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.

References

[1]

Beautiful Soup 2004. Beautiful Soup. https://www.crummy.com/software/BeautifulSoup/. Accessed: 10-April-2019.

[2]

BGP Routing Table Analysis 1999. BGP Routing Table Analysis. http://thyme.apnic.net/. Accessed: 10-April-2019.

[3]

Common Crawl 2010. Common Crawl. https://commoncrawl.org/. Accessed: 10-April-2019.

[4]

Digital Envoy 1999. NetAcuity. https://www.digitalelement.com/solutions/. Accessed: 10-April-2019.

[5]

Xuan Feng, Qiang Li, Haining Wang, and Limin Sun. 2018. Acquisitional Rule-based Engine for Discovering Internet-of-Things Devices. In 27th USENIX Security Symposium (USENIX Security 18). USENIX Association, Baltimore, MD, 327–341. https://www.usenix.org/conference/usenixsecurity18/presentation/feng

[6]

Manaf Gharaibeh, Anant Shah, Bradley Huffaker, Han Zhang, Roya Ensafi, and Christos Papadopoulos. 2017. A Look at Router Geolocation in Public and Commercial Databases. In Proceedings of the 2017 Internet Measurement Conference(IMC ’17). ACM, New York, NY, USA, 463–469. https://doi.org/10/gfvgsq

Digital Library

[7]

Bamba Gueye, Artur Ziviani, Mark Crovella, and Serge Fdida. 2006. Constraint-Based Geolocation of Internet Hosts. IEEE/ACM Trans. Netw. 14, 6 (Dec. 2006), 1219–1232. https://doi.org/10/ffh2c2

Digital Library

[8]

C. Guo, Y. Liu, W. Shen, H. J. Wang, Q. Yu, and Y. Zhang. 2009. Mining the Web and the Internet for Accurate IP Address Geolocations. In IEEE INFOCOM 2009(INFOCOM’09). 2841–2845. https://doi.org/10/cmv5nd

[9]

Andreas Hanemann, Jeff W. Boote, Eric L. Boyd, Jérôme Durand, Loukik Kudarimoti, Roman Łapacz, D. Martin Swany, Szymon Trocha, and Jason Zurawski. 2005. PerfSONAR: A Service Oriented Architecture for Multi-domain Network Monitoring. In International Conference on Service-Oriented Computing (ICSOC). Berlin, Heidelberg, 241–254.

[10]

Bradley Huffaker, Marina Fomenkov, and kc claffy. 2014. DRoP: DNS-Based Router Positioning. SIGCOMM Comput. Commun. Rev. 44, 3 (July 2014), 5–13. https://doi.org/10/f6f3k8

[11]

IP2Location 2001. IP2Location. https://www.ip2location.com/. Accessed: 10-April-2019.

[12]

Ethan Katz-Bassett, John P. John, Arvind Krishnamurthy, David Wetherall, Thomas Anderson, and Yatin Chawathe. 2006. Towards IP Geolocation Using Delay and Topology Measurements. In Proceedings of the 6th ACM SIGCOMM Conference on Internet Measurement(IMC ’06). ACM, New York, NY, USA, 71–84. https://doi.org/10/bdnhxh

Digital Library

[13]

S. Laki, P. Mátray, P. Hága, T. Sebők, I. Csabai, and G. Vattay. 2011. Spotter: A Model Based Active Geolocation Service. In 2011 Proceedings IEEE INFOCOM(INFOCOM’11). 3173–3181. https://doi.org/10/cn225w

[14]

D. Li, J. Chen, C. Guo, Y. Liu, J. Zhang, Z. Zhang, and Y. Zhang. 2013. IP-Geolocation Mapping for Moderately Connected Internet Regions. IEEE Transactions on Parallel and Distributed Systems 24, 2 (Feb. 2013), 381–391. https://doi.org/10/gdwtgf

Digital Library

[15]

Q. Li, X. Feng, R. Wang, Z. Li, and L. Sun. 2018. Towards Fine-grained Fingerprinting of Firmware in Online Embedded Devices. In IEEE INFOCOM 2018 - IEEE Conference on Computer Communications. 2537–2545. https://doi.org/10.1109/INFOCOM.2018.8486326

[16]

H. Liu, Y. Zhang, Y. Zhou, D. Zhang, X. Fu, and K. K. Ramakrishnan. 2014. Mining Checkins from Location-Sharing Services for Client-Independent IP Geolocation. In IEEE INFOCOM 2014 - IEEE Conference on Computer Communications(INFOCOM’14). 619–627. https://doi.org/10/gfvgr6

[17]

W. Matthews and L. Cottrell. 2000. The PingER project: active Internet performance monitoring for the HENP community. IEEE Communications Magazine 38, 5 (May 2000), 130–136. https://doi.org/10.1109/35.841837

Digital Library

[18]

MaxMind, Inc 2002. MaxMind GeoIP2 Database MaxMind. https://www.maxmind.com/en/geoip2-databases/. Accessed: 10-April-2019.

[19]

MaxMind, Inc 2002. MaxMind GeoLite2 Free Downloadable Databases. https://dev.maxmind.com/geoip/geoip2/geolite2/. Accessed: 10-April-2019.

[20]

James A. Muir and Paul C. Van Oorschot. 2009. Internet Geolocation: Evasion and Counterevasion. ACM Comput. Surv. 42, 1 (Dec. 2009), 4:1–4:23. https://doi.org/10/fwhxsm

Digital Library

[21]

David Nadeau and Satoshi Sekine. 2007. A survey of named entity recognition and classification. Lingvisticae Investigationes 30, 1 (2007), 3–26.

[22]

OpenStreetMap 2004. OpenStreetMap Wiki. https://wiki.openstreetmap.org/w/index.php. Accessed: 10-April-2019.

[23]

Venkata N. Padmanabhan and Lakshminarayanan Subramanian. 2001. An Investigation of Geographic Mapping Techniques for Internet Hosts. In Proceedings of the 2001 Conference on Applications, Technologies, Architectures, and Protocols for Computer Communications(SIGCOMM ’01). ACM, New York, NY, USA, 173–185. https://doi.org/10/bmthnj

[24]

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. 2011. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research 12 (2011), 2825–2830.

Digital Library

[25]

Larry Peterson, Tom Anderson, David Culler, and Timothy Roscoe. 2003. A Blueprint for Introducing Disruptive Technology into the Internet. SIGCOMM Comput. Commun. Rev. 33, 1 (Jan. 2003), 59–64. https://doi.org/10/cp6w9p

Digital Library

[26]

Ingmar Poese, Steve Uhlig, Mohamed Ali Kaafar, Benoit Donnet, and Bamba Gueye. 2011. IP Geolocation Databases: Unreliable?SIGCOMM Comput. Commun. Rev. 41, 2 (April 2011), 53–56. https://doi.org/10/d3cbbx

Digital Library

[27]

RIPE Atlas 2016. RIPE Atlas. https://atlas.ripe.net/. Accessed: 10-April-2019.

[28]

Y. Shavitt and N. Zilberman. 2011. A Geolocation Databases Study. IEEE Journal on Selected Areas in Communications 29, 10 (Dec. 2011), 2044–2056. https://doi.org/10/dgb3d3

[29]

Tesseract 1984. Tesseract Open Source OCR Engine. https://github.com/tesseract-ocr/tesseract. Accessed: 10-April-2019.

[30]

Kristina Toutanova, Dan Klein, Christopher D. Manning, and Yoram Singer. 2003. Feature-Rich Part-of-Speech Tagging with a Cyclic Dependency Network. In Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1(NAACL ’03). Association for Computational Linguistics, Stroudsburg, PA, USA, 173–180. https://doi.org/10.3115/1073445.1073478

Digital Library

[31]

Yong Wang, Daniel Burgener, Marcel Flores, Aleksandar Kuzmanovic, and Cheng Huang. 2011. Towards Street-Level Client-Independent IP Geolocation. In Proceedings of the 8th USENIX Conference on Networked Systems Design and Implementation(NSDI’11). USENIX Association, Berkeley, CA, USA, 365–379.

Digital Library

[32]

Bernard Wong, Ivan Stoyanov, and Emin Gün Sirer. 2007. Octant: A Comprehensive Framework for the Geolocalization of Internet Hosts. In Proceedings of the 4th USENIX Conference on Networked Systems Design & Implementation(NSDI’07). USENIX Association, Berkeley, CA, USA.

Cited By

Wang XZhao DLiu XZhang ZZhao T(2025)NeighborGeo: IP geolocation based on neighborsComputer Networks10.1016/j.comnet.2024.110896257(110896)Online publication date: Feb-2025
https://doi.org/10.1016/j.comnet.2024.110896
Li RYuan XYin MLuo X(2024)Mobile IP Geolocation Based on District Anchor Without Cooperation of Users or Internet Service ProvidersIEEE/ACM Transactions on Networking10.1109/TNET.2024.347133532:6(5507-5523)Online publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1109/TNET.2024.3471335
Lin JLi CGong WSong GFan LWang ZYang J(2024)ProbeGeo: A Comprehensive Landmark Mining Framework Based on Web ContentIEEE/ACM Transactions on Networking10.1109/TNET.2024.342208932:5(4398-4413)Online publication date: Oct-2024
https://doi.org/10.1109/TNET.2024.3422089
Show More Cited By

Index Terms

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

Index terms have been assigned to the content through auto-classification.

Recommendations

IP Geolocation through Geographic Clicks
IP geolocation databases map IP addresses to their physical locations. They are used to determine the location of online users when their precise location is unavailable. These databases are vital for a number of online services, including search engine ...
IP Geolocation Using Traceroute Location Propagation and IP Range Location Interpolation
WWW '21: Companion Proceedings of the Web Conference 2021

Many online services, including search engines, content delivery networks, ad networks, and fraud detection utilize IP geolocation databases to map IP addresses to their physical locations. However, IP geolocation databases are often inaccurate. We ...
GeoCAM: An IP-Based Geolocation Service Through Fine-Grained and Stable Webcam Landmarks
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-...

Comments

Information & Contributors

Information

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.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 April 2020

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

WWW '20

Sponsor:

SIGWEB

WWW '20: The Web Conference 2020

April 20 - 24, 2020

Taipei, Taiwan

Acceptance Rates

Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

11
Total Citations
View Citations
392
Total Downloads

Downloads (Last 12 months)27
Downloads (Last 6 weeks)5

Reflects downloads up to 25 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Wang XZhao DLiu XZhang ZZhao T(2025)NeighborGeo: IP geolocation based on neighborsComputer Networks10.1016/j.comnet.2024.110896257(110896)Online publication date: Feb-2025
https://doi.org/10.1016/j.comnet.2024.110896
Li RYuan XYin MLuo X(2024)Mobile IP Geolocation Based on District Anchor Without Cooperation of Users or Internet Service ProvidersIEEE/ACM Transactions on Networking10.1109/TNET.2024.347133532:6(5507-5523)Online publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1109/TNET.2024.3471335
Lin JLi CGong WSong GFan LWang ZYang J(2024)ProbeGeo: A Comprehensive Landmark Mining Framework Based on Web ContentIEEE/ACM Transactions on Networking10.1109/TNET.2024.342208932:5(4398-4413)Online publication date: Oct-2024
https://doi.org/10.1109/TNET.2024.3422089
Li ZZhou FWang ZXu XLiu LYin G(2024)Measuring and classifying IP usage scenarios: a continuous neural trees approachScientific Reports10.1038/s41598-024-55750-x14:1Online publication date: 1-Mar-2024
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
Saquib NKapoor SKrintz CWolski RMock M(2023)GreenCoin: A Renewable Energy-Aware Cryptocurrency2023 IEEE International Conference on Cloud Engineering (IC2E)10.1109/IC2E59103.2023.00016(70-80)Online publication date: 25-Sep-2023
https://doi.org/10.1109/IC2E59103.2023.00016
Ma ZHu XLi NFeng HZhang SLi TLiu FZhou QTian ZWang HHu G(2023)HGL_GEOInformation Processing and Management: an International Journal10.1016/j.ipm.2023.10351860:6Online publication date: 1-Nov-2023
https://dl.acm.org/doi/10.1016/j.ipm.2023.103518
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
https://doi.org/10.1007/978-3-031-19211-1_12
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

HTML Format

View this article in HTML Format.

Figures

Tables

Media

View Table of Conten