Abstract
Client-independent Internet Protocol address (IP) geolocation is a critical problem in the Internet World, of which the accuracy is based on highly reliable landmarks. However, most existing methods focus heavily on improving the location estimating method rather than improving the quality and quantity of landmarks. Without sufficient landmarks of high quality, they face difficulties when attempting to further improve accuracy. Even though some existing mining based methods dig massive landmarks from online web resources, most landmarks are of low quality because they do not make full use of these open resources. In this paper, we propose ONE-Geo, a methodology to mine highly reliable landmarks as much as possible by extracting the owner name of web servers. For a given target IP, ONE-Geo extracts the real owner name from web page information and registration records. Utilizing this clue, ONE-Geo determines the correct location by searching address information on an organization knowledge graph and conduct inference. Experimental results show that ONE-Geo achieves a median error distance of 463 m on 165 web servers and a median error distance of 7.7 km on 721 nodes that do not host a website. For web servers, ONE-Geo outperforms existing methods and several commercial tools. To be specific, 66.1% nodes are geolocated by ONE-Geo with an error less than 1 km, which is two times as many as Street-level Geolocation(SLG), which is one of the best existing methods on IP geolocating.
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Acknowledgements
This work was supported in part by the National Key R&D Program of China (Grant No. 2018YFB0803402, No. 2017YFB0802804), the Key Program of National Natural Science Foundation of China (Grant No. U1766215), and the National Natural Science Foundation of China (Grant No. 61702503, No. 61702504).
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Wang, Y., Wang, X., Zhu, H., Zhao, H., Li, H., Sun, L. (2019). ONE-Geo: Client-Independent IP Geolocation Based on Owner Name Extraction. In: Biagioni, E., Zheng, Y., Cheng, S. (eds) Wireless Algorithms, Systems, and Applications. WASA 2019. Lecture Notes in Computer Science(), vol 11604. Springer, Cham. https://doi.org/10.1007/978-3-030-23597-0_28
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DOI: https://doi.org/10.1007/978-3-030-23597-0_28
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