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Abstract
Most of the current Intrusion Detection Systems (IDS) examine all data features to detect intrusion or misuse patterns. Some of the features may be redundant or contribute little (if anything) to the detection process. We investigated the performance of two feature selection algorithms involving Bayesian Networks (BN) and Classification and Regression Trees (CART) and an ensemble of BN and CART. An hybrid architecture is further proposed by combining different feature selection algorithms. Empirical results indicate that significant input feature selection is important to design an IDS that is lightweight, efficient and effective for real world detection systems.
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Authors and Affiliations
Department of Computer Science, Oklahoma State University, USA
Srilatha Chebrolu, Ajith Abraham & Johnson P. Thomas
- Srilatha Chebrolu
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- Ajith Abraham
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- Johnson P. Thomas
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Editors and Affiliations
Indian Statistical Institute, Electronics and Communication Sciences Unit, Kolkata, India
Nikhil Ranjan Pal
School of Computer and Information Sciences, Knowledge Engineering and Discovery Research Institute (KEDRI), Auckland University of Technology, Private Bag 92006, Auckland, New Zealand
Nik Kasabov
Department of Instrumentation and Electronics Engineering, Jadavpur University, Salt-lake Campus, 700098, Calcutta, India
Rajani K. Mudi
Indian Statistical Institute, 203 B. T. Road, 700 108, Calcutta,
Srimanta Pal
Indian Statistical Institute, Computer Vision and Pattern Recognition Unit, 700108, Kolkata, India
Swapan Kumar Parui
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Chebrolu, S., Abraham, A., Thomas, J.P. (2004). Hybrid Feature Selection for Modeling Intrusion Detection Systems. In: Pal, N.R., Kasabov, N., Mudi, R.K., Pal, S., Parui, S.K. (eds) Neural Information Processing. ICONIP 2004. Lecture Notes in Computer Science, vol 3316. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30499-9_158
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