基于混沌粒子群算法和小波SVM的P2P流量识别方法  

Peer-to-Peer Traffic identification Method Based on Chaos Particle Swarm Algorithm and Wavelet SVM

作　　者：王春枝[1] 张会丽[1] 叶志伟[1] 

机构地区：[1]湖北工业大学计算机学院,武汉430068

出　　处：《计算机科学》2015年第10期117-121,共5页Computer Science

基　　金：国家自然科学基金项目:基于不可分小波核函数支持向量机的对等网络流量识别(61170135)资助

摘　　要：针对对等网络(Peer-to-Peer,P2P)流量具有的多尺度和突变性等问题,提出了基于小波核函数的支持向量机(Support Vector Machine,SVM)的P2P流量识别算法。进一步,对常用的SVM参数训练方法训练时间过长和易陷入局部极优值等缺陷进行分析,使用混沌粒子群算法对SVM参数进行优化以提高参数训练效率和识别准确率。最后利用真实的校园网网络流量数据对所提方法的有效性进行测试,结果表明,相对于使用传统核函数和参数训练方法的支持向量机P2P流量识别方法,所提方法具有更高的P2P流量识别正确率和计算效率。A novel peer-to-peer（P2P）traffic identification algorithm was proposed as the P2P traffic has the features of multi-scale and mutability. The identification algorithm is based on support vector machine （SVM） with the wavelet kernel function. Further,the disadvantages of long training time and easily falling into local minimum in the SVM pa- rameters training methods were analyzed,and chaos particle swarm algorithm was employed to optimize the SVM pa- rameters in order to improve the efficiency of parameters training and the identification accuracy. Finally, the real cam- pus network traffic data were used to test the efficiency of the proposed method. The experimental results show that the proposed method has higher identification accuracy and computational efficiency compared with the support vector ma- chine with the traditional kernel function and parameters training method.

关 键 词：P2P流量识别 支持向量机 小波 混沌粒子群优化算法 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程] TP393[自动化与计算机技术—控制科学与工程]