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Optimization of the Neural-Network-Based Multiple Classifiers Intrusion Detection System

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1 Author(s)
Xiangmei Li ; Coll. of Network Eng., Chengdu Univ. of Inf. Technol., Chengdu, China

In this paper, according to the difference between the attack categories, we adjust the 41-dimensional input features of the neural-network-based multiple classifiers intrusion detection system. After repeated experiment, we find that the every adjusted sub-classifier is better in convergence precision, shorter in training time than the 41-features sub-classifier, moreover, the whole intrusion detection system is higher in the detection rate, and less in the false negative rate than the 41-features multiple classifiers intrusion detection system. So, the scheme of the adjusting input features is able to optimize the neural-network-based multiple classifiers intrusion detection system, and proved to be feasible in practice.

Published in:

Internet Technology and Applications, 2010 International Conference on

Date of Conference:

20-22 Aug. 2010