Intrusion Detection Combining Multiple Decision Trees by Fuzzy logic

Authors:
Jun-feng Tian;Yue Fu;Ying Xu;Jian-ling Wang
Affiliations:
Hebei University, Baoding, China;Hebei University, Baoding, China;Hebei University, Baoding, China;Hebei University, Baoding, China
Venue:
PDCAT '05 Proceedings of the Sixth International Conference on Parallel and Distributed Computing Applications and Technologies
Year:
2005

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Abstract

In order to improve detection performance of data mining-based intrusion detection system, this paper presents a method of combining multiple decision trees based on fuzzy logic, especially the fuzzy integral. The main idea of this method is to divide a great large dataset into several sub-datasets, mine on sub-datasets separately to construct different sub-decision trees, detect TCP data by different sub-decision trees, and then nonlinearly combine the results from multiple sub-decision trees by fuzzy integral. The experiment results show that this technique is superior to individual decision trees for intrusion detection in terms of classification accuracy.