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.