结合遗传算法和阻尼牛顿算法的小波神经网络入侵检测

Intrusion detection using wavelet neural networks with GA and LM

小波神经网络结合了小波变换和神经网络的优点,具有很强的非线性映射能力和自适应、自学习能力,特别适合于入侵检测系统.但小波神经网络的也有易于陷入局部极小值、收敛速度慢的弱点.对此,本文引入遗传算法来优化产生小波神经网络的初始权值与阈值等,确定一个较好的搜索空间,从而克服小波神经网络易于陷入局部极小值的缺点;同时引入了阻尼牛顿算法,在遗传算法所确定了的搜索空间中对网络进行快速训练,解决传统小波神经网络收敛速度慢的问题,两者构成阻尼牛顿-遗传-小波神经网络.仿真结果表明该方法可行,使神经网络的逼近能力和泛化能力得到了显著提高.

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 ization capability be significantly enhanced. general