Abstract
This paper introduces a hybrid scheme that combines the advantages of deep belief network and support vector machine. An application of intrusion detection imaging has been chosen and hybridization scheme have been applied to see their ability and accuracy to classify the intrusion into two outcomes: normal or attack, and the attacks fall into four classes; R2L, DoS, U2R, and Probing. First, we utilize deep belief network to reduct the dimensionality of the feature sets. This is followed by a support vector machine to classify the intrusion into five outcome; Normal, R2L, DoS, U2R, and Probing. To evaluate the performance of our approach, we present tests on NSL-KDD dataset and show that the overall accuracy offered by the employed approach is high.
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
Shon, T., Moon, J.: A hybrid machine learning approach to network anomaly detection. Information Sciences 177, 3799–3821 (2007)
Anderson, J.P.: Computer security threat monitoring and surveillance.Technical Report, James P. Anderson Co., Fort Washington (April 1980)
Stallings, W.: Cryptography and network security principles and practices. Prentice Hall, USA (2006)
Tsai, C., Hsu, Y., Lin, C., Lin, W.: Intrusion detection by machine learning: A review. Expert Systems with Applications 36, 11994–12000 (2009)
Biermann, E., Cloete, E., Venter, L.M.: A comparison of intrusion detection Systems. Computer and Security 20, 676–683 (2001)
Verwoerd, T., Hunt, R.: Intrusion detection techniques and approaches. Computer Communications 25, 1356–1365 (2002)
Ilgun, K., Kemmerer, R.A., Porras, P.A.: State transition analysis:A rule-based intrusion detection approach. IEEE Trans. Software Eng. 21, 181–199 (1995)
Marchette, D.: A statistical method for profiling network traffic. In: proceedings of the First USENIX Workshop on Intrusion Detection and Network Monitoring (Santa Clara), CA, pp. 119–128 (1999)
Mukkamala, S., Janoski, G., Sung, A.: Intrusion detection: support vector machines and neural networks. In: Proceedings of the IEEE International Joint Conference on Neural Networks (ANNIE), St. Louis, MO, pp. 1702–1707 (2002)
Lundin, E., Jonsson, E.: Anomaly-based intrusion detection: privacy concerns and other problems. Computer Networks 34, 623–640 (2002)
Wu, S., Banzhaf, W.: The use of computational intelligence in intrusion detection systems: A review. Applied Soft Computing 10, 1–35 (2010)
Mohamed, A.R., Dahl, G., Hinton, G.E.: Deep belief networks for phone recognition. In: NIPS 22 Workshop on Deep Learning for Speech Recognition (2009)
Hinton, G.E.: A fast learning algorithm for deep belief nets. Neural Computation 18, 1527–1554 (2006)
Noulas, A.K., Krse, B.J.A.: Deep Belief Networks for Dimensionality Reduction. In:Belgian-Dutch Conference on Artificial Intelligence, Netherland (2008)
Larochelle, H., Bengio, Y.: Classification using discriminative restricted boltzmann machines. In: Proceedings of the 25th International Conference on Machine learning, vol. 307, pp. 536–543 (2008)
McAfee, L.: Document Classification using Deep Belief Nets, CS224n, Sprint (2008)
Larochelle, H., Bengio, Y., Louradour, J., Lamblin, P.: Exploring Strategies for Training Deep Neural Networks. Journal of Machine Learning Research 10, 1–40 (2009)
Cohen, I., Tian, Q., Zhou, X.S., Huang, T.S.: Feature Selection Using Principal Feature Analysis. In: Proceedings of the 15th International Conference on Multimedia, Augsburg, Germany, September 25-29 (2007)
KDD 1999 dataset Irvine, CA, USA (July 2010), http://kdd.ics.uci.edu/databases
Tavallaee, M., Bagheri, E., Lu, W., Ghorbani, A.A.: A Detailed Analysis of the KDD CUP 99 Data Set. In: Proceeding of the IEEE Symposium on Computational Intelligence in security and defense application, CISDA (2009)
Weka. Data Mining Software in java, http://www.cs.waikato.ac.nz/ml/weka/
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Salama, M.A., Eid, H.F., Ramadan, R.A., Darwish, A., Hassanien, A.E. (2011). Hybrid Intelligent Intrusion Detection Scheme. In: Gaspar-Cunha, A., Takahashi, R., Schaefer, G., Costa, L. (eds) Soft Computing in Industrial Applications. Advances in Intelligent and Soft Computing, vol 96. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20505-7_26
Download citation
DOI: https://doi.org/10.1007/978-3-642-20505-7_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-20504-0
Online ISBN: 978-3-642-20505-7
eBook Packages: EngineeringEngineering (R0)