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HLOC: Hints-based geolocation leveraging multiple measurement frameworks

  • Quirin Scheitle
  • , Oliver Gasser
  • , Patrick Sattler
  • , Georg Carle
  • Technical University of Munich

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

44 Scopus citations

Abstract

Geographically locating an IP address is of interest for many purposes. There are two major ways to obtain the location of an IP address: querying commercial databases or conducting latency measurements. For structural Internet nodes, such as routers, commercial databases are limited by low accuracy, while current measurement-based approaches overwhelm users with setup overhead and scalability issues. In this work we present our system HLOC, aiming to combine the ease of database use with the accuracy of latency measurements. We evaluate HLOC on a comprehensive router data set of 1.4M IPv4 and 183k IPv6 routers. HLOC first extracts location hints from rDNS names, and then conducts multi-tier latency measurements. Configuration complexity is minimized by using publicly available large-scale measurement frameworks such as RIPE Atlas. Using this measurement, we can confirm or disprove the location hints found in domain names. We publicly release HLOC's ready-to-use source code, enabling researchers to easily increase geolocation accuracy with minimum overhead.

Original languageEnglish
Title of host publicationTMA 2017 - Proceedings of the 1st Network Traffic Measurement and Analysis Conference
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9783901882951
DOIs
StatePublished - 4 Aug 2017
Event1st Network Traffic Measurement and Analysis Conference, TMA 2017 - Dublin, Ireland
Duration: 21 Jun 201723 Jun 2017

Publication series

NameTMA 2017 - Proceedings of the 1st Network Traffic Measurement and Analysis Conference

Conference

Conference1st Network Traffic Measurement and Analysis Conference, TMA 2017
Country/TerritoryIreland
CityDublin
Period21/06/1723/06/17

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