Abstract
A novel hybrid algorithm based radial basis function (RBF) neural network is proposed for network anomaly detection in this paper. The quantum-behaved particle swarm optimization, which outperforms other optimization algorithm considerably on its simple architecture and fast convergence, has previously applied to solve optimum problem. However, the QPSO also has its own shortcomings. So, a hybrid algorithm in training RBF neural network was proposed. This new evolutionary algorithm, which is based on a hybrid of quantum-behaved particle swarm optimization (QPSO) and gradient descent algorithm (GD), is employed to train RBFNN. Experimental result on KDD99 intrusion detection datasets shows that this RBFNN using the novel hybrid algorithm has high detection rate while maintaining a low false positive rate.
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