Abstract
The central problem in training a radial basis function neural network (RBFNN) is the selection of hidden layer neurons, which includes the selection of the center and width of those neurons. In this paper, we propose an enhanced swarm intelligence clustering (ESIC) method to select hidden layer neurons, and then, training a cosine RBFNN base on gradient descent learning process. Also, the new method is applied for intrusion detection. Experimental results show that the average DR and FPR of our ESIC-based RBFNN detection classifier maintained a better performance than BP, SVM and OLS RBF.
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MIT Lincoln Laboratory (1998), http://www.ll.mit.edu/IST/
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Feng, Y., Wu, Zf., Zhong, J., Ye, Cx., Wu, Kg. (2008). An Enhanced Swarm Intelligence Clustering-Based RBF Neural Network Detection Classifier. In: Huang, DS., Wunsch, D.C., Levine, D.S., Jo, KH. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence. ICIC 2008. Lecture Notes in Computer Science(), vol 5227. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85984-0_63
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DOI: https://doi.org/10.1007/978-3-540-85984-0_63
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-85983-3
Online ISBN: 978-3-540-85984-0
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