Network anomal detection wavelet neural network based on QPSO
Lin Xing · Journal of Liaoning Technical University · 2009
In order to improve the detection rate for anomaly state and reduce the false positive rate for normal state in the network anomaly detection,a novel method of network anomaly detection based on constructing wavelet neural network(WNN) using quantum-behaved particle swarm optimization(QPSO) algorithm is proposed.The WNN is trained by QPSO.A multidimensional vector composed of WNN parameters is regarded as a particle in learning algorithm.The parameter vector,which has a best adaptation value,is searched globally.The well-known KDD CUP 99 Intrusion Detection Data Set is used as the experimental data.Experimental result on KDD 99 intrusion detection datasets shows that this learning algorithm has more rapid convergence,better global convergence ability compared with the traditional gradient descent(GD) algorithm and particle swarm optimization(PSO),and the accuracy of anomaly detection is enhanced.It also shows the remarkable ability of this novel algorithm to detect new type of attacks.