基于粒关系矩阵的流量在线分类

Online Traffic Classification Based on Granular Relation Matrix

随着各种网络应用爆发式增长,流量的在线分类陷入困境之中.传统的基于包统计特征的机器学习方法适用于稳定的网络环境,当网络拥塞出现严重的时延和丢包时将产生较大误差.因而本文提出基于粒计算模型的分类方法.粒计算属于人工智能计算的分支,当数据缺失、信息不完全或是有噪数据仍拥有较高的分辨能力.为此本文将网络流量定义成粒子并构造粒子间关系,再建立粒关系矩阵.传统的包统计特征只是粒关系矩阵当观测角度达到最大时的特例,因此粒关系矩阵对流量特性的描述更为全面,以此进行分类也更为精准.最后实验数据证明了该方法的有效性和优越性.

Online traffic classification is getting into troubles when the network applications are exploding.The traditional machine learning methods based on statistical characteristics of packets work well in stable network environment,but not in congestion environment with serious delay and packet loss.Therefore,a novel classification method based on granular computing is proposed in this paper.Granular computing belongs to the field of artificial intelligence computing,which is usually used to process missing,incomplete or noisy data.So we first define granules for the traffic,then construct the relations between the granules,and finally establish the relation matrix.The traditional statistical characteristics are only the special case of the relation matrix when the scale is the largest.The granular relation matrix can describe the traffic more comprehensively and classify them more accurately.The experiment results show its validity and advantages when compared with other methods.