Title :
Mapping the Internet: Geolocating Routers by Using Machine Learning
Author :
Prieditis, Armand ; Gang Chen
Author_Institution :
Neustar Labs., Mountain View, CA, USA
Abstract :
Knowing the geolocation of a router can help to predict the geolocation of an Internet user, which is important for local advertising, fraud detection, and geo-fencing applications. For example, the geolocation of the last router on the path to a user is a reasonable guess for the user\´s geolocation. Current methods for geolocating a router are based on parsing a router\´s name to find geographic hints. Unfortunately, these methods are noisy and often provide no hints. This paper presents results on using machine learning methods to "sharpen" a router\´s noisy location based on the time delay between one or more routers and a target router or end user IP address. The novelty of this approach is that geolocation of the one or more routers is not required to be known.
Keywords :
Internet; geography; learning (artificial intelligence); Internet; fraud detection; geo-fencing; local advertising; machine learning methods; time delay; Clustering algorithms; Computers; Geology; IP networks; Internet; Noise measurement; Training; Clustering; Geolocation; Machine Learning; Prediction;
Conference_Titel :
Computing for Geospatial Research and Application (COM.Geo), 2013 Fourth International Conference on
Conference_Location :
San Jose, CA
DOI :
10.1109/COMGEO.2013.17