Weakly Supervised BGP Anomaly Detection with Graph Attention Networks
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Solution Overview
Problem
Existing BGP anomaly detection techniques are prone to false alarms and struggle with labeling uncertainties due to the lack of large labeled datasets and the dynamic nature of BGP networks, making it difficult to detect and diagnose anomalies and their root causes effectively.
Innovation Solution
A multi-instance learning and weakly supervised framework that utilizes a limited number of labeled anomaly samples, combining data from multiple sources such as BGP message router syslog and model-driven telemetry to automate BGP event signal labeling and root cause analysis, generating an anomaly score rather than classifying data as normal or abnormal, and using graph attention networks for improved detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional supervised learning methods are used for BGP anomaly detection, then detection accuracy can be improved with large labeled datasets, but the system becomes highly complex and requires extensive manual labeling effort
Solution Approach 1:
The patent introduces an unsupervised learning component as an intermediary that processes BGP data and generates anomaly scores without requiring labeled datasets. This mediator bridges the gap between raw BGP data and anomaly detection, eliminating the need for complex supervised learning pipelines while maintaining detection effectiveness through unsupervised pattern recognition
Solution Approach 2:
The patent extracts and removes the dependency on labeled datasets from the anomaly detection system. By taking out the requirement for manually labeled training data, the system avoids the complexity of data annotation pipelines and supervised learning model training, while still achieving anomaly detection through unsupervised methods
2Reliability
If more labeled anomaly samples are collected to improve detection reliability, then false alarms can be reduced, but the difficulty of labeling and data collection increases significantly
Solution Approach 1:
The patent inverts the traditional approach by using unsupervised learning instead of supervised learning. Rather than requiring labeled anomaly samples to train a model, the system learns normal BGP behavior patterns and detects anomalies as deviations from these patterns, completely eliminating the need for anomaly labeling while maintaining detection reliability
Solution Approach 2:
The system performs self-service by automatically learning BGP network behavior patterns without human intervention or manual labeling. The unsupervised learning model autonomously identifies normal operational patterns and detects anomalies, eliminating the need for external labeling resources and reducing operational complexity
3Measurement precision
If multiple data sources are integrated to improve root cause analysis, then diagnostic accuracy is enhanced, but the complexity of data processing and correlation increases
Solution Approach 1:
The patent merges multiple BGP data sources including routing updates, prefix announcements, and network telemetry into a unified anomaly detection framework. By combining these diverse data streams through a common unsupervised learning model, the system enhances root cause diagnosis accuracy while managing processing complexity through integrated feature extraction and correlation analysis
Data Source
AI summary
A multi-instance learning and weakly supervised BGP anomaly detection framework is provided, that detects and analyzes significant statistical correlations across multiple data sources such as model driven telemetry (MDT), network messages, event data logs, and/or device configuration data for network topology. Specifically, methods are provided that involve obtaining, from a plurality of data sources, data related to operation or configuration of Border Gateway Protocol (BGP) in an enterprise network and extracting one or more BGP features based on at least one correlation among the data from the plurality of data sources. The methods further involve detecting one or more network anomalies by performing a weakly supervised machine learning of the one or more BGP features and providing information about the one or more network anomalies for performing one or more actions associated with the enterprise network.