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JACIII Vol.15 No.5 pp. 495-505
doi: 10.20965/jaciii.2011.p0495
(2011)

Paper:

Integrated Rule Mining Based on Fuzzy GNP and Probabilistic Classification for Intrusion Detection

Nannan Lu, Shingo Mabu, and Kotaro Hirasawa

Graduate School of Information, Production and Systems, Waseda University, 2-7 Hibikino, Wakamatsu-ku, Kitakyushu, Fukuoka 808-0135, Japan

Received:
October 27, 2010
Accepted:
February 23, 2011
Published:
July 20, 2011
Keywords:
network security, genetic network programming, intrusion detection system, fuzzy GNP, probabilistic classification
Abstract

With the increasing popularity of the Internet, network security has become a serious problem recently. How to detect intrusions effectively becomes an important component in network security. Therefore, a variety of algorithms have been devoted to this challenge. Genetic network programming is a newly developed evolutionary algorithm with directed graph gene structures, and it has been applied to data mining for intrusion detection systems providing good performances in intrusion detection. In this paper, an integrated rule mining algorithm based on fuzzy GNP and probabilistic classification is proposed. The integrated rule mining uses fuzzy class association rule mining algorithm to extract rules with different classes. Actually, it can deal with both discrete and continuous attributes in network connection data. Then, the classification is done probabilistically using different class rules. The integrated method showed excellent results by simulation experiments.

Cite this article as:
Nannan Lu, Shingo Mabu, and Kotaro Hirasawa, “Integrated Rule Mining Based on Fuzzy GNP and Probabilistic Classification for Intrusion Detection,” J. Adv. Comput. Intell. Intell. Inform., Vol.15, No.5, pp. 495-505, 2011.
Data files:
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