Abstract
近年来,利用机器学习方法处理流量分类问题成为网络测量领域一个新兴的研究方向.在现有研究中,朴素贝叶斯方法及其改进算法以其实现简单、分类高效的特点而被广泛应用.但此类方法过分依赖于样本在样本空间的分布,具有潜在的不稳定性.为此,引入C4.5决策树方法来处理流量分类问题.该方法利用训练数据集中的信息熵来构建分类模型,并通过对分类模型的简单查找来完成未知网络流样本的分类.理论分析和实验结果都表明,利用C4.5决策树来处理流量分类问题在分类稳定性上均具有明显的优势.
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