摘要
基于数据挖掘的入侵检测系统中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,提出了多决策树融合模型MDTF,也就是把海量数据集分成若干子数据集,在子数据集上进行挖掘形成不同的子决策树,然后用加权平均法将多棵子决策树对网络数据的检测结果进行融合形成最优判断。实验采用KDD99数据,实验结果表明,该方法可以得到较好的入侵检测性能,分布并行处理可以提高数据挖掘效率。
in order to improve the detection performance and the data mining efficiency of data mining-based intrusion detection system, the method of multiple decision tree fusion （MDTF） is to divide a great large dataset into several sub-datasets, mine on sub-datasets by decision tree separately, and detect network data by different sub-decision trees, and then combine the results from multiple sub-decision trees by weighted average. Using the dataset of KDD99, the experimental results show that this technique is superior to the single decision tree of mining on a great large dataset for intrusion detection in terms of classification accuracy. Adopting distributed and parallel mining can improve the speed of data mining.
出处
《计算机工程与设计》
CSCD
北大核心
2008年第13期3391-3393,共3页
Computer Engineering and Design
关键词
决策树
数据挖掘
加权平均
信息融合
入侵检测
decision tree
data mining
weighted average
information fusion
intrusion detection
