A Street-Level IP Geolocation Method Based on Delay-Distance Correlation and Multilayered Common Routers. (12th January 2021)

• Record Type: Journal Article
• Authors: Ding, Shichang; Zhao, Fan; Luo, Xiangyang
• Other Names: Wang Jinwei Academic Editor.
• Is Part Of: Security and communication networks. Volume 2021(2021)
• Publication Date: 2021-01-12
• Subjects: Computer networks -- Security measures -- Periodicals; Computer security -- Periodicals; Cryptography -- Periodicals; 005.805
• DOI: 10.1155/2021/6658642
• Languages: English
• ISSNs: 1939-0114

Abstract

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.