Cybercrime Prediction via Geographically Weighted Learning

TL;DR

This paper introduces GeogGNN, a graph neural network that incorporates geographical data to improve cybersecurity classification accuracy, demonstrating superior performance over standard neural networks on spatially dependent data.

Contribution

The paper proposes GeogGNN, a novel geographically weighted graph neural network that leverages spatial information for enhanced classification accuracy in cybersecurity tasks.

Findings

GeogGNN outperforms standard neural networks in accuracy.

The model speeds up learning by utilizing spatial continuity.

Mathematical proof shows GeogGNN's general applicability to spatial data.

Abstract

Inspired by the success of Geographically Weighted Regression and its accounting for spatial variations, we propose GeogGNN -- A graph neural network model that accounts for geographical latitude and longitudinal points. Using a synthetically generated dataset, we apply the algorithm for a 4-class classification problem in cybersecurity with seemingly realistic geographic coordinates centered in the Gulf Cooperation Council region. We demonstrate that it has higher accuracy than standard neural networks and convolutional neural networks that treat the coordinates as features. Encouraged by the speed-up in model accuracy by the GeogGNN model, we provide a general mathematical result that demonstrates that a geometrically weighted neural network will, in principle, always display higher accuracy in the classification of spatially dependent data by making use of spatial continuity and local averaging features.