计算机工程与应用2016，Vol.52Issue(24)：11-18,8.DOI:10.3778/j.issn.1002-8331.1604-0303
分段Hurst指数感知的流级别分类
Complementing flow classification in consideration of piecewise Hurst exponent
摘要
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.关键词
流/识别与分类/流量分形/数据相关性/Hurst指数Key words
flow/identification and classification/traffic fractals/data dependency/Hurst exponent分类
信息技术与安全科学引用本文复制引用
汤萍萍,王再见,王冬菊..分段Hurst指数感知的流级别分类[J].计算机工程与应用,2016,52(24):11-18,8.基金项目
国家自然科学基金(No.61401004) （No.61401004）
安徽省自然科学基金(No.1508085QF133) （No.1508085QF133）
安徽师范大学创新基金(No.901-741407). （No.901-741407）
