SDN环境下不同机器学习算法的网络流量分类分析
Network traffic classification analysis of different machine learning algorithms in SDN environment
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摘要: 为对比分析软件定义网络（SDN）环境下不同机器学习算法的网络流量分类效果，对Moore数据集进行了平衡处理，在机器学习平台RapidMiner上对K-近邻（KNN）、随机森林（RF）、支持向量机（SVM）和梯度提升决策树（GBDT）4种经典机器学习算法选取不同的分类特征进行分类实验.实验结果表明，较其他3种算法，GBDT算法可以在较短的时间内获得更好的分类效果.Abstract: In order to compare and analyze the network traffic classification effect of different machine learning algorithms in the software defined network (SDN) environment, the Moore dataset was balanced, and four classic machine learning algorithms including KNN, random forest (RF), support vector machine (SVM) and gradient lifting decision tree (GBDT) were supported on the machine learning platform RapidMiner to select different classification features for classification experiments. Experimental results showed that compared with the other three algorithms, the GBDT algorithm could obtain better classification results in a shorter time.
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