An approximation decision entropy based decision tree algorithm and its application in intrusion detection

Authors:
Hongbo Zhao;Feng Jiang;Chunping Wang
Affiliations:
College of Information Science and Technology, Qingdao University of Science and Technology, Qingdao, P.R. China;College of Information Science and Technology, Qingdao University of Science and Technology, Qingdao, P.R. China;College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, P.R. China
Venue:
RSKT'12 Proceedings of the 7th international conference on Rough Sets and Knowledge Technology
Year:
2012

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Abstract

In this paper, we propose a novel decision tree algorithm DTADE within the framework of rough set theory, and apply DTADE to intrusion detection. We define a new information entropy model -- approximation decision entropy (ADE) in rough sets, which combines the concept of conditional entropy in Shannon's information theory and the concept of approximation accuracy in rough sets. In algorithm DTADE, ADE is adopted as the heuristic information for the selection of splitting attributes. Moreover, we present a method of decision tree pre-pruning based on the concept of knowledge entropy proposed by Düntsch and Gediga. Finally, the KDDCUP99 data set is used to verify the effectiveness of our algorithm in intrusion detection.