Subspace-Based Anomaly Detection for Large-Scale Campus Network Traffic
With the continuous development of information technology and the continuous progress of traffic bandwidth, the types and methods of network attacks have become more complex, posing a great threat to the large-scale campus network environment. To solve this problem, a network traffic anomaly detecti...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-01-01
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| Series: | Journal of Applied Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2023/8489644 |
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| _version_ | 1832547316574191616 |
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| author | Xiaofeng Zhao Qiubing Wu |
| author_facet | Xiaofeng Zhao Qiubing Wu |
| author_sort | Xiaofeng Zhao |
| collection | DOAJ |
| description | With the continuous development of information technology and the continuous progress of traffic bandwidth, the types and methods of network attacks have become more complex, posing a great threat to the large-scale campus network environment. To solve this problem, a network traffic anomaly detection model based on subspace information entropy flow matrix and a subspace anomaly weight clustering network traffic anomaly detection model combined with density anomaly weight and clustering ideas are proposed. Under the two test sets of public dataset and collected campus network data information of a university, the detection performance of the proposed anomaly detection method is compared with other anomaly detection algorithm models. The results show that the proposed detection model is superior to other models in speed and accuracy under the open dataset. And the two traffic anomaly detection models proposed in the study can well complete the task of network traffic anomaly detection under the large-scale campus network environment. |
| format | Article |
| id | doaj-art-bdff4418afc74302a468349151314f7a |
| institution | Kabale University |
| issn | 1687-0042 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Applied Mathematics |
| spelling | doaj-art-bdff4418afc74302a468349151314f7a2025-02-03T06:45:22ZengWileyJournal of Applied Mathematics1687-00422023-01-01202310.1155/2023/8489644Subspace-Based Anomaly Detection for Large-Scale Campus Network TrafficXiaofeng Zhao0Qiubing Wu1Book and Information CenterBook and Information CenterWith the continuous development of information technology and the continuous progress of traffic bandwidth, the types and methods of network attacks have become more complex, posing a great threat to the large-scale campus network environment. To solve this problem, a network traffic anomaly detection model based on subspace information entropy flow matrix and a subspace anomaly weight clustering network traffic anomaly detection model combined with density anomaly weight and clustering ideas are proposed. Under the two test sets of public dataset and collected campus network data information of a university, the detection performance of the proposed anomaly detection method is compared with other anomaly detection algorithm models. The results show that the proposed detection model is superior to other models in speed and accuracy under the open dataset. And the two traffic anomaly detection models proposed in the study can well complete the task of network traffic anomaly detection under the large-scale campus network environment.http://dx.doi.org/10.1155/2023/8489644 |
| spellingShingle | Xiaofeng Zhao Qiubing Wu Subspace-Based Anomaly Detection for Large-Scale Campus Network Traffic Journal of Applied Mathematics |
| title | Subspace-Based Anomaly Detection for Large-Scale Campus Network Traffic |
| title_full | Subspace-Based Anomaly Detection for Large-Scale Campus Network Traffic |
| title_fullStr | Subspace-Based Anomaly Detection for Large-Scale Campus Network Traffic |
| title_full_unstemmed | Subspace-Based Anomaly Detection for Large-Scale Campus Network Traffic |
| title_short | Subspace-Based Anomaly Detection for Large-Scale Campus Network Traffic |
| title_sort | subspace based anomaly detection for large scale campus network traffic |
| url | http://dx.doi.org/10.1155/2023/8489644 |
| work_keys_str_mv | AT xiaofengzhao subspacebasedanomalydetectionforlargescalecampusnetworktraffic AT qiubingwu subspacebasedanomalydetectionforlargescalecampusnetworktraffic |