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《Journal of Changchun University》 2009-06
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Feature selection of intrusion detection system based on BP neural network

LI Kai1,TIAN Shuang-liang1,GENG Li-jun2(1.Institute of Computer and Information Engineering,Northwest University for Nationalities,Lanzhou 730030,China;2.School of Accounting,Shanxi University of Finance and Economics,Taiyuan 030006,China)  
With the advent of a variety of the invasions and attack network tools,intrusion detection has become a key component of network management.Feature selection can effectively improve machine learning and rule extraction algorithm performance.This article designs a intrusion detection system based on genetic neural network,which adopts selection method based on a variety of improved genetic algorithm features.The experimental results show that the genetic algorithm selection with different characteristics has some influences on the right classification rate of BP neural network.
【CateGory Index】: TP393.08;TP183
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