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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 modified quantum-behaved particle swarm optimization (MQPSO) algorithm was proposed. The WNN was trained by MQPSO. A multidimensional vector composed of WNN parameters was regarded as a particle in learning algorithm. The parameter vector, which has a best adaptation value, was searched globally. The well-known KDD Cup 1999 Intrusion Detection Data Set was 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 quantum-behaved particle swarm optimization (QPSO), and the accuracy of anomaly detection is enhanced. It also shows the remarkable ability of this novel algorithm to detect new type of attacks.