An efficient SVM-based method for multi-class network traffic classification

Abstract

Multi-class network traffic classification is a fundamental function for network services and management. Support vector machine (SVM) based network traffic classification has recently attracted increasing interest, for its high accuracy and low training sample size requirement. However, to better fit applications with delay requirements, it is desirable to reduce the high computation cost of existing SVM-based traffic classifiers. In this paper, we propose a novel scheme for SVM-based traffic classification (called fuzzy tournament). Experiment results based on real network traffic traces show that our proposed scheme can reduce computation cost by as much as 7.65 times; in the mean time, misclassification ratio is consistently reduced by up to 2.35 times as well.