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).
Comments: 18 Pages.
[v1] 2012-08-20 21:28:38
Unique-IP document downloads: 288 times
Vixra.org is a pre-print repository rather than a journal. Articles hosted may not yet have been verified by peer-review and should be treated as preliminary. In particular, anything that appears to include financial or legal advice or proposed medical treatments should be treated with due caution. Vixra.org will not be responsible for any consequences of actions that result from any form of use of any documents on this website.
Add your own feedback and questions here:
You are equally welcome to be positive or negative about any paper but please be polite. If you are being critical you must mention at least one specific error, otherwise your comment will be deleted as unhelpful.