  IP Geolocation databases are widely used in online services to map end user
IP addresses to their geographical locations. However, they use proprietary
geolocation methods and in some cases they have poor accuracy. We propose a
systematic approach to use publicly accessible reverse DNS hostnames for
geolocating IP addresses. Our method is designed to be combined with other
geolocation data sources. We cast the task as a machine learning problem where
for a given hostname, we generate and rank a list of potential location
candidates. We evaluate our approach against three state of the art academic
baselines and two state of the art commercial IP geolocation databases. We show
that our work significantly outperforms the academic baselines, and is
complementary and competitive with commercial databases. To aid
reproducibility, we open source our entire approach.
