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IP geolocation aims at locating the geographical position of Internetdevices, which plays an essential role in many Internet applications. In thisfield, a long-standing challenge is how to find a large number ofhighly-reliable landmarks, which is the key to improve the precision of IPgeolocation. To this end, many efforts have been made, while many IPgeolocation methods still suffer from unacceptable error distance because ofthe lack of landmarks. In this paper, we propose a novel IP geolocation system,named XLBoost-Geo, which focuses on enhancing the number and the density ofhighly reliable landmarks. The main idea is to extract location-indicatingclues from web pages and locating the web servers based on the clues. Based onthe landmarks, XLBoost-Geo is able to geolocate arbitrary IPs with little errordistance. Specifically, we first design an entity extracting method based on abidirectional LSTM neural network with a self-adaptive loss function (LSTM-Ada)to extract the location-indicating clues on web pages and then generatelandmarks based on the clues. Then, by measurements on network latency andtopology, we estimate the closest landmark and associate the coordinate of thelandmark with the location of the target IP. The results of our experimentsclearly validate the effectiveness and efficiency of the extracting method, theprecision, number, coverage of the landmarks, and the precision of the IPgeolocation. On RIPE Atlas nodes, XLBoost-Geo achieves 2,561m median errordistance, which outperforms SLG and IPIP.

XLBoost-Geo: An IP Geolocation System Based on Extreme Landmark Boosting