The proposed system is developed in two main phases and also a supplementary optimizing stage. At the first phase, the most important features are selected using fuzzy association rules mining (FARM) to reduce the dimension of input features to the misuse detector. At the second phase, a fuzzy adaptive resonance theory‐based neural network (ARTMAP) is used as a misuse detector. The accuracy of the proposed approach depends strongly on the precision of the parameters of FARM module and also fuzzy ARTMAP neural classifier. So, the genetic algorithm (GA) is incorporated into the proposed method to optimize the parameters of mentioned modules in this study. Classification rate (CR) results show the importance role of GA in improving the performance of the proposed intrusion detection system (IDS). The performance of proposed system is investigated in terms of detection rate (DR), false alarm rate (FAR) and cost per example (CPE).
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[v1] 2012-08-20 21:28:38
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