Abstract
The information security of computer networks has become a serious global issue and also a hot topic in computer networking researches. Many approaches have been proposed to tackle these problems, especially the denial of service (DoS) attacks. The Multiple Classifier System (MCS) is one of the approaches that have been adopted in the detection of DoS attacks recently. For a MCS to perform better than a single classifier, it is crucial for the base classifiers which embedded in the MCS to be diverse. Both resampling, e.g. bagging, and feature grouping could promote diversity of base classifiers. In this paper, we propose an approach to select the reduced feature group for each of the base classifiers in the MCS based on the mutual information between the features and class labels. The base classifiers being combined using the weighted sum is examined in this paper. Different feature grouping methods are evaluated theoretically and experimentally.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Xu, L., Krzyzak, A., Suen, C.Y.: Methods for combining multiple classifiers and their applications to handwriting recognition. IEEE Trans. SMC 22, 418–435 (1992)
Kittler, J., Hatef, M., Duin, R.P.W., Matas, J.: On Combining Classifiers. IEEE Trans. On Pattern Analysis and Machine Intelligence 20, 226–239 (1998)
Giorgio, G., Fabio, R., Luca, D.: Fusion of multiple classifiers for intrusion detection in computer networks. Pattern Recognition Letters 24, 1795–1803 (2003)
Mukkamala, S., Sung, A.H., Abraham, A.: Intrusion detection using an ensemble of intelligent paradigms. Journal of Network and Computer applications (2004)
Hanson, L., Salamon, P.: Neural Network Ensembles. IEEE Transactions on pattern analysis and machine intelligence (1990)
Oza, N.C., Tumer, K.: Input Decimation Ensembles: Decorrelation through Dimensinality Reduction. In: Kittler, J., Roli, F. (eds.) MCS 2001. LNCS, vol. 2096, pp. 238–247. Springer, Heidelberg (2001)
Tumer, K., Ghosh, J.: Classifier combining: analytical results and implications. In: Working notes from the Workshop ‘Integrating Multiple Learned Models’, 13th National Conference on Artificial Intelligence, Protland, Oregon (August 1996)
Ho, T.K.: The random subspace method for constructing decision forests. IEEE Transactions on pattern analysis and machine intelligence (1998)
Bryll, R., Gutierrez-Osuna, R., Quek, F.: Attribute bagging: improving accuracy of classifier ensembles by using random feature subsets. Pattern recognition (2003)
Kuncheva, L.I.: Genetic algorithm for feature selection for parallel classifiers. Information Processing Letters 46, 163–168 (1993)
Ho, T.H., Hull, J.J., Srihari, S.N.: Decision Combination in Multiple Classifier System. IEEE Trans. PAMI 16 Pt. 1, 66–75 (1994)
Yager, R.R., Kacprzyk, J. (eds.): The Ordered Weighted Averaging Operators. Kluwer Academic Publishers, Dordrecht (1997)
Ng, W.W.Y., Chan, A.P.F., Yeung, D.S., Tsang, E.C.C.: Quantitative Study on the Generalization Error of Multiple Classifier Systems. Submitted to IC-SMC (2005)
Battiti, R.: Using Mutual Information for Selecting Features in Supervised Neural Net Learning. IEEE Transactions on neural networks 5(4) (July 1994)
Axelsson, S.: The base-rate fallacy and the difficulty of intrusion detection. ACM Trans. Information Syst. Security 3(3), 186–205 (2000)
Proctor, P.E.: The Practical Intrusion Detection Handbook. Prentice-Hall, Englewood Cliffs (2001)
Kumar, S., Spafford, E.: A pattern matching model for misuse intrusion detection. In: Proceedings of the 17th National Computer Security Conference (1994b)
Lee, W., Stolfo, S.J.: Data mining approaches for intrusion detection. In: Proceedings of the seventh USENIX Security Symposium (1998)
Schapire, R.: The strength of weak learnability. Machine Learning, 197-227 (1990)
Verwoerd, T., Hunt, R.: Intrusion detection techniques and approaches. Computer communications, 1356–1365 (2002)
Ng, W.W.Y., Chang, R.K.C., Yeung, D.S.: Dimensionality Reduction for Denial of Service Detection Problems Using RBFNN Output Sensitivity. In: Proc. on International Conference of Machine Learning and Cybernetics, pp. 1298–1298 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chan, A.P.F., Ng, W.W.Y., Yeung, D.S., Tsang, E.C.C. (2005). Multiple Classifier System with Feature Grouping for Intrusion Detection: Mutual Information Approach. In: Khosla, R., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2005. Lecture Notes in Computer Science(), vol 3683. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11553939_21
Download citation
DOI: https://doi.org/10.1007/11553939_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28896-1
Online ISBN: 978-3-540-31990-0
eBook Packages: Computer ScienceComputer Science (R0)