机器学习用于网络流量识别  

Network Flow Identification Based on Machine Learning

作　　者：马永立[1] 钱宗珏[1] 寿国础[1] 胡怡红[1] 

机构地区：[1]北京邮电大学信息与通信工程学院。北京100876

出　　处：《北京邮电大学学报》2009年第1期65-68,共4页Journal of Beijing University of Posts and Telecommunications

基　　金：国家自然科学基金项目(60672025)

摘　　要：提出了将机器学习中的C4.5算法应用于传输层的网络流量特征识别技术.运用相关性特征选择和遗传算法形成了流量特征子集.提出并采用N折交叉验证与测试集相结合的方法评估了国家运营宽带网络中的流量测试分类结果.实验证明,无须预知端口和协议标签,网络流量就能被成功地识别与分析.Machine learning with CA. 5 algorithm is proposed for network traffic identification. The correlation feature selection algorithm and the genetic algorithm are adopted to select the attribute feature subset. A method of combining N-fold cross validation with testing set is suggested to assess the classification results of the current national broadband network traffic. Experiments demonstrate that network traffic can be successfully identified and analyzed, meanwhile, the port number and the application layer protocol label of network flows are not necessary to be known in advance.

关 键 词：机器学习 决策树 流量识别 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]