  IP geolocation aims at locating the geographical position of Internet
devices, which plays an essential role in many Internet applications. In this
field, a long-standing challenge is how to find a large number of
highly-reliable landmarks, which is the key to improve the precision of IP
geolocation. To this end, many efforts have been made, while many IP
geolocation methods still suffer from unacceptable error distance because of
the 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 of
highly reliable landmarks. The main idea is to extract location-indicating
clues from web pages and locating the web servers based on the clues. Based on
the landmarks, XLBoost-Geo is able to geolocate arbitrary IPs with little error
distance. Specifically, we first design an entity extracting method based on a
bidirectional LSTM neural network with a self-adaptive loss function (LSTM-Ada)
to extract the location-indicating clues on web pages and then generate
landmarks based on the clues. Then, by measurements on network latency and
topology, we estimate the closest landmark and associate the coordinate of the
landmark with the location of the target IP. The results of our experiments
clearly validate the effectiveness and efficiency of the extracting method, the
precision, number, coverage of the landmarks, and the precision of the IP
geolocation. On RIPE Atlas nodes, XLBoost-Geo achieves 2,561m median error
distance, which outperforms SLG and IPIP.
