Research Article
GeoBLR: Dynamic IP Geolocation Method Based on Bayesian Linear Regression
@INPROCEEDINGS{10.1007/978-3-030-12981-1_22, author={Fei Du and Xiuguo Bao and Yongzheng Zhang and Yu Wang}, title={GeoBLR: Dynamic IP Geolocation Method Based on Bayesian Linear Regression}, proceedings={Collaborative Computing: Networking, Applications and Worksharing. 14th EAI International Conference, CollaborateCom 2018, Shanghai, China, December 1-3, 2018, Proceedings}, proceedings_a={COLLABORATECOM}, year={2019}, month={2}, keywords={Network security Dynamic IP geolocation Machine learning Bayesian Linear Regression}, doi={10.1007/978-3-030-12981-1_22} }- Fei Du
Xiuguo Bao
Yongzheng Zhang
Yu Wang
Year: 2019
GeoBLR: Dynamic IP Geolocation Method Based on Bayesian Linear Regression
COLLABORATECOM
Springer
DOI: 10.1007/978-3-030-12981-1_22
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, , 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, 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.
