Abstract
Very little research on feature extraction has been taken in the field of network intrusion detection. This paper proposes a novel method of applying principal component neural networks for intrusion feature extraction, and then the extracted features are employed by SVM for classification. The adaptive principal components extraction (APEX) algorithm is adopted for the implementation of PCNN. The MIT’s KDD Cup99 dataset is used to evaluate the proposed method compared to SVM without application of feature extraction technique, which clearly demonstrates that PCNN-based feature extraction method can greatly reduce the dimension of input space without degrading or even boosting the performance of intrusion detection system.
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Gao, HH., Yang, HH., Wang, XY. (2005). Principal Component Neural Networks Based Intrusion Feature Extraction and Detection Using SVM. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3611. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539117_4
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DOI: https://doi.org/10.1007/11539117_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28325-6
Online ISBN: 978-3-540-31858-3
eBook Packages: Computer ScienceComputer Science (R0)