Network Anomaly Detection Using RBF Neural Network with Hybrid QPSO

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.

[1]  Yue Shi,et al.  A modified particle swarm optimizer , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[2]  Maurice Clerc,et al.  The particle swarm - explosion, stability, and convergence in a multidimensional complex space , 2002, IEEE Trans. Evol. Comput..

[3]  Jun Sun,et al.  A global search strategy of quantum-behaved particle swarm optimization , 2004, IEEE Conference on Cybernetics and Intelligent Systems, 2004..

[4]  Robert M. Sanner,et al.  Gaussian Networks for Direct Adaptive Control , 1991, 1991 American Control Conference.

[5]  Erkki Oja,et al.  Rival penalized competitive learning for clustering analysis, RBF net, and curve detection , 1993, IEEE Trans. Neural Networks.

[6]  Wang Jiaying,et al.  A modified particle swarm optimization algorithm , 2005 .

[7]  Wenbo Xu,et al.  Particle swarm optimization with particles having quantum behavior , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[8]  Teuvo Kohonen,et al.  Self-organization and associative memory: 3rd edition , 1989 .

[9]  Wenbo Xu,et al.  Adaptive parameter control for quantum-behaved particle swarm optimization on individual level , 2005, 2005 IEEE International Conference on Systems, Man and Cybernetics.

[10]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[11]  David S. Broomhead,et al.  Multivariable Functional Interpolation and Adaptive Networks , 1988, Complex Syst..

[12]  D. Broomhead,et al.  Radial Basis Functions, Multi-Variable Functional Interpolation and Adaptive Networks , 1988 .

[13]  R. Eberhart,et al.  Empirical study of particle swarm optimization , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).