Cybercrime Prediction via Geographically Weighted Learning
This work addresses cybercrime prediction with spatial data, but it is incremental as it builds on existing geographically weighted methods and uses synthetic data.
The authors tackled the problem of cybercrime prediction by proposing GeogGNN, a graph neural network that incorporates geographical coordinates to account for spatial variations, and demonstrated it achieves higher accuracy than standard neural networks and convolutional neural networks on a synthetic dataset for a 4-class classification task in cybersecurity.
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.