Abstract
Based on the artificial neural network and means of classification, this paper puts forward the Fisher-RBF Data Fusion Model. Abandon redundant and invalid data and decrease dimensionality of feature space to attain the goal of increasing the data fusion efficiency. In the simulation, the experiment of the network intrusion detection is conducted by using KDDCUP’99_10percent data set as the data source. The result of simulation experiment shows that on a fairly large scale, Fisher-RBF model can increase detection rate and discrimination rate, and decrease missing-report rate and misstatement rate.
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© 2012 Springer-Verlag Berlin Heidelberg
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Zhou, J., Wang, J., Qun, Z. (2012). The Research on Fisher-RBF Data Fusion Model of Network Security Detection. In: Wang, J., Yen, G.G., Polycarpou, M.M. (eds) Advances in Neural Networks – ISNN 2012. ISNN 2012. Lecture Notes in Computer Science, vol 7368. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31362-2_39
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DOI: https://doi.org/10.1007/978-3-642-31362-2_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31361-5
Online ISBN: 978-3-642-31362-2
eBook Packages: Computer ScienceComputer Science (R0)