An Optimization Method for the Geolocation Databases of Internet Hosts Based on Machine Learning

Abstract

In order to improve the accuracy and robustness of geolocation (geographic location) databases, a method based on machine learning called GeoCop (Geolocation Cop) is proposed for optimizing the geolocation databases of Internet hosts. In addition to network measurement, which is always used by the existing geolocation methods, our geolocation model for Internet hosts is also derived by both routing policy and machine learning. After optimization with the GeoCop method, the geolocation databases of Internet hosts are less prone to imperfect measurement and irregular routing. In addition to three frequently used geolocation databases (IP138, QQWry, and IPcn), we obtain two other geolocation databases by implementing two well-known geolocation methods (the constraint-based geolocation method and the topology-based geolocation method) for constructing the optimized objects. Finally, we give a comprehensive analysis on the performance of our method. On one hand, we use typical benchmarks to compare the performance of these databases after optimization; on the other hand, we also perform statistical tests to display the improvement of the GeoCop method. As presented in the comparison tables, the GeoCop method not only achieves improved performance in both accuracy and robustness but also enjoys less measurements and calculation overheads.