Abstract
The ICSA (Immune Clonal Selection Algorithm) based structure and parameter learning of wavelet network for intrusion detection is proposed. The hierarchical structure is used in the coding scheme, thus we can realize evolution of topologic structure and the parameter learning of the wavelet network meanwhile. The experimental results show that the method based on ICSA can get higher true rate of IDS (Intrusion Detection System) than advanced wavelet network and Immune wavelet network. At the same time, the method proposed can reduce the false rate of IDS and has faster convergence speed in experiment.
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Liu, F., Luo, L. (2005). Immune Clonal Selection Wavelet Network Based Intrusion Detection. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds) Artificial Neural Networks: Biological Inspirations – ICANN 2005. ICANN 2005. Lecture Notes in Computer Science, vol 3696. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11550822_52
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DOI: https://doi.org/10.1007/11550822_52
Publisher Name: Springer, Berlin, Heidelberg
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