基于AdaBoost-SVM的P2P流量识别方法

针对传统的P2P流量识别技术存在识别率低和误判率高的缺点,将机器学习中Ada Boost算法的良好分类能力和SVM的泛化能力结合起来,提出一种基于Ada Boost-SVM组合算法的P2P网络流量识别模型,将SVM作为Ada Boost的基分类器,运用最小近邻法计算支持向量与训练集的样本间的距离实现分类进行P2P流量识别。最后,以4种P2P流量数据为研究对象在MATLAB上进行仿真,仿真结果表明,提出的Ada Boost-SVM的组合算法在P2P网络流量的分类性能和分类准确率上都优于单纯的Ada Boost和SVM,组合算法的P2P流量平均识别率高达98.7%,远高于Ada Boost和SVM的识别率。

P2P Traffic Identification Method Based on AdaBoost and SVM

The traditional P2P traffic identification technology has shortcomings of low recognition rate and high rate of false positives,this paper combines AdaBoost algorithm with good generalization ability of SVM classification together,Proposed P2P network traffic identification model based on a combination of AdaBoost-SVM algorithm. The minimum distance is calculated using the nearest neighbor method and support vector samples of the training set to achieve the classification between P2P traffic identification. Finally,taking four kinds of P2P traffic data for example,simulation results show that,AdaBoost and combinations of the SVM algorithm is proposed in the classification performance and classification accuracy is better than pure AdaBoost and SVM,the average recognition rate of Combination Algorithm was up to 98.7%,much higher than the recognition rate of AdaBoost and SVM.