Fast application-level traffic classification using NetFlow records - Liang CHEN; Jian GONG (Tongxin xuebao)
In order to improve the performance and reduce the resources usage of application-level traffic classification,a novel fast application-level traffic classification(FATC) algorithm using IP flow record from NetFlow as input was presented.FATC adopted metric selection algorithm based on correlation coefficient to measure the correlation among flow metric variables,and deleted the irrelevant or redundant metrics,then used Bayes discrimination to classify network traffic to the application category that of smallest misjudge loss.The theoretical analysis and experimental results show that,with more than 95% accuracy,the FATC algorithm greatl reduces the time and space complexity of current application-level traffic classification algorithms during the training and classification processes,and can work efficiently on 10Gbit/s backbone network in real time.