A method for online detecting ＆ classifying traffic anomalies（ODC for short） from a network-wide angle of view was put forward.This method constructed traffic matrix with a metric of traffic feature entropy incrementally

de-tected traffic anomalies online using incremental principal component analysis

and then classified traffic anomalies online using incremental k-means

from which network operators could benefit for taking corresponding countermeasures.Theoretical analysis and experiment analysis show that the method has lower storage and less computing time complexity

which could satisfy the requirements of real-time process.Analysis based on both measurement data from Abilene and simulation experiments demonstrate that the method has very good detection and classification performance.