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An improved density peaks clustering algorithm based on grid screening and mutual neighborhood degree for network anomaly detection
With the rapid development of network technologies and the increasing amount of network abnormal traffic, network anomaly detection presents challenges. Existing supervised methods cannot detect unknown attack, and unsupervised methods have low anomaly detection accuracy. Here, we propose a clusteri...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8792034/ https://www.ncbi.nlm.nih.gov/pubmed/35082307 http://dx.doi.org/10.1038/s41598-021-02038-z |
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author | Chen, Liangchen Gao, Shu Liu, Baoxu |
author_facet | Chen, Liangchen Gao, Shu Liu, Baoxu |
author_sort | Chen, Liangchen |
collection | PubMed |
description | With the rapid development of network technologies and the increasing amount of network abnormal traffic, network anomaly detection presents challenges. Existing supervised methods cannot detect unknown attack, and unsupervised methods have low anomaly detection accuracy. Here, we propose a clustering-based network anomaly detection model, and then a novel density peaks clustering algorithm DPC-GS-MND based on grid screening and mutual neighborhood degree for network anomaly detection. The DPC-GS-MND algorithm utilizes grid screening to effectively reduce the computational complexity, improves the clustering accuracy through mutual neighborhood degree, and also defines a cluster center decision value for automatically selecting cluster centers. We implement complete experiments on two real-world datasets KDDCup99 and CIC-IDS-2017, and the experimental results demonstrated that the proposed DPC-GS-MND can detect network anomaly traffic with higher accuracy and efficiency. Together, it has a good application prospect in the network anomaly detection system in complex network environments. |
format | Online Article Text |
id | pubmed-8792034 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-87920342022-01-28 An improved density peaks clustering algorithm based on grid screening and mutual neighborhood degree for network anomaly detection Chen, Liangchen Gao, Shu Liu, Baoxu Sci Rep Article With the rapid development of network technologies and the increasing amount of network abnormal traffic, network anomaly detection presents challenges. Existing supervised methods cannot detect unknown attack, and unsupervised methods have low anomaly detection accuracy. Here, we propose a clustering-based network anomaly detection model, and then a novel density peaks clustering algorithm DPC-GS-MND based on grid screening and mutual neighborhood degree for network anomaly detection. The DPC-GS-MND algorithm utilizes grid screening to effectively reduce the computational complexity, improves the clustering accuracy through mutual neighborhood degree, and also defines a cluster center decision value for automatically selecting cluster centers. We implement complete experiments on two real-world datasets KDDCup99 and CIC-IDS-2017, and the experimental results demonstrated that the proposed DPC-GS-MND can detect network anomaly traffic with higher accuracy and efficiency. Together, it has a good application prospect in the network anomaly detection system in complex network environments. Nature Publishing Group UK 2022-01-26 /pmc/articles/PMC8792034/ /pubmed/35082307 http://dx.doi.org/10.1038/s41598-021-02038-z Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This 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 Chen, Liangchen Gao, Shu Liu, Baoxu An improved density peaks clustering algorithm based on grid screening and mutual neighborhood degree for network anomaly detection |
title | An improved density peaks clustering algorithm based on grid screening and mutual neighborhood degree for network anomaly detection |
title_full | An improved density peaks clustering algorithm based on grid screening and mutual neighborhood degree for network anomaly detection |
title_fullStr | An improved density peaks clustering algorithm based on grid screening and mutual neighborhood degree for network anomaly detection |
title_full_unstemmed | An improved density peaks clustering algorithm based on grid screening and mutual neighborhood degree for network anomaly detection |
title_short | An improved density peaks clustering algorithm based on grid screening and mutual neighborhood degree for network anomaly detection |
title_sort | improved density peaks clustering algorithm based on grid screening and mutual neighborhood degree for network anomaly detection |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8792034/ https://www.ncbi.nlm.nih.gov/pubmed/35082307 http://dx.doi.org/10.1038/s41598-021-02038-z |
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