基于多维支持向量机的P2P网络流量识别模型

提出一种多维支持向量机(MSVM)训练方法,并建立了一种基于多维支持向量机的P2P网络流量识别模型。该模型利用多维支持向量机作为分类器来识别P2P流量,各种网络流量经过数据捕获模块、特征提取模块、数据预处理模块以及MSVM训练模块将网络流量分类成P2P流量和Non-P2P流量,再经过组建的MSVM支持向量库识别出具体的P2P流量和未知P2P。未知的P2P流量经过数据采集模块、特征提取模块、数据预处理模块以及MSVM训练模块将其特征数据加入MSVM支持向量库,以便将来识别P2P流量。理论分析与数值实验表明,该模型具有较好的实验结果和所期望的识别精确度。

P2P network traffic identification model based on MSVM

In this paper, a training method of Multi-dimensional Support Vector Machines (MSVM) is proposed, and a P2P network traffic identification model is constructed based on MSVM. The model uses MSVM classification to identify all P2P traffic. Data capture module, feature extraction module, data pre-processing module and MSVM training modules are used to classify all kinds of network traffic into P2P traffic and Non-P2P traffic. Then by construction of MSVM library, the specific P2P traffic and unknown P2P traffic can be identified. The feature information of unknown P2P traffic is extracted by data acquisition module, feature extraction module, data pre-processing module and MSVM training modules, and then is added to the MSVM support vector library for future P2P traffic identification. Theoretical analysis and numerical experiments show that the constructed model can achieve better identification results with desired accuracy.