MOADA-SVR:a multivariate online anomaly detection algorithm based on SVR
Network anomaly detection is critical to guarantee stabilized and effective network operation.Although PCA-based network-wide anomaly detection algorithm has good detection performance,it cannot satisfy demands of online detection.In order to solve the problem,after traffic matrix model was introduc...
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2011-01-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/74412960/ |
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| _version_ | 1841537815201972224 |
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| author | QIAN Ye-kui1 CHEN Ming1 |
| author_facet | QIAN Ye-kui1 CHEN Ming1 |
| author_sort | QIAN Ye-kui1 |
| collection | DOAJ |
| description | Network anomaly detection is critical to guarantee stabilized and effective network operation.Although PCA-based network-wide anomaly detection algorithm has good detection performance,it cannot satisfy demands of online detection.In order to solve the problem,after traffic matrix model was introduced,a normality model of traffic was constructed using SVR and the sparsification of support vector solutions.Based on these,a multivariate online anomaly detection algorithm based on SVR named MOADA-SVR was proposed.Theoretic analysis showed that MOADA-SVR had lower storage and less computing overhead compared with PCA.Analysis for traffic matrix datasets Internet showed that MOADA-SVR had also good detection performance,approximating PCA. |
| format | Article |
| id | doaj-art-a9aac70e69a341de970cd93985c399a7 |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2011-01-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-a9aac70e69a341de970cd93985c399a72025-01-14T08:22:46ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2011-01-013210611374412960MOADA-SVR:a multivariate online anomaly detection algorithm based on SVRQIAN Ye-kui1CHEN Ming1Network anomaly detection is critical to guarantee stabilized and effective network operation.Although PCA-based network-wide anomaly detection algorithm has good detection performance,it cannot satisfy demands of online detection.In order to solve the problem,after traffic matrix model was introduced,a normality model of traffic was constructed using SVR and the sparsification of support vector solutions.Based on these,a multivariate online anomaly detection algorithm based on SVR named MOADA-SVR was proposed.Theoretic analysis showed that MOADA-SVR had lower storage and less computing overhead compared with PCA.Analysis for traffic matrix datasets Internet showed that MOADA-SVR had also good detection performance,approximating PCA.http://www.joconline.com.cn/zh/article/74412960/anomaly detectiononline detectionSVRmultivariate analysistraffic matrix |
| spellingShingle | QIAN Ye-kui1 CHEN Ming1 MOADA-SVR:a multivariate online anomaly detection algorithm based on SVR Tongxin xuebao anomaly detection online detection SVR multivariate analysis traffic matrix |
| title | MOADA-SVR:a multivariate online anomaly detection algorithm based on SVR |
| title_full | MOADA-SVR:a multivariate online anomaly detection algorithm based on SVR |
| title_fullStr | MOADA-SVR:a multivariate online anomaly detection algorithm based on SVR |
| title_full_unstemmed | MOADA-SVR:a multivariate online anomaly detection algorithm based on SVR |
| title_short | MOADA-SVR:a multivariate online anomaly detection algorithm based on SVR |
| title_sort | moada svr a multivariate online anomaly detection algorithm based on svr |
| topic | anomaly detection online detection SVR multivariate analysis traffic matrix |
| url | http://www.joconline.com.cn/zh/article/74412960/ |
| work_keys_str_mv | AT qianyekui1 moadasvramultivariateonlineanomalydetectionalgorithmbasedonsvr AT chenming1 moadasvramultivariateonlineanomalydetectionalgorithmbasedonsvr |