基于K均值和双支持向量机的P2P流量识别方法

针对目前常用于P2P流量识别的有监督机器学习方法普遍存在时间代价较高的现状,提出采用时间代价为标准支持向量机四分之一的双支持向量机来构建分类器,并采用K均值集成方法快速生成有标签样本集,组合有标签样本集构成双支持向量机的训练样本,最后利用构建好的双支持向量机分类模型进行P2P流量的识别。实验结果表明采用基于K均值集成结合双支持向量机的方法在P2P流量识别的时间代价、准确率和稳定性方面要远优于标准支持向量机。

P2P traffic identification method based on K-means and twin support vector machine

Most of the P2P traffic identification methods have the problem of high time cost. Therefore, it was proposed to use TWin Support Vector Machine (TWSVM) whose time cost was a quarter of the common Support Vector Machine (SVM) to build classifier. K means ensemble was used to create labeled sample set and labeled sample set was combined as the training sample of the TWSVM. At last, the constructed classification model was used to identify P2P traffic. The experimental results show that the method based on K means and TWSVM can significantly decrease time cost of the P2P traffic identification, and has a higher accuracy rate and better stability than the standard SVM.