改进的图半监督支持向量机用于P2P网络流识别

对于机器学习在P2P网络流识别中需要大量标记训练数据的问题，提出一种基于改进图半监督支持向量机的P2P流识别方法。采用自动调节的高斯核函数计算少量标识数据和大量未标识训练样本之间的相似距离以构建图模型，并在标记传播过程中嵌入训练样本局部分布信息以获取未标记样本的标识；在此基础上使用所有已标记样本对SVM训练实现P2P网络流识别。实验结果表明该方法能够兼顾整个训练样本集的信息，在提高SVM识别精度的同时，极大降低了人工标记训练样本的成本。

Improved Graphic Semi-supervised SVM for P2P Network Traffic Identification

In P2P network traffic identification, aiming at the problems that passive machine learning needs a lot of labeled training data ,an improved graphic semi-supervised learning method was proposed. and with SVM used in P2P network traffic identification. Gauss kernel function of self-regulation was applied for calculating similar distance of graphic model.Meanwhile,in the course of label propagation, local distribution information of training samples was added to get label of unlabeled samples.Finally, the labeled samples were used to train SVM for P2P network traffic identification. Simulation shows that the method can give consideration to all the information of training samples, effectively improve accuracy rate of P2P network traffic identification and greatly reduce the cost of labeling training samples.