A Learning-Based Approach for IP Geolocation

The ability to pinpoint the geographic location of IP hosts is compelling for applications such as on-line advertising and network attack diagnosis. While prior methods can accurately identify the location of hosts in some regions of the Internet, they produce erroneous results when the delay or topology measurement on which they are based is limited. The hypothesis of our work is that the accuracy of IP geolocation can be improved through the creation of a flexible analytic framework that accommodates different types of geolocation information. In this paper, we describe a new framework for IP geolocation that reduces to a machine-learning classification problem. Our methodology considers a set of lightweight measurements from a set of known monitors to a target, and then classifies the location of that target based on the most probable geographic region given probability densities learned from a training set. For this study, we employ a Naive Bayes framework that has low computat...

• Type: Conference
• Year: 2010
• Where: PAM
• Authors: Brian Eriksson, Paul Barford, Joel Sommers, Robert Nowak
• Keywords: Computer Networks, Geolocation Accuracy, Geolocation Information, IP Geolocation, PAM 2010