A Learning-Based Approach for IP Geolocation

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The ability to pinpoint the geographic location of IP hosts is compelling for applications such as on-line advertising and network at-tack 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 the 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, the authors describe a new framework for IP geolocation that reduces to a machine-learning classification problem.