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Intrusion detection using wavelet neural networks with GA and LM

GUO De-chao1,2,CAI Li-dong1(1.Department of Computer Science,Jinan University,Guangzhou 510632,China;2.School of Economy and Management,Guangzhou University of Chinese Medicine,Guangzhou 510006,China)  
The wavelet neural network(WNN) combines both advantages of the wavelet transform and the neural network,hence being of strong nonlinear mapping,adaptive and self-learning capabilities,and fairly suitable to the intrusion detection.However,it has some weakness in computing,such as easy convergence to local minimums and a slow convergence rate.To improve WNN's performance first the genetic algorithm(GA) is introduced to optimize WNN's initial weights and thresholds etc.for getting a better solution space to avoid local minimums;then the Levenberg-Marquardt(LM) algorithm is used to speed up the convergence rate,thus leading to an algorithm-hybrid neural network,namely the GALM-WNN.The simulation results show that such a hybrid treatment makes WNN's approximation and generalization capability be significantly enhanced.
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