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GeoBLR: Dynamic IP Geolocation Method Based on Bayesian Linear Regression

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Collaborative Computing: Networking, Applications and Worksharing (CollaborateCom 2018)

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Abstract

The geographical location of dynamic IP addresses is important for network security applications. The delay-based or topology-based measurement method and the association-analysis-based method improve the median estimation accuracy, but are still affected by the limited precision (about 799 m) and the longer response time (tens of seconds), which cannot meet the location-aware applications of high-precise and real-time location requirements, especially the position of dynamic IP addresses. In this paper, we propose a novel approach for dynamic IP geolocation based on Bayesian Linear Regression, namely, GeoBLR, which exploits geolocation resources fundamentally different from existing ones. We exploit the location data that users would like to share in location sharing services for accurate and real-time geolocation of dynamic IP addresses. Experimental results show that compared to existing geolocation techniques, GeoBLR achieves (1) a median estimation error of 239 m and (2) a mean response time of 270 ms, which are valuable for accurate location-aware network security applications.

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Acknowledgment

This work was supported by the National Key R&D Program 2016, 2016YFB080 1300/2016YFB0801304.

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Authors and Affiliations

  1. Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China

    Fei Du & Yongzheng Zhang

  2. School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China

    Fei Du & Yongzheng Zhang

  3. National Internet Emergency Center, CNCERT/CC, Beijing, China

    Xiuguo Bao & Yu Wang

Authors
  1. Fei Du
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  2. Xiuguo Bao
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  3. Yongzheng Zhang
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  4. Yu Wang
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Corresponding author

Correspondence to Yongzheng Zhang .

Editor information

Editors and Affiliations

  1. Shanghai University, Shanghai, China

    Honghao Gao

  2. University of West London, London, UK

    Xinheng Wang

  3. Hangzhou Dianzi University, Hangzhou Shi, Zhejiang, China

    Yuyu Yin

  4. London South Bank University, London, UK

    Muddesar Iqbal

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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Du, F., Bao, X., Zhang, Y., Wang, Y. (2019). GeoBLR: Dynamic IP Geolocation Method Based on Bayesian Linear Regression. In: Gao, H., Wang, X., Yin, Y., Iqbal, M. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2018. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 268. Springer, Cham. https://doi.org/10.1007/978-3-030-12981-1_22

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  • DOI: https://doi.org/10.1007/978-3-030-12981-1_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-12980-4

  • Online ISBN: 978-3-030-12981-1

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