Abstract
Current Intrusion Detection Systems (IDS) examine all data features to detect intrusion or misuse patterns. Some of the features may be redundant or contribute little (if anything) to the detection process. The purpose of this study is to identify important input features in building an IDS that is computationally efficient and effective. This paper proposes an IDS model based on general and enhanced Flexible Neural Tree (FNT). Based on the pre-defined instruction/operator sets, a flexible neural tree model can be created and evolved. This framework allows input variables selection, over-layer connections and different activation functions for the various nodes involved. The FNT structure is developed using an evolutionary algorithm and the parameters are optimized by particle swarm optimization algorithm. Empirical results indicate that the proposed method is efficient.
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Chen, Y., Abraham, A., Yang, J. (2005). Feature Selection and Intrusion Detection Using Hybrid Flexible Neural Tree. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3498. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427469_71
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DOI: https://doi.org/10.1007/11427469_71
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25914-5
Online ISBN: 978-3-540-32069-2
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