부산대학교 도서관

Autonomous systems (ASes) at the top of the global transit hierarchy play core roles in the Internet. Thus, accurately geolocating their routers is crucial for drawing credible conclusions on topics such as network resilience and traffic censorship. IP geolocation databases (DBs) are frequently used for this purpose. However, the accuracy of DBs on top AS routers has not been fully evaluated due to the lack of a comprehensive ground truth dataset. In this study, we address this gap by constructing a geolocation ground truth dataset that contains more than 12,000 router interfaces in the top 10 ASes, utilizing delay measurements from carefully selected Looking Glass vantage points. We evaluate the coverage and accuracy of 6 DBs, including 4 widely used ones and 2 new ones. Our evaluation shows that most DBs exhibit poor accuracy when geolocating top AS routers. We conduct an in-depth analysis to uncover the primary techniques behind DBs. Our investigation reveals the reasons behind the poor performance of certain DBs. Moreover, we discover that the best-performing DB heavily relies on hostnames, and all DBs perform poorly in geolocating routers without hostnames. Our dataset, which is the largest ground truth dataset of top AS routers to the best of our knowledge, will be publicly accessible to the Internet research community.