天地一体化智能网络流量实时分类

Real-time Traffic Classification of Space-earth Integrated Intelligent Network

天地一体化智能网络规模大,环境复杂,网络中流量业务类型繁多且流量具有突发性.本文结合Spark大数据分布式平台,根据流量的特点设计了SFFS-FCBF-C4.5(简称SFC)决策树分类模型,实现了大规模网络下流量的实时分类,以保障网络中资源的合理分配和利用.SFC算法是在C4.5决策树算法的基础上结合了改进后的快速相关滤波算法(Fast Correlation-Based Filter Solution,FCBF)和连续型属性值离散化算法,可以在有效去除冗余特征和降低模型复杂度的同时,提高模型分类的速度和准确率.仿真结果表明,SFC决策树分类模型相比传统的流量分类模型具有较好的稳定性和较高的准确率,可以很好的适应复杂多变的网络环境.同时,Spark大数据分布式平台的应用大幅度提高了大规模网络下流量分类的速度,能够对海量流量进行实时分类.

The Space-earth Integrated Intelligent Network has a large scale and a complex environment.There are many types of traffic in the network and the traffic is bursty.In this paper,combined with the Spark big data distributed platform,the SFFS-FCBF-C4.5(SFC)decision tree classification model is designed according to the characteristics of the traffic,which realizes the real-time classification of traffic in large-scale networks to ensure the reasonable allocation and utilization of resources in the network.The SFC algorithm combines the improved FCBF(Fast Correlation-Based Filter Solution)feature selection and the continuous attribute value discretization algorithm on the basis of the C4.5 decision tree algorithm,which can effectively remove redundant features,reduce model complexity,and improve the speed and accuracy of model classification.The simulation results show that the SFC decision tree classification model has good stability and high accuracy compared to the current models,and can well adapt to the complex and changeable network environment.At the same time,the application of Spark big data distributed platform greatly improves the speed of traffic classification under large-scale networks,and can classify massive traffic in real time.