摘要
传统的决策树分类方法,对于较小的数据集是非常有效的。但是,当这些方法用于入侵检测系统中时,由于巨大的网络流量,因此,存在着检测性能低和数据挖掘效率不高等问题。为了解决这些问题,提出了加权多决策树模型。将这种方法应用于入侵检测系统中,实验结果表明,该方法提高了入侵检测性能和大容量数据的处理能力。
The traditional decision tree category methods are methods are applied to intrusion detection system, it exists low these problems, weighted multi-decision tree model was described that the method improved the performance of intrusion detection effective on small data sets.But, because of huge network flow ,when these detection performance and lacking of the data mining efficiency. In order to solve .This method was applied to intrusion detection system, experimental results show and mass data processing power.
出处
《计算机安全》
2009年第8期12-14,共3页
Network & Computer Security
关键词
多决策树
入侵检测系统
数据挖掘
Multi-decision tree
instrusion detection system
data mining
