Abstract
With the developing of Internet, network intrusion has becoming more and more common.Extreme learning machine (ELM) is an efficient learning algorithm for generalized single hidden layer feed-forward networks. ELM can be used for network intrusion detection.This work introduces a method using extreme learning machine to detect network intrusion. In proposed approach, a classifier is trained and used to classify connections as one of five categories. The experiment data applied is KDD99 data, which is the benchmark data for intrusion detection. In additional, this proposed method is compared against decision tree, neural network and support vector machines .It can be seen that the proposed method which using extreme learning machine has better performance than support vector machines in terms of sensitivity.
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References
Zaman, S., Karray, F.: Lightweight IDS based on features selection and IDS classification scheme. In: International Conference on Computational Science and Engineering, CSE 2009, vol. 3, pp. 365–370 (2009)
Quinlan, J.R.: Induction of decision trees. Machine Learning 1(1), 81–106 (1986)
Hagan, M.T., Demuth, H.B., Beale, M.H.: Neural network design. Pws Pub., Boston (1996)
Burges, J.: A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 2, 121–167 (1998)
Rong, H.-J., Huang, G.-B., Ong, Y.-S.: Extreme learning machine for multi-categories classification applications. In: Proceedings of IEEE International Joint Conference on Neural Networks (IJCNN 2008) (IEEE World Congress on Computational Intelligence), Hong Kong, June 1-8, pp. 1709–1713 (2008)
Lan, Y., Soh, Y.C., Huang, G.-B.: Extreme learning machine-based bacterial protein subcellular localization prediction. In: Proceedings of IEEE International Joint Conference on Neural Networks (IJCNN 2008) (IEEE World Congress on Computational Intelligence), Hong Kong, June 1-8, pp. 1859–(1863)
Huang, G.-B., Chen, Y.-Q., Babri: Classification ability of single hidden layer feed-forward neural networks. Neural Networks 11(3), 799–801 (2000)
Cheng, G.-J., Cai, L., Pan, H.-X.: Comparison of Extreme Learning Machine with Support Vector Regression for Reservoir Permeability Prediction. In: International Conference on Computational Intelligence and Security, vol. 2, pp. 173–176 (2009)
Jalil, K.A.: Comparison of Machine Learning Algorithms Performance in Detecting Network Intrusion. In: 2010 International Conference on Networking and Information Technology (2010)
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© 2015 Springer International Publishing Switzerland
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Ye, Z., Yu, Y. (2015). Network Intrusion Detection. In: Cao, J., Mao, K., Cambria, E., Man, Z., Toh, KA. (eds) Proceedings of ELM-2014 Volume 2. Proceedings in Adaptation, Learning and Optimization, vol 4. Springer, Cham. https://doi.org/10.1007/978-3-319-14066-7_8
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DOI: https://doi.org/10.1007/978-3-319-14066-7_8
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-14065-0
Online ISBN: 978-3-319-14066-7
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