Open Access

ARTICLE

City-Level Homogeneous Blocks Identification for IP Geolocation

Fuxiang Yuan, Fenlin Liu, Chong Liu, Xiangyang Luo^*

School of Cyberspace Security, PLA Strategic Support Force Information Engineering University, Zhengzhou, 450001, China

* Corresponding Author: Xiangyang Luo. Email:

Intelligent Automation & Soft Computing 2020, 26(6), 1403-1417. https://doi.org/10.32604/iasc.2020.011902

Received 04 June 2020; Accepted 27 June 2020; Issue published 24 December 2020

Abstract

IPs in homogeneous blocks are tightly connected and close to each other in topology and geography, which can help geolocate sensitive target IPs and maintain network security. Therefore, this manuscript proposes a city-level homogeneous blocks identification algorithm for IP geolocation. Firstly, IPs with consistent geographic location information in multiple databases and some landmarks in a specific area are obtained as targets; the /31 containing each target is used as a candidate block; vantage points are deployed to probe IPs in the candidate blocks to obtain delays and paths, and alias resolution is performed. Then, based on the analysis of paths of all IPs in blocks as well as last-hop routers of paths, conditions are set to identify homogenous blocks, and the city-level location of each homogenous block is analyzed based on the identification of city topology boundary IPs. Finally, the size of each homogeneous block is expanded step by step and the new block is identified until the largest city-level homogeneous block containing each target IP is identified. Experiments are conducted in many cities in China and the US. Results show that the proposed algorithm has a good effect on the identification of city-level homogeneous blocks, and the location accuracy of IPs in homogeneous blocks is about 99.4%. When the identified homogenous blocks are applied to target IP geolocation, the average geolocation accuracy of probing reachable target IPs is about 95.7%; when applied to landmark expansion, the number of landmarks can be greatly increased, thereby the success rate of existing geolocation algorithm such as SLG is improved.

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Keywords

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