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Inferring router ownership based on the classification of intra- and inter-domain links
Research on router ownership inference is central to many Internet studies, such as network failure diagnosis, network boundary identification, network resilience assessment, and inter-domain congestion detection. The existing router ownership inference method bdrmapIT has relatively few constraints...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10060407/ https://www.ncbi.nlm.nih.gov/pubmed/36991030 http://dx.doi.org/10.1038/s41598-023-32202-6 |
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author | Liu, Yan Zhao, Yi Guo, Xiaoyu Liu, Lian |
author_facet | Liu, Yan Zhao, Yi Guo, Xiaoyu Liu, Lian |
author_sort | Liu, Yan |
collection | PubMed |
description | Research on router ownership inference is central to many Internet studies, such as network failure diagnosis, network boundary identification, network resilience assessment, and inter-domain congestion detection. The existing router ownership inference method bdrmapIT has relatively few constraints on routers at the end of traceroute paths, resulting in some inference errors. In this paper, a router ownership inference method based on the classification of intra- and inter-domain links is proposed. In this method, the differentiating Internet Protocol (IP) address vector distance feature, the autonomous system relationship feature of the IP link, and the fan-in and fan-out features are designed to support the discrimination of IP link types. The use of additional information derived from the link type enriches the basis for router ownership inference and improves the accuracy of the inference result. Experimental results show that the accuracy reaches 96.4% and 94.6% on the two verification sets, respectively, which is 3.2–11.2% better than the existing typical methods. |
format | Online Article Text |
id | pubmed-10060407 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-100604072023-03-31 Inferring router ownership based on the classification of intra- and inter-domain links Liu, Yan Zhao, Yi Guo, Xiaoyu Liu, Lian Sci Rep Article Research on router ownership inference is central to many Internet studies, such as network failure diagnosis, network boundary identification, network resilience assessment, and inter-domain congestion detection. The existing router ownership inference method bdrmapIT has relatively few constraints on routers at the end of traceroute paths, resulting in some inference errors. In this paper, a router ownership inference method based on the classification of intra- and inter-domain links is proposed. In this method, the differentiating Internet Protocol (IP) address vector distance feature, the autonomous system relationship feature of the IP link, and the fan-in and fan-out features are designed to support the discrimination of IP link types. The use of additional information derived from the link type enriches the basis for router ownership inference and improves the accuracy of the inference result. Experimental results show that the accuracy reaches 96.4% and 94.6% on the two verification sets, respectively, which is 3.2–11.2% better than the existing typical methods. Nature Publishing Group UK 2023-03-29 /pmc/articles/PMC10060407/ /pubmed/36991030 http://dx.doi.org/10.1038/s41598-023-32202-6 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Liu, Yan Zhao, Yi Guo, Xiaoyu Liu, Lian Inferring router ownership based on the classification of intra- and inter-domain links |
title | Inferring router ownership based on the classification of intra- and inter-domain links |
title_full | Inferring router ownership based on the classification of intra- and inter-domain links |
title_fullStr | Inferring router ownership based on the classification of intra- and inter-domain links |
title_full_unstemmed | Inferring router ownership based on the classification of intra- and inter-domain links |
title_short | Inferring router ownership based on the classification of intra- and inter-domain links |
title_sort | inferring router ownership based on the classification of intra- and inter-domain links |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10060407/ https://www.ncbi.nlm.nih.gov/pubmed/36991030 http://dx.doi.org/10.1038/s41598-023-32202-6 |
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