (AS) that rely on the Border Gateway Protocol (BGP) for inter-domain routing. BGP anomalies--such as route leaks and hijacks--can divert traffic through unauthorized or inefficient paths, jeopardizing network reliability and security. Although existing rule-based and machine learning methods can detect these anomalies using structured metrics, they still require experts with in-depth BGP knowledge of, for example, AS relationships and historical incidents, to interpret events and propose remediation. In this paper, we introduce BEAR (BGP Event Analysis and Reporting), a novel framework that leverages large language models (LLMs) to automatically generate comprehensive reports explaining detected BGP anomaly events. BEAR employs a multi-step reasoning process that translates tabular BGP data into detailed textual narratives, enhancing interpretability and analytical precision. To address the limited availability of publicly documented BGP anomalies, we also present a synthetic data generation framework powered by LLMs. Evaluations on both real and synthetic datasets demonstrate that BEAR achieves 100% accuracy, outperforming Chain-of-Thought and in-context learning baselines. This work pioneers an automated approach for explaining BGP anomaly events, offering valuable operational insights for network management.