Cybercrime Prediction via Geographically Weighted Learning: The Case Study of GCC

Cybercrime is becoming more sophisticated, thus raising significant challenges for both private and government sectors. 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 to combat cybercrimes. 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 (GCC) region as a case study. Results show that GeogGNN has higher accuracy than standard neural networks and convolutional neural networks, which treat the coordinates as features. Therefore, the GeogGNN model is a powerful tool for handling complex, geographically distributed data as it effectively captures the underlying structure and dependencies between data points involving complex relationships.