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Knowledge about the geographic locations of Internet routers and servers is highly valuable for research on various aspects of Internet structure, performance, economics, and security. Whereas databases for geolocation are commercially available and targeted mostly at end hosts, RIPE offers an open IPmap platform, including its single-radius engine, for geolocation of core Internet infrastructure. This paper introduces the research community to the IPmap single-radius engine and evaluates effectiveness of this method versus commercial geolocation databases NetAcuity and GeoLite2. Access to ground truth constitutes a major challenge in conducting such evaluation studies. The paper collects IP addresses for its study from three sources: virtual machines from the Ring of the Netherlands Network Operators’ Group, M-Lab Pods operated by Google, and CAIDA’s Ark monitors. The ground truth dataset is further diversified through addition of IP addresses that are small latency away from Ark monitors. The evaluation considers accuracy, coverage, and consistency of geolocation as well as effectiveness of the single-radius method for different types of autonomous systems. The paper manually analyzes a problematic case where single-radius mistakenly geolocates an IP address of a Budapest-based router to Vienna. Finally, the paper provides recommendations to both users and developers of the single-radius method and discusses limitations of the reported evaluation. The main conclusion is that the IPmap single-radius engine geolocates core Internet infrastructure more accurately than the considered commercial databases and that Internet researchers can greatly benefit from using the IPmap platform for their geolocation needs.
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Connecting the Hosts: Street-Level IP Geolocation with Graph Neural Networks
Pinpointing the geographic location of an IP address is important for a range of location-aware applications spanning from targeted advertising to fraud prevention. The majority of traditional measurement-based and recent learning-based methods either focus on the efficient employment of topology or utilize data mining to find clues of the target IP in publicly available sources. Motivated by the limitations in existing works, we propose a novel framework named GraphGeo, which provides a complete processing methodology for street-level IP geolocation with the application of graph neural networks. It incorporates IP hosts knowledge and kinds of neighborhood relationships into the graph to infer spatial topology for high-quality geolocation prediction. We explicitly consider and alleviate the negative impact of uncertainty caused by network jitter and congestion, which are pervasive in complicated network environments. Extensive evaluations across three large-scale real-world datasets demonstrate that GraphGeo significantly reduces the geolocation errors compared to the state-of-the-art methods. Moreover, the proposed framework has been deployed on the web platform as an online service for 6 months.
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- Award ID(s):
- 2030249
- PAR ID:
- 10403312
- Date Published:
- Journal Name:
- The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
- Page Range / eLocation ID:
- 4121 to 4131
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3534678.3539049
