关键词: 流, 识别与分类, 流量分形, 数据相关性, Hurst指数

Abstract: The dominant methodology of flow identification and classification is based on statistical analysis, which mainly focuses on extracting efficient characteristics. However, its illogical hypothesis of characteristics independency and data independency dwarfs the classification effectiveness. Thus quantities of methods are proposed to resolve the problem of characteristics dependency, but few achievements as to data dependency. Therefore, theory of traffic fractals is introduced to identify and classify flows in consideration of data dependency, which has to be modified and adjusted to fit the practical application. Finally, theoretical evaluations indicate the validity of the revised theory, and series of experiments demonstrate the performance of this method when classifying on coarse size and classifying unknown flows.

Key words: flow, identification and classification, traffic fractals, data dependency, Hurst exponent