Abstract
Network intrusion detection is an important technique in computer security. However, the performance of existing intrusion detection systems (IDSs) is unsatisfactory since new attacks are constantly developed and the speed of network traffic volumes increases fast. To improve the performance of IDSs both in accuracy and speed, this paper proposes a novel adaptive intrusion detection method based on principal component analysis (PCA) and support vector machines (SVMs). By making use of PCA, the dimension of network data patterns is reduced significantly. The multi-class SVMs are employed to construct classification models based on training data processed by PCA. Due to the generalization ability of SVMs, the proposed method has good classification performance without tedious parameter tuning. Dimension reduction using PCA may improve accuracy further. The method is also superior to SVMs without PCA in fast training and detection speed. Experimental results on KDD-Cup99 intrusion detection data illustrate the effectiveness of the proposed method.
Supported by the National Natural Science Foundation of China Under Grants 60303012, 90104001, Specialized Research Fund for the Doctoral Program of Higher Education under Grant 20049998027, and Chinese Post-Doctor Science Foundation under Grant 200403500202.
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References
Lippmann, R., Cunningham, R.: Improving Intrusion Detection Performance Using Keyword Selection and Neural Networks. Computer Networks 34(4), 597–603 (2000)
Lee, W., Stolfo, S.J.: Data Mining Approaches for Intrusion Detection. In: Proceedings of the 1998 USENIX Security Symposium (1998)
Lee, W., Stolfo, S., Mok, K.: Adaptive Intrusion Detection: A Data Mining Approach. Artificial Intelligence Review 14(6), 533–567 (2000)
Luo, J., Bridges, S.M.: Mining Fuzzy Association Rules and Fuzzy Frequency Episodes for Intrusion Detection. International Journal of Intelligent Systems, 687–703 (2000)
Cannady, J.: Applying Neural Networks to Misuse Detection. In: Proceedings of the 21st National Information Systems Security Conference (1998)
Mahoney, M., Chan, P.: Learning Nonstationary Models of Normal Network Traffic for Detecting Novel Attacks. In: Proceedings of 8th International Conference on Knowledge Discovery and Data Mining, pp. 376–385 (2002)
Shah, H., Undercoffer, J., Joshi, A.: Fuzzy Clustering for Intrusion Detection. In: Proceedings of the 12th IEEE International Conference on Fuzzy Systems, pp. 1274–1278 (2003)
Jolliffe, I.T.: Principal Component Analysis, 2nd edn. Springer, Heidelberg (2002)
Hastie, T.J., Tibshirani, R.J., Friedman, J.H.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer, Heidelberg (2001)
Platt, J.: Fast Training of Support Vector Machines using Sequential Minimal Optimization. In: Scholkopf, B., Burges, C.J.C., Smola, A.J. (eds.) Advances in Kernel Methods—Support Vector Learning, pp. 185–208. MIT Press, Cambridge (1999)
Lin, C.-J.: Formulations of Support Vector Machines: a Note from an Optimization Point of View. Neural Computation 13(2), 307–317 (2001)
Fan, R.-E., Chen, P.-H., Lin, C.-J.: Working Set Selection using the Second Order Information for Training SVM. Technical report, Department of Computer Science, National Taiwan University (2005)
Vapnik, V.N.: Statistical Learning Theory. Wiley, Chichester (1998)
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Xu, X., Wang, X. (2005). An Adaptive Network Intrusion Detection Method Based on PCA and Support Vector Machines. In: Li, X., Wang, S., Dong, Z.Y. (eds) Advanced Data Mining and Applications. ADMA 2005. Lecture Notes in Computer Science(), vol 3584. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11527503_82
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DOI: https://doi.org/10.1007/11527503_82
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-27894-8
Online ISBN: 978-3-540-31877-4
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