GNN-Geo: A Graph Neural Network-based Fine-grained IP geolocation Framework

TL;DR

GNN-Geo introduces a graph neural network framework for fine-grained IP geolocation, leveraging network connection data to improve accuracy over traditional rule-based and prior learning methods.

Contribution

This paper reformulates IP geolocation as a graph node regression problem and proposes a novel GNN-based framework, GNN-Geo, to enhance generalization and accuracy.

Findings

GNN-Geo outperforms rule-based and learning-based baselines in real-world networks.

The framework effectively models connection information through message passing.

Experimental results demonstrate the potential of GNNs for IP geolocation tasks.

Abstract

Rule-based fine-grained IP geolocation methods are hard to generalize in computer networks which do not follow hypothetical rules. Recently, deep learning methods, like multi-layer perceptron (MLP), are tried to increase generalization capabilities. However, MLP is not so suitable for graph-structured data like networks. MLP treats IP addresses as isolated instances and ignores the connection information, which limits geolocation accuracy. In this work, we research how to increase the generalization capability with an emerging graph deep learning method -- Graph Neural Network (GNN). First, IP geolocation is re-formulated as an attributed graph node regression problem. Then, we propose a GNN-based IP geolocation framework named GNN-Geo. GNN-Geo consists of a preprocessor, an encoder, messaging passing (MP) layers and a decoder. The preprocessor and encoder transform measurement data into the initial node embeddings. MP layers refine the initial node embeddings by modeling the connection information. The decoder maps the refined embeddings to nodes' locations and relieves the convergence problem by considering prior knowledge. The experiments in 8 real-world IPv4/IPv6 networks in North America, Europe and Asia show the proposed GNN-Geo clearly outperforms the state-of-art rule-based and learning-based baselines. This work verifies the great potential of GNN for fine-grained IP geolocation.