From Topology to Behavioral Semantics: Enhancing BGP Security by Understanding BGP's Language with LLMs

2025-11-18

AI Summary

Existing BGP anomaly detection methods rely on manual inspection or conventional machine learning, suffering from poor scalability, low accuracy, weak generalization, and high retraining costs—primarily because they model only AS-level topology while neglecting behavioral patterns and policy semantics. To address this, we propose BGPShield, the first framework to leverage large language models (LLMs) for deep BGP semantic understanding. It innovatively constructs AS behavioral profiles and policy-motivation representations. We design a segmented aggregation module coupled with a lightweight contrastive compression network, integrated with an AR-DTW semantic alignment algorithm to achieve topology-agnostic, semantically consistent representations. Evaluated on 16 real-world datasets, BGPShield achieves 100% verified anomaly detection with <5% false positive rate; it models unseen ASes in under one second without retraining—significantly outperforming state-of-the-art baselines such as BEAM.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

The trust-based nature of Border Gateway Protocol (BGP) makes it vulnerable to disruptions like prefix hijacking and misconfigurations, threatening routing stability. Traditional detection relies on manual inspection with limited scalability. Machine/Deep Learning (M/DL) approaches automate detection but suffer from suboptimal precision, limited generalizability, and high retraining costs. This is because existing methods focus on topological structures rather than comprehensive semantic characteristics of Autonomous Systems (ASes), often misinterpreting functionally similar but topologically distant ASes. To address this, we propose BGPShield, an anomaly detection framework built on LLM embeddings that captures the Behavior Portrait and Routing Policy Rationale of each AS beyond topology, such as operational scale and global role. We propose a segment-wise aggregation scheme to transform AS descriptions into LLM representations without information loss, and a lightweight contrastive reduction network to compress them into a semantic-consistent version. Using these representations, our AR-DTW algorithm aligns and accumulates semantic distances to reveal behavioral inconsistencies. Evaluated on 16 real-world datasets, BGPShield detects 100% of verified anomalies with a false discovery rate below 5%. Notably, the employed LLMs were released prior to evaluation events, verifying generalizability. Furthermore, BGPShield constructs representations for unseen ASes within one second, significantly outperforming BEAM which demands costly retraining (averaging 65 hours).

Problem

• BGP security vulnerabilities from prefix hijacking and misconfigurations threaten routing stability
• Traditional detection methods rely on manual inspection with limited scalability
• Machine learning approaches suffer from poor precision, generalizability, and high retraining costs

Innovation

• Uses LLM embeddings to capture AS behavioral semantics
• Applies segment-wise aggregation for lossless AS representation
• Employs AR-DTW algorithm to detect behavioral inconsistencies