Hybrid rule mining based on fuzzy GNP and probabilistic classification for intrusion detection
Nannan Lu, Shingo Mabu, Wenjing Li, Kotaro Hirasawa · Society of Instrument and Control Engineers of Japan · 2010
With increasing Internet popularity, network security has become a serious problem recently. 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, which has been applied to data mining for intrusion detection systems and has shown that it provides good performances in intrusion detection. In this paper, a hybrid rule mining algorithm based on Fuzzy GNP and probabilistic classification has been proposed. Hybrid rule mining uses fuzzy class association rule mining algorithm to extract rules with different classes. Then, using different class rules and the classification of data is done probabilistically. The hybrid methods showed excellent results by the simulation experiments.