华中科技大学学报（自然科学版）2016，Vol.44Issue(z1)：1-5,10,6.DOI:10.13245/j.hust.16S101
基于组合分类器的DDoS攻击流量分布式检测模型
DDoS attack traffic distributed detection model based on ensemble classifiers
摘要
Abstract
To address the problems of poor scalability ,low detection efficiency and high false alarm rate in traditional attack traffic centralized detection model ,the random forest distributed detection model was designed to aim at DDoS (distributed denial of service) attack traffic .The model included data acquisition module ,data preprocessing module ,distributed classification detection module and a‐larm response module .The model was compared with the distributed detection method based on the Adaboost algorithm ,and the validity of model was verified by the experimental study .The results show that the ensemble classifiers distributed detection model based on random forest has higher de‐tection rate ,accuracy ,precision ,and lower false alarm rate .The model has flexible deployment ,and it is suitable for engineering practice .关键词
DDoS攻击检测/决策树/基分类器/随机森林/组合分类器Key words
DDoS attack detection/decision tree/base classifier/random forest/ensemble classifiers分类
信息技术与安全科学引用本文复制引用
贾斌,马严,赵翔..基于组合分类器的DDoS攻击流量分布式检测模型[J].华中科技大学学报（自然科学版）,2016,44(z1):1-5,10,6.基金项目
国家国际科技合作与交流专项项目（2013DFE13130）． （）
