Street-Level IP Geolocation Algorithm Based on Landmarks Clustering

Existing IP geolocation algorithms based on delay similarity often rely on the principle that geographically adjacent IPs have similar delays. However, this principle is often invalid in real Internet environment, which leads to unreliable geolocation results. To improve the accuracy and reliability of locating IP in real Internet, a street-level IP geolocation algorithm based on landmarks clustering is proposed. Firstly, we use the probes to measure the known landmarks to obtain their delay vectors, and cluster landmarks using them. Secondly, the landmarks are clustered again by their latitude and longitude, and the intersection of these two clustering results is taken to form training sets. Thirdly, we train multiple neural networks to get the mapping relationship between delay and location in each training set. Finally, we determine one of the neural networks for the target by the delay similarity and relative hop counts, and then geolocate the target by this network. As it brings together the delay and geographical coordinates clustering, the proposed algorithm largely improves the inconsistency between them and enhances the mapping relationship between them. We evaluate the algorithm by a series of experiments in Hong Kong, Shanghai, Zhengzhou and New York. The experimental results show that the proposed algorithm achieves street-level IP geolocation, and comparing with existing typical street-level geolocation algorithms, the proposed algorithm improves the geolocation reliability significantly.