GA-Optimized Wavelet Neural Networks for System Identification
ICICIC '06 Proceedings of the First International Conference on Innovative Computing, Information and Control - Volume 1
Network Anomaly Detection Based on Wavelet Fuzzy Neural Network with Modified QPSO
International Journal of Distributed Sensor Networks
Wavelet neural networks for function learning
IEEE Transactions on Signal Processing
A new class of wavelet networks for nonlinear system identification
IEEE Transactions on Neural Networks
Accuracy analysis for wavelet approximations
IEEE Transactions on Neural Networks
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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.