基于相对决策嫡的决策树算法及其在入侵检测中的应用

为了弥补传统决策树算法的不足,提出一种基于相对决策熵的决策树算法DTRDE。首先,将Shannon提出的信息熵引入到粗糙集理论中,定义一个相对决策熵的概念,并利用相对决策熵来度量属性的重要性;其次,在算法DTRDE中,采用基于相对决策熵的属性重要性以及粗糙集中的属性依赖性来选择分离属性,并且利用粗糙集中的属性约简技术来删除冗余的属性,旨在降低算法的计算复杂性;最后,将该算法应用于网络入侵检测。在KDD Cup99数据集上的实验表明,DTRDE算法比传统的基于信息熵的算法具有更高的检测率,而其计算开销则与传统方法接近。

Relative Decision Entropy Based Decision Tree Algorithm and its Application in Intrusion Detection

To overcome the disadvantages of traditional decision tree algorithms, this paper proposed a relative decision entropy based decision tree algorithm DTRDE. First, we introduced the information entropy proposed by Shannon into rough set theory, defined a concept of relative decision entropy, and utilized the relative decision entropy to measure the significance of attributes. Second, in algorithm DTRDE, we adopted the relative decision entropy based significance of attributes and the dependency of attributes in rough sets to select splitting attributes. And we used the attribute reduction technology in rough sets to delete the redundant attributes,aiming to reduce the computation complexity of our algorithm. Finally, we applied the proposed algorithm to network intrusion detection. The experiments on KDI)Cup99 dataset demonstrate that DTRDE algorithm has higher detection rate than the traditional information entropy based algorithms,and its computational expense is simliar to those of the traditional methods.