Abstract
This paper proposes a novel intrusion detection approach by applying kernel principal component analysis (KPCA) for intrusion feature extraction and followed by support vector machine (SVM) for classification. The MIT’s KDD Cup 99 dataset is used to evaluate these feature extraction methods, and classification performances achieved by SVM with PCA and KPCA feature extraction are compared with those obtained by PCR and KPCR classification methods and by SVM without application of feature extraction. The results clearly demonstrate that feature extraction can greatly reduce the dimension of input space without degrading the classifiers’ performance. Among these methods, the best performance is achieved by SVM using only four principal components extracted by KPCA.
Keywords
- Support Vector Machine
- Feature Extraction
- Intrusion Detection
- Anomaly Detection
- Intrusion Detection System
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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© 2005 Springer-Verlag Berlin Heidelberg
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Gao, HH., Yang, HH., Wang, XY. (2005). Kernel PCA Based Network 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_15
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DOI: https://doi.org/10.1007/11539117_15
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)