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City-Level IP Geolocation Algorithm Based on PoP Network Topology
Author(s) -
Shuodi Zu,
Xiangyang Luo,
Siqi Liu,
Yan Liu,
Fenlin Liu
Publication year - 2018
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2018.2878309
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
The existing city-level IP geolocation algorithms determine the location of IP by delay measurement and landmark comparison, and thus, the geolocation ability of these algorithms is affected by the delay precision and the number of landmarks. To alleviate the dependence on these conditions, a new city-level geolocation algorithm is proposed based on the PoP network topology in this paper. First, according to the distribution of one-hop delay between network nodes in different cities, the network nodes belonging to the target city are picked out from the detection path, and the landmarks are extended. Second, common anonymous route structures are used to find and merge anonymous routes in the path information. Finally, the PoP network topology inside the city is extracted through the tightly connected network nodes, recorded into the PoP database, and used for city-level geolocation. The experiment results of 35808 IP geolocations in 28 cities of China and the United States verify that the proposed algorithm still has good city-level geolocation ability when the delay accuracy is low or the number of landmarks is small, comparing with the existing typical IP geolocation algorithms LBG and SLG, the proposed algorithm improves the success rate of city-level geolocation from 74.86% and 94.14% to 97.67%.

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