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Towards street-level client-independent IP geolocation

Published: 30 March 2011 Publication History

Abstract

A highly accurate client-independent geolocation service stands to be an important goal for the Internet. Despite an extensive research effort and significant advances in this area, this goal has not yet been met. Motivated by the fact that the best results to date are achieved by utilizing additional 'hints' beyond inherently inaccurate delay-based measurements, we propose a novel geolocation method that fundamentally escalates the use of external information. In particular, many entities (e.g., businesses, universities, institutions) host their Web services locally and provide their actual geographical location on their Web-sites. We demonstrate that the information provided in this way, when combined with network measurements, represents a precious geolocation resource. Our methodology automatically extracts, verifies, utilizes, and opportunistically inflates such Web-based information to achieve high accuracy. Moreover, it overcomes many of the fundamental inaccuracies encountered in the use of absolute delay measurements. We demonstrate that our system can geolocate IP addresses 50 times more accurately than the best previous system, i.e., it achieves a median error distance of 690 meters on the corresponding data set.

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Published In

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NSDI'11: Proceedings of the 8th USENIX conference on Networked systems design and implementation
March 2011
27 pages

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  • VMware
  • NSF: National Science Foundation
  • Google Inc.
  • Infosys
  • USENIX Assoc: USENIX Assoc

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USENIX Association

United States

Publication History

Published: 30 March 2011

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  • (2019)A Large-scale Study on the Risks of the HTML5 WebAPI for Mobile Sensor-based AttacksThe World Wide Web Conference10.1145/3308558.3313539(3063-3071)Online publication date: 13-May-2019
  • (2018)How to Catch when Proxies LieProceedings of the Internet Measurement Conference 201810.1145/3278532.3278551(203-217)Online publication date: 31-Oct-2018
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  • (2017)Classification and Prediction Based Data Mining algorithms to Predict Email Marketing CampaignsProceedings of the 2nd International Conference on Computing and Wireless Communication Systems10.1145/3167486.3167520(1-5)Online publication date: 14-Nov-2017
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