research-article

Towards IP-based Geolocation via Fine-grained and Stable Webcam Landmarks

Authors:

Limin SunAuthors Info & Claims

WWW '20: Proceedings of The Web Conference 2020

Pages 1422 - 1432

https://doi.org/10.1145/3366423.3380216

Published: 20 April 2020 Publication History

Abstract

IP-based geolocation is essential for various location-aware Internet applications, such as online advertisement, content delivery, and online fraud prevention. Achieving accurate geolocation enormously relies on the number of high-quality (i.e., the fine-grained and stable over time) landmarks. However, the previous efforts of garnering landmarks have been impeded by the limited visible landmarks on the Internet and manual time cost. In this paper, we leverage the availability of numerous online webcams that are used to monitor physical surroundings as a rich source of promising high-quality landmarks for serving IP-based geolocation. In particular, we present a new framework called GeoCAM, which is designed to automatically generate qualified landmarks from online webcams, providing IP-based geolocation services with high accuracy and wide coverage. GeoCAM periodically monitors websites that are hosting live webcams and uses the natural language processing technique to extract the IP addresses and latitude/longitude of webcams for generating landmarks at large-scale. We develop a prototype of GeoCAM and conduct real-world experiments for validating its efficacy. Our results show that GeoCam can detect 282,902 live webcams hosted in webpages with 94.2% precision and 90.4% recall, and then generate 16,863 stable and fine-grained landmarks, which are two orders of magnitude more than the landmarks used in prior works. Thus, by correlating a large scale of landmarks, GeoCAM is able to provide a geolocation service with high accuracy and wide coverage.

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Published In

cover image ACM Conferences

WWW '20: Proceedings of The Web Conference 2020

April 2020

3143 pages

ISBN:9781450370233

DOI:10.1145/3366423

Editors:
Yennun Huang
Acadmica sinica, Taiwan
,
Irwin King
The Chinese University of Hong Kong, Hong Kong
,
Tie-Yan Liu
Microsoft Research Asia, China
,
Maarten van Steen
University of Twente, Netherlands

Copyright © 2020 ACM.

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Published: 20 April 2020

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April 20 - 24, 2020

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https://doi.org/10.1038/s41598-024-55750-x
Hong ALi YZhang HWang MAn CWang J(2023)A cheap and accurate delay-based IP Geolocation method using Machine Learning and Looking Glass2023 IFIP Networking Conference (IFIP Networking)10.23919/IFIPNetworking57963.2023.10186436(1-9)Online publication date: 12-Jun-2023
https://doi.org/10.23919/IFIPNetworking57963.2023.10186436
Ding SLuo xWang JFu X(2023)GNN-Geo: A Graph Neural Network-based Fine-grained IP geolocation FrameworkIEEE Transactions on Network Science and Engineering10.1109/TNSE.2023.3266752(1-18)Online publication date: 2023
https://doi.org/10.1109/TNSE.2023.3266752
Tai WChen BZhong TWang YChen KZhou F(2023)RIPGeo: Robust Street-Level IP Geolocation2023 24th IEEE International Conference on Mobile Data Management (MDM)10.1109/MDM58254.2023.00031(138-147)Online publication date: Jul-2023
https://doi.org/10.1109/MDM58254.2023.00031
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Liu JWang JLiu PZhu HSun L(2022)Discover the ICS Landmarks Based on Multi-stage Clue MiningWireless Algorithms, Systems, and Applications10.1007/978-3-031-19211-1_12(139-151)Online publication date: 17-Nov-2022
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https://dl.acm.org/doi/10.1109/TNET.2021.3073926

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