SubvectorS_Geo: A Neural-Network-Based IPv6 Geolocation Algorithm
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Published:2023-01-05
Issue:2
Volume:13
Page:754
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ISSN:2076-3417
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Container-title:Applied Sciences
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language:en
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Short-container-title:Applied Sciences
Author:
Ma Zhaorui123, Hu Xinhao2, Zhang Shicheng2, Li Na14, Liu Fenlin1ORCID, Zhou Qinglei1ORCID, Wang Hongjian2, Hu Guangwu3ORCID, Dong Qilin2
Affiliation:
1. State Key Laboratory of Mathematical Engineering and Advanced Computing, Zhengzhou 450052, China 2. School of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China 3. Shenzhen Institute of Information Technology, School of Computer Sciences, Shenzhen 518172, China 4. School of Electronic Information Engineering, Sias University, Zhengzhou 451150, China
Abstract
IPv6 geolocation is necessary for many location-based Internet services. However, the accuracy of the current IPv6 geolocation methods including machine-learning-based or deep-learning-based location algorithms are unsatisfactory for users. Strong geographic correlation is observed for measurement path features close to the target IP, so previous methods focused more on stable paths in the vicinity of the probe. Based on this, this paper proposes a new IPv6 geolocation algorithm, SubvectorS_Geo, which is mainly divided into three steps: firstly, it filters geographically relevant routing feature codes layer by layer to approximate the fine-grained trusted region of the target; secondly, it extracts delay vectors into the trusted region; thirdly, it evaluates the vector similarity to determine the final target geolocation information. The final experiments show that the median error distance range is 7.025 km to 9.709 km on three real datasets (Shanghai, New York State, and Tokyo). Compared with the advanced method, the median distance error distance is reduced by at least 6.8% and the average error distance is reduced by at least 9.2%.
Funder
Key scientific research project plans of higher education institutions in Henan Province Guangdong Basic and Applied Basic Research Foundation Key Project of Shenzhen Municipality School-enterprise Collab-orative Innovation Project of SZIIT
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
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