Abstract
Network anomaly detection is currently a challenge due to the number of different attacks and the number of potential attackers. Intrusion detection systems aim to detect misuses or network anomalies in order to block ports or connections, whereas firewalls act according to a predefined set of rules. However, detecting the specific anomaly provides valuable information about the attacker that may be used to further protect the system, or to react accordingly. This way, detecting network intrusions is a current challenge due to growth of the Internet and the number of potential intruders. In this paper we present an intrusion detection technique using an ensemble of support vector classifiers and dimensionality reduction techniques to generate a set of discriminant features. The results obtained using the NSL-KDD dataset outperforms previously obtained classification rates.
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.
Similar content being viewed by others
References
Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: a survey. ACM Computing Surveys 41(3) (2009)
Hoffman, A., Schimitz, C., Sick, B.: Intrussion detection in computer networks with neural and fuzzy classifiers. In: Kaynak, O., Alpaydın, E., Oja, E., Xu, L. (eds.) ICANN 2003 and ICONIP 2003. LNCS, vol. 2714, pp. 316–324. Springer, Heidelberg (2003)
Network Security Lab - Knowledge Discovery and Data Mining (NSL-KDD) (2007), http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html
Tavallaee, M., Stakhanova, N., Ghorbani, A.: Toward credible evaluation of anomaly-based intrusion-detection methods. Trans. Sys. Man Cyber Part C 40, 516–524 (2010)
Kayacik, H., Zincir-Heywood, A., Heywood, M.: A hierarchical som-based intrusion detection system. Journal Engineering Applications of Artificial Intelligence 20(4), 439–451 (2007)
Eskin, E., Arnold, A., Prerau, M., Portnoy, L., Stolfo, S.: A geometric framework for unsupervised anomaly detection: Detecting intrusions in unlabeled data. In: Applications of Data Mining in Computer Security. Kluwer (2002)
Theodoridis, S., Koutroumbas, K.: Pattern Recognition. Academic Press (2009)
Müller, K., Mika, S., Ratsch, G., Tsuda, B., Schölkopf, B.: An introduction to kernel-based learning algorithms. IEEE Transactions on Neural Networks 12(2), 181–201 (2003)
Tenenbaum, J., de Silva, V., Langford, J.: A global geometric framework for nonlinear dimensionality reduction. Science 290, 2319–2323 (2000)
Turk, M., Pentland, A.: Eigenfaces for recognition. Journal of Cognitive Neuroscience 3(1), 71–86 (1992)
Vapnik, V.N.: Statistical Learning Theory. Wiley-Interscience (1998)
Bottou, L., Cortes, C., Denker, J., Drucker, H., Guyon, I., Jackel, L., LeCun, Y., Muller, U., Sackinger, E., Simard, P., Vapnik, V.: Comparison of classifier methods: A case study in handwriting digit recognition. In: Proc. International Conference on Pattern Recognition, pp. 77–87 (1994)
Bredensteiner, E., Bennett, K.: Multicategory classification by support vector machines. Computational Optimization and Applications 12(1-3), 53–79 (1999)
Lippmann, R., Fried, D., Graf, I., Haines, J., Kendball, K., McClung, D., Weber, D., Webster, S., Wyschgrod, D., Cuningham, R., Zissman, M.: Evaluating intrusion detection systems: the 1998 darpa off-line intrusion detection evaluation. Descex 2, 1012–1027 (2000)
McHugh, J.: Testing intrusion detection systems: a critique of the 1998 and 1999 darpa instrusion detection systems evaluation as performed by lyncoln laboratory. ACM Transactions on Information and Systems Security 3(4), 262–294 (2000)
Panda, M., Abraham, A., Abraham, M.: Discriminative multinomial naïve bayes for network intrusion detection. In: 6th Conference on Information Assurance and Security, IAS (2010)
Nziga, J.: Minimal dataset for network intrusion detection systems via dimensionality reduction. In: 6th International Conference on Digital Information Management, ICDIM (2011)
Tavallaee, M., Bagheri, E., Wei, L., Ghorbani, A.: A detailed analysis of the kddcup 1999 dataset. In: Proceedings of the IEEE International Symposium on Computational Intelligence in Security and Defense Applications CISDA (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
de la Hoz, E., Ortiz, A., Ortega, J., de la Hoz, E. (2013). Network Anomaly Classification by Support Vector Classifiers Ensemble and Non-linear Projection Techniques. In: Pan, JS., Polycarpou, M.M., Woźniak, M., de Carvalho, A.C.P.L.F., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2013. Lecture Notes in Computer Science(), vol 8073. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40846-5_11
Download citation
DOI: https://doi.org/10.1007/978-3-642-40846-5_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40845-8
Online ISBN: 978-3-642-40846-5
eBook Packages: Computer ScienceComputer Science (R0)