DBSCAN Clustering Algorithm Based on Big Data Is Applied in Network Information Security Detection
In order to improve the certainty and clarity of information security detection, an application method of big data clustering algorithm in information security detection is proposed. The experimental results show that when the amount of data is close to 6000, the efficiency of the improved algorithm...
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| Vydáno v: | Security and communication networks Ročník 2022; s. 1 - 8 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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London
Hindawi
12.07.2022
John Wiley & Sons, Inc |
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| ISSN: | 1939-0114, 1939-0122 |
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| Abstract | In order to improve the certainty and clarity of information security detection, an application method of big data clustering algorithm in information security detection is proposed. The experimental results show that when the amount of data is close to 6000, the efficiency of the improved algorithm is nearly 70% higher than that of DBSCAN, and it is still very close to the efficiency of the BIRCH algorithm. The algorithm has a high processing speed for large-scale data sets without increasing the time complexity and can also accurately cluster clusters of any shape. When the data set increases from 9000 rows to 58000 rows, in turn, the time-consuming of the traditional DBSCAN algorithm increases sharply, while the time-consuming of the improved DBSCAN algorithm is still stable, and the time-consuming gap between the two is getting bigger and bigger. At the same time, the algorithm adopts a heuristic adaptive algorithm to estimate some threshold parameters of the clustering algorithm, which can avoid the direct setting of the threshold parameters by the user and can effectively estimate the relevant threshold parameters, extract clusters of any shape, and the clustering effect is obvious. |
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| AbstractList | In order to improve the certainty and clarity of information security detection, an application method of big data clustering algorithm in information security detection is proposed. The experimental results show that when the amount of data is close to 6000, the efficiency of the improved algorithm is nearly 70% higher than that of DBSCAN, and it is still very close to the efficiency of the BIRCH algorithm. The algorithm has a high processing speed for large-scale data sets without increasing the time complexity and can also accurately cluster clusters of any shape. When the data set increases from 9000 rows to 58000 rows, in turn, the time-consuming of the traditional DBSCAN algorithm increases sharply, while the time-consuming of the improved DBSCAN algorithm is still stable, and the time-consuming gap between the two is getting bigger and bigger. At the same time, the algorithm adopts a heuristic adaptive algorithm to estimate some threshold parameters of the clustering algorithm, which can avoid the direct setting of the threshold parameters by the user and can effectively estimate the relevant threshold parameters, extract clusters of any shape, and the clustering effect is obvious. |
| Author | Zhang, Yan |
| Author_xml | – sequence: 1 givenname: Yan orcidid: 0000-0002-9501-7897 surname: Zhang fullname: Zhang, Yan organization: Department of Information EngineeringShiJiaZhuang University of Applied TechnologyShijiazhuang 050081HebeiChina |
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| Cites_doi | 10.1021/acsomega.0c05758 10.1055/a-1556-0210 10.1007/s42114-021-00229-w 10.1007/s40964-021-00172-8 10.1007/s10668-021-01810-5 10.1007/s13198-021-01094-y 10.1007/s10257-021-00538-z 10.1016/j.jbspin.2019.09.001 10.1155/2021/5814879 10.1088/1742-6596/1942/1/012033 10.3390/ijerph17030748 10.1016/j.eswa.2020.113361 10.1088/1742-6596/1918/4/042032 10.1049/cim2.12029 10.1109/tii.2019.2927349 10.1088/1742-6596/1757/1/012105 10.1038/s41591-019-0725-7 10.1088/1742-6596/1744/3/032065 10.1007/s13349-020-00386-4 10.1007/s00500-021-06536-0 10.3390/s21165429 10.1016/j.asoc.2021.107171 10.1053/j.jvca.2019.11.012 |
| ContentType | Journal Article |
| Copyright | Copyright © 2022 Yan Zhang. Copyright © 2022 Yan Zhang. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0 |
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| SubjectTerms | Adaptive algorithms Algorithms Big Data Cluster analysis Clustering Computer networks Data mining Datasets Efficiency Information industry Internet Methods Network security Parameter estimation Parameters Security Shape effects |
| Title | DBSCAN Clustering Algorithm Based on Big Data Is Applied in Network Information Security Detection |
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