research-article

LandmarkMiner: Street-level Network Landmarks Mining Method for IP Geolocation

Authors:

Xiangyang LuoAuthors Info & Claims

ACM Transactions on Internet of Things, Volume 2, Issue 3

Article No.: 21, Pages 1 - 22

https://doi.org/10.1145/3457409

Published: 08 July 2021 Publication History

Abstract

High-confidence network landmarks are the basis of IP geolocation. However, existing landmarks acquisition methods had weakness such as high time cost and insufficient landmarks number. For this, LandmarkMiner, a street-level network landmarks mining method, is proposed based on service identification and domain name association. First, LandmarkMiner trains classifiers using the scanning results of IPs with known hosting service type, identifies the hosting service type of target IPs using the trained classifiers, and obtains the classified IPs’ domain names using DNS. Then, according to institutional names, a database associating institutional name with possible domain names is built by statistical relationship, which is obtained between the known institutional names and their domain names. Finally, geographical location of IP's domain name after classification is matched in the database and online maps, thereby obtaining landmarks and evaluating reliability of them. LandmarkMiner has mined 9,423 reliable street-level landmarks from 304M IPs in 18 cities. Comparing with existing methods, LandmarkMiner increases the number of reliable street-level landmarks significantly and can be applied in different network connectivity conditions.

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Cited By

Ma ZHu XLiu FLuo XZhang STai WRen GEr ZXu M(2025)Landmark-v6: A stable IPv6 landmark representation method based on multi-feature clusteringInformation Processing & Management10.1016/j.ipm.2024.10392162:1(103921)Online publication date: Jan-2025
https://doi.org/10.1016/j.ipm.2024.103921
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

LandmarkMiner: Street-level Network Landmarks Mining Method for IP Geolocation
1. Networks
  1. Network performance evaluation
    1. Network measurement
2. Security and privacy
  1. Network security
  2. Security services

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Published In

cover image ACM Transactions on Internet of Things

ACM Transactions on Internet of Things Volume 2, Issue 3

August 2021

197 pages

EISSN:2577-6207

DOI:10.1145/3474396

Editors:
Schahram Dustdar
TU Wien, Austria
,
Gian Pietro Picco
University of Trento, Italy

Issue’s Table of Contents

Copyright © 2021 ACM.

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New York, NY, United States

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ACM Journals for the Design of Smart and Connected Systems

Publication History

Published: 08 July 2021

Accepted: 01 March 2021

Revised: 01 January 2021

Received: 01 June 2020

Published in TIOT Volume 2, Issue 3

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The Science and Technology Innovation Leading Talent Program
National Natural Science Foundation of China
Plan for Scientific Innovation Talent of Henan Province

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Cited By

Ma ZHu XLiu FLuo XZhang STai WRen GEr ZXu M(2025)Landmark-v6: A stable IPv6 landmark representation method based on multi-feature clusteringInformation Processing & Management10.1016/j.ipm.2024.10392162:1(103921)Online publication date: Jan-2025
https://doi.org/10.1016/j.ipm.2024.103921
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
Xie YZhang ZLiu YChen ELi N(2023)Evaluation Method of IP Geolocation Database Based on City Delay CharacteristicsElectronics10.3390/electronics1301001513:1(15)Online publication date: 19-Dec-2023
https://doi.org/10.3390/electronics13010015
Ma ZHu XZhang SLi NLiu FZhou QWang HHu GDong Q(2023)SubvectorS_Geo: A Neural-Network-Based IPv6 Geolocation AlgorithmApplied Sciences10.3390/app1302075413:2(754)Online publication date: 5-Jan-2023
https://doi.org/10.3390/app13020754
Ma ZZhang SLi NLi THu XFeng HZhou QLiu FQuan XWang HHu GZhang SZhai YChen SZhang S(2023)GraphNEIComputer Networks: The International Journal of Computer and Telecommunications Networking10.1016/j.comnet.2023.109946235:COnline publication date: 1-Nov-2023
https://dl.acm.org/doi/10.1016/j.comnet.2023.109946
Wang SLi FYu JLai HWu SZhou W(2023)Enhancing Semantic Consistency in Linguistic Steganography via Denosing Auto-Encoder and Semantic-Constrained Huffman CodingNatural Language Processing and Chinese Computing10.1007/978-3-031-44696-2_62(799-812)Online publication date: 12-Oct-2023
https://dl.acm.org/doi/10.1007/978-3-031-44696-2_62
Wang RLi RDong WZhang ZJiang L(2022)Fine-grained identification of camera devices based on inherent featuresMathematical Biosciences and Engineering10.3934/mbe.202217319:4(3767-3786)Online publication date: 2022
https://doi.org/10.3934/mbe.2022173
Li RWang XLuo X(2022)High-accuracy model recognition method of mobile device based on weighted feature similarityScientific Reports10.1038/s41598-022-26518-y12:1Online publication date: 18-Dec-2022
https://doi.org/10.1038/s41598-022-26518-y

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