A feature selection method of network traffic based on statistic and ranking strategy

Abstract

It is required to select the best features from so many ones in order to avoid the high complexity of the model, the low classification accuracy and efficiency caused by redundant and irrelevant features, if network traffic classification is obtained by using the statistical characteristics. To solve the problem, a network traffic feature selection method based on statistic and ranking is proposed, according to generate the initial feature subset by using feature selection coefficient defined by statistic and then generate the optimal feature subset through the second feature selection of the initial feature subset by using feature influence coefficient defined by classification accuracy as the reference of extraction and ranking. Experimental results show that the proposed algorithm can reduce the number of features effectively while ensuring the overall classification accuracy and a good balance is achieved between classification effectiveness, efficiency and stability.

Keywords

• network traffic classification
• feature selection
• statistic and ranking
• feature influence