Distributed denial of service attack detection using an ensemble of neural classifier
Computer Communications
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Aimed at the intrusion behaviors are characterized with uncertainty, complexity, diversity and dynamic tendency and the advantages of wavelet neural network (WNN), an intrusion detection method based on WNN is presented in this paper. Moreover, we adopt a algorithm of reduce the number of the wavelet basic function by analysis the sparseness property of sample data which can optimize the wavelet network in a large extent, and the learning algorithm based on the gradient descent was used to train network. We discussed and analyzed the impact factor of intrusion behaviors. With the ability of strong nonlinear function approach and fast convergence rate of WNN, the intrusion detection method based on WNN can detect various intrusion behaviors rapidly and effectively by learning the typical intrusion characteristic information. The experimental result shows that this intrusion detection method is feasible and effective.