Relative Decision Entropy Based Decision Tree Algorithm and its Application in Intrusion Detection
基于相对决策嫡的决策树算法及其在入侵检测中的应用

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.