基于最优ABC-SVM算法的P2P流量识别

目前对等网络（Peer-to-Peer，P2P）流量的识别是网络管理研究的热门话题。基于支持向量机（Support Vector Machine , SVM）的P2P流量识别方法是常用的P2P流量识别方法之一。然而SVM的性能主要受参数和其使用特征的影响，而传统的方法则是将SVM的参数优化和特征选择问题分开处理，因此这样很难获得整体性能最优的SVM分类器。本论文提出了一种基于最优人工蜂群算法和支持向量机相结合的P2P流量识别方法，利用人工蜂群算法，将SVM的参数和特征选择问题视为最优化问题同步处理，可以获得整体性能最优的参数和特征子集。在真实的P2P数据上的实验结果表明提出的方法具有很好的自适应性和分类精度，能够同时获取特征子集和SVM参数的最优解，提高SVM分类器的整体性能。

Identification of P2P traffic based on the optimal ABC-SVM

Currently peer-to-peer (P2P) network traffic identification is a hot topic in network management. Identification of P2P traffic based on support vector machine (SVM) is a commonly used P2P traffic identification method. However, the performance of SVM is mainly affected by the parameters and features used, the traditional method is to optimize the parameters and features of SVM separately. Hence, it is difficult to obtain the optimal SVM classifier on the whole. This paper proposes a P2P traffic identification approach based on artificial bee colony algorithm and the optimal SVM. In the paper, tuning parameters of SVM and feature selection is regarded as the optimization problem, which is handled with artificial bee colony algorithm synchronously. As a result the optimal parameters and feature subset of SVM is obtained. The results show that the proposed method has good adaptability and classification accuracy on the real P2P data; it could simultaneously obtain the optimal feature subset and parameters of SVM and improve the overall performance.