Detection And Location Of Misconfiguration Of GNN-Based IP VPN

Abstract: Configuration verification of networks, especially virtual private networks, is a complex task that needs to be done before every update of a production environment so that network providers can ensure network availability for their customers. This paper discusses a graph-based neural network (GNN) approach for detecting and locating configuration errors in IP virtual private networks (VPNS). The study focuses on two GNN models, one that focuses on routing misconfigurations between customer and provider edge routers, and the other on VPN routing misconfigurations between different provider edge routers. The goal is to provide a tool that simplifies the process of verifying an end-to-end VPN configuration.
In the study, both models were trained using a balanced dataset containing examples of tag configurations extracted from an IMSNetwork-based VPN deployment. The results show that both models show high accuracy when dealing with VPNS of different sizes (from 3 to 40 sites) and two types of architectures (full mesh and hub radiant).
The advantage of this method is that the graph neural network can capture the complex relationship between network topology and configuration, so that configuration errors can be detected more effectively. By using this technology, network providers can validate network configurations before each update to ensure network availability for their customers.