A Street-Level IP Geolocation Method Based on Delay-Distance Correlation and Multilayered Common Routers

Author:

Ding Shichang12ORCID,Zhao Fan1ORCID,Luo Xiangyang1ORCID

Affiliation:

1. State Key Laboratory of Mathematical Engineering and Advanced Computing, Zhengzhou 276800, China

2. Institute of Computer Science, University of Gottingen, Gottingen 37075, Germany

Abstract

The geographical locations of smart devices can help in providing authentication information between multimedia content providers and users in 5G networks. The IP geolocation methods can help in estimating the geographical location of these smart devices. The two key assumptions of existing IP geolocation methods are as follows: (1) the smallest relative delay comes from the nearest host; (2) the distance between hosts which share the closest common routers is smaller than others. However, the two assumptions are not always true in weakly connected networks, which may affect accuracy. We propose a novel street-level IP geolocation algorithm (Corr-SLG), which is based on the delay-distance correlation and multilayered common routers. The first key idea of Corr-SLG is to divide landmarks into different groups based on relative-delay-distance correlation. Different from previous methods, Corr-SLG geolocates the host based on the largest relative delay for the strongly negatively correlated groups. The second key idea is to introduce the landmarks which share multilayered common routers into the geolocation process, instead of only relying on the closest common routers. Besides, to increase the number of landmarks, a new street-level landmark collection method called WiFi landmark is also presented in this paper. The experiments in one province capital city of China, Zhengzhou, show that Corr-SLG can improve the geolocation accuracy remarkably in a real-world network.

Funder

National Natural Science Foundation of China

Publisher

Hindawi Limited

Subject

Computer Networks and Communications,Information Systems

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1. IP Geolocation with Adversarial Probe Mitigation;NOMS 2024-2024 IEEE Network Operations and Management Symposium;2024-05-06

2. IP2vec: an IP node representation model for IP geolocation;Frontiers of Computer Science;2023-12-28

3. HGL_GEO: Finer-grained IPv6 geolocation algorithm based on hypergraph learning;Information Processing & Management;2023-11

4. SubvectorS_Geo: A Neural-Network-Based IPv6 Geolocation Algorithm;Applied Sciences;2023-01-05

5. GNN-Geo: A Graph Neural Network-based Fine-grained IP geolocation Framework;IEEE Transactions on Network Science and Engineering;2023

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