Abstract
Computer security has become a critical issue with the rapid development of business and other transaction systems over the Internet. The application of artificial intelligence, machine learning and data mining techniques to intrusion detection systems has been increasing recently. But most research is focused on improving the classification performance of a classifier. Selecting important features from input data leads to simplification of the problem, and faster and more accurate detection rates. Thus selecting important features is an important issue in intrusion detection. Another issue in intrusion detection is that most of the intrusion detection systems are performed by off-line and it is not a suitable method for a real-time intrusion detection system. In this paper, we develop the real-time intrusion detection system, which combines an on-line feature extraction method with the on-line Least Squares Support Vector Machine classifier. Applying the proposed system to KDD CUP 99 data, experimental results show that it has a remarkable feature feature extraction, classification performance and reducing detection time compared to existing off-line intrusion detection system.
This study was supported by a grant of the Korea Health 21 R&D Project, Ministry of Health & Welfare, Republic of Korea (A05-0909-A80405-05N1-00000A).
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References
Eskin, E.: Anomaly detection over noisy data using learned probability distribution. In: Proceedings of the Seventeenth International Conference on Machine Learning, pp. 443–482 (2000)
Ghosh, A., Schwartzbard, A.: A Study in using neural networks for anomaly and misuse detection. In: Proceedings of the Eighth USENIX Security Symposium, pp. 443–482 (1999)
Lee, W., Stolfo, S.J., Mok, K.: A Data mining in workflow environments.:Experience in intrusion detection. In: Proceedings of the 1999 Conference on Knowledge Discovery and Data Mining (1999)
Tipping, M.E., Bishop, C.M.: Mixtures of probabilistic principal component analysers. Neural Computation 11(2), 443–482 (1998)
Kramer, M.A.: Nonlinear principal component analysis using autoassociative neural networks. AICHE Journal 37(2), 233–243 (1991)
Diamantaras, K.I., Kung, S.Y.: Principal Component Neural Networks: Theory and Applications. John Wiley & Sons, Inc., New York (1996)
Kim, B.J., Shim, J.Y., Hwang, C.H., Kim, I.K.: On-line Feature Extraction Based on Emperical Feature Map. In: Zhong, N., Raś, Z.W., Tsumoto, S., Suzuki, E. (eds.) ISMIS 2003. LNCS (LNAI), vol. 2871, pp. 440–444. Springer, Heidelberg (2003)
Softky, W.S., Kammen, D.M.: Correlation in high dimensional or asymmetric data set: Hebbian neuronal processing. Neural Networks 4, 337–348 (1991)
Gupta, H., Agrawal, A.K., Pruthi, T., Shekhar, C., Chellappa., R.: An Experimental Evaluation of Linear and Kernel-Based Methods for Face Recognition, Accessible at, http://citeseer.nj.nec.com
Liu, J., Chen, J.P., Jiang, S., Cheng, J.: Online LS-SVM for function estimation and classification. Journal of University of Science and Technology Beijing 10(5), 73–77 (2003)
Vapnik, V.N.: Statistical learning theory. John Wiley & Sons, New York (1998)
Hall, P., Marshall, D., Martin, R.: On-line eigenalysis for classification. In: British Machine Vision Conference, September 1998, vol. 1, pp. 286–295 (1998)
Winkeler, J., Manjunath, B.S., Chandrasekaran, S.: Subset selection for active object recognition. In: CVPR, June 1999, vol. 2, pp. 511–516. IEEE Computer Society Press, Los Alamitos (1999)
Murakami, H., Kumar., B.V.K.V.: Efficient calculation of primary images from a set of images. IEEE PAMI 4(5), 511–515 (1982)
Scholkopf, B., Smola, A., Muller, K.R.: Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation 10(5), 1299–1319 (1998)
Tsuda, K.: Support vector classifier based on asymmetric kernel function. In: Proc. ESANN (1999)
Mika, S.: Kernel algorithms for nonlinear signal processing in feature spaces. Master’s thesis, Technical University of Berlin (November 1998)
Accessable at, http://kdd.ics.uci.edu/databases/kddcup99
Gestel, V.T., Suykens, J.A., Lanckriet, G., Lambrechts, A., De Moor, B. Vandewalle, J.: A Bayesian Framework for Least Squares Support Vector Machine Classifiers. Internal Report 00-65, ESAT-SISTA, K.U. Leuven
Suykens, J.A.K., Vandewalle, J.: Multiclass Least Squares Support Vector Machines. In: Proc. International Joint Conference on Neural Networks (IJCNN 1999), Washington DC (1999)
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Kim, BJ., Kim, I.K. (2006). Improved Kernel Based Intrusion Detection System. In: Kollias, S., Stafylopatis, A., Duch, W., Oja, E. (eds) Artificial Neural Networks – ICANN 2006. ICANN 2006. Lecture Notes in Computer Science, vol 4132. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11840930_90
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DOI: https://doi.org/10.1007/11840930_90
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-38871-5
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