关键词: 深度流检测, 特征选择, 流识别, 动态信息

Abstract: The feature selection algorithm plays an important role in deep flow inspection technology; the correct recognition and classification of dataflow all need the process of flow characteristics selection, and using these flow characteristics to distinguish the different traffic types. The current information based feature selection algorithms compute the information entropy of the feature in the whole sample space, without considering the process of dynamic change in feature selection in computation, thus cannot accurately measure the correlation degree between each feature in the feature selection process, affecting the feature selection results, ultimately leading to reducing the performance of classification algorithm. This paper proposes an improved dynamic feature selection algorithms based on the information standard, considering the dynamic changes of information criteria in the feature selection process, by removing the redundant and useless information to avoid the interference of dynamic process and achieve the accuracy and efficiency for characteristics selection. Experimental data shows that, the proposed dynamic flow feature selection algorithm’s classification performance is better than that of other selection algorithms.

Key words: deep stream inspection, characteristic selection, stream indentify, dynamic information