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...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Editorial Department of Journal on Communications
2011-01-01
|
| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/74412960/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | 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. |
|---|---|
| ISSN: | 1000-436X |