Research on Network Attack Traffic Detection HybridAlgorithm Based on UMAP-RF
Network attack traffic detection plays a crucial role in protecting network operations and services. To accurately detect malicious traffic on the internet, this paper designs a hybrid algorithm UMAP-RF for both binary and multiclassification network attack detection tasks. First, the network traffi...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-07-01
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| Series: | Algorithms |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1999-4893/15/7/238 |
| _version_ | 1797407931539390464 |
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| author | Xiaoyu Du Cheng Cheng Yujing Wang Zhijie Han |
| author_facet | Xiaoyu Du Cheng Cheng Yujing Wang Zhijie Han |
| author_sort | Xiaoyu Du |
| collection | DOAJ |
| description | Network attack traffic detection plays a crucial role in protecting network operations and services. To accurately detect malicious traffic on the internet, this paper designs a hybrid algorithm UMAP-RF for both binary and multiclassification network attack detection tasks. First, the network traffic data are dimensioned down with UMAP algorithm. The random forest algorithm is improved based on parameter optimization, and the improved random forest algorithm is used to classify the network traffic data, distinguishing normal data from abnormal data and classifying nine different types of network attacks from the abnormal data. Experimental results on the UNSW-NB15 dataset, which are significant improvements compared to traditional machine-learning methods, show that the UMAP-RF hybrid model can perform network attack traffic detection effectively, with accuracy and recall rates of 92.6% and 91%, respectively. |
| first_indexed | 2024-03-09T03:49:54Z |
| format | Article |
| id | doaj.art-dad64a562eec4d1ea427caae9ecfdfef |
| institution | Directory Open Access Journal |
| issn | 1999-4893 |
| language | English |
| last_indexed | 2024-03-09T03:49:54Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj.art-dad64a562eec4d1ea427caae9ecfdfef2023-12-03T14:30:07ZengMDPI AGAlgorithms1999-48932022-07-0115723810.3390/a15070238Research on Network Attack Traffic Detection HybridAlgorithm Based on UMAP-RFXiaoyu Du0Cheng Cheng1Yujing Wang2Zhijie Han3School of Computer and Information Engineering, Henan University, Kaifeng 475004, ChinaSchool of Computer and Information Engineering, Henan University, Kaifeng 475004, ChinaSchool of Computer and Information Engineering, Henan University, Kaifeng 475004, ChinaSchool of Software, Henan University, Kaifeng 475004, ChinaNetwork attack traffic detection plays a crucial role in protecting network operations and services. To accurately detect malicious traffic on the internet, this paper designs a hybrid algorithm UMAP-RF for both binary and multiclassification network attack detection tasks. First, the network traffic data are dimensioned down with UMAP algorithm. The random forest algorithm is improved based on parameter optimization, and the improved random forest algorithm is used to classify the network traffic data, distinguishing normal data from abnormal data and classifying nine different types of network attacks from the abnormal data. Experimental results on the UNSW-NB15 dataset, which are significant improvements compared to traditional machine-learning methods, show that the UMAP-RF hybrid model can perform network attack traffic detection effectively, with accuracy and recall rates of 92.6% and 91%, respectively.https://www.mdpi.com/1999-4893/15/7/238internetcyber attackrandom forestUMAPmachine learning |
| spellingShingle | Xiaoyu Du Cheng Cheng Yujing Wang Zhijie Han Research on Network Attack Traffic Detection HybridAlgorithm Based on UMAP-RF Algorithms internet cyber attack random forest UMAP machine learning |
| title | Research on Network Attack Traffic Detection HybridAlgorithm Based on UMAP-RF |
| title_full | Research on Network Attack Traffic Detection HybridAlgorithm Based on UMAP-RF |
| title_fullStr | Research on Network Attack Traffic Detection HybridAlgorithm Based on UMAP-RF |
| title_full_unstemmed | Research on Network Attack Traffic Detection HybridAlgorithm Based on UMAP-RF |
| title_short | Research on Network Attack Traffic Detection HybridAlgorithm Based on UMAP-RF |
| title_sort | research on network attack traffic detection hybridalgorithm based on umap rf |
| topic | internet cyber attack random forest UMAP machine learning |
| url | https://www.mdpi.com/1999-4893/15/7/238 |
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