Network traffic detection based on multi-resolution low rank model - Guo-zhen CHENG; Dong-nian CHENG; Ding-jiu YU (Tongxin xuebao)
Because network traffic was usually characterized by its higher-dimensional features,related detectors and classifiers for identifying traffic anomalies were suffering the increased complexity.Several key observations given by existing studies showed that network anomalies were distributed typically in a sparse way,and each of anomalies was essentially characterized by its lower-dimensional features.Based on this important finding,a novel model detecting traffic anomalies—multi-resolution low rank (MRLR) was developed.The proposed MRLR allowed us to dynamically filter the “proper”feature sets and then to classify anomalies accurately.The validation shows that MRLR can accurately reduce the dimensions of flow features to lower than 10%,on the other hand,the complexity of MRLR-classifiers is O(n).