Abstract
Most of the existing IDS use all the features in network packet to evaluate and look for known intrusive patterns. Some of these features are irrelevant and redundant. The drawback to this approach is a lengthy detection process. In real-time environment this may degrade the performance of an IDS. Thus, feature selection is required to address this issue. In this paper, we use wrapper approach where we integrate Rough Set and Particle Swarm to form a 2-tier structure of feature selection process. Experimental results show that feature subset proposed by Rough-DPSO gives better representation of data and they are robust.
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
Sung, A.H., Mukkamala, S.: Identifying Important Features for Intrusion Detection Using Support Vector Machines and Neural Networks. In: SAINT’03. Proceedings of the 2003 Symposium on Applications and the Internet, pp. 209–216 (2003)
Li, J., Zhang, G.Y, Gu, G.C.: The Research and Implementation of Intelligent Intrusion Detection System Based on Artificial Neural Network. In: IEEE Proceedings of the 3rd. International Conference on Machine Learning and Cybernetics, pp. 3178–3182. IEEE Computer Society Press, Los Alamitos (2004)
Zhang, C., Jiang, J., Kamel, M.: Intrusion Detection using Hierarchical Neural Networks. Pattern Recognition Letters 26, 779–791 (2005)
Xu, X., Wang, X.: An Adaptive Network Intrusion Detection Method Based on PCA and Support Vector Machines. In: Li, X., Wang, S., Dong, Z.Y. (eds.) ADMA 2005. LNCS (LNAI), vol. 3584, pp. 696–703. Springer, Heidelberg (2005)
Gao, H., Yang, H., Wang, X.: Kernel PCA Based Network Intrusion Feature Extraction and Detection Using SVM. In: Wang, L., Chen, K., Ong, Y.S. (eds.) ICNC 2005. LNCS, vol. 3611, pp. 89–94. Springer, Heidelberg (2005)
Chebrolu, S., Abraham, A., Thomas, J.P.: Feature Deduction and Ensemble Design of Intrusion Detection Systems. Journal of Computers and Security 24(4), 295–307 (2005)
Chen, Y., Abraham, A., Yang, J.: Feature Selection and Intrusion Detection Using Hybrid Flexible Neural Tree. In: Wang, J., Liao, X.-F., Yi, Z. (eds.) ISNN 2005. LNCS, vol. 3498, pp. 439–444. Springer, Heidelberg (2005)
Chen, Y., Abraham, A., Yang, J.: Feature Selection and Classification Using Hybrid Flexible Neural Tree. Journal of Neurocomputing 7, 305–313 (2006)
Shi, Y.: Particle Swarm Optimization. Feature Article, IEEE Neural Networks Society, 8–12 (2004)
Wang, K., Huang, L., Zhou, C., Pang, W.: Particle Swarm Optimization for Traveling Salesman Problem. In: Proceedings of the Second International Conference on Machine Learning and Cybernetics, Xi’an (November 2-5, 2003)
Kennedy, J., Spears, W.M.: Matching Algorithms to Problems: An Experimental Test of the Particle Swarm and Some Genetic Algorithms on the Multimodal Problem Generator. In: Proceedings of International Conference on Evolutionary Computation, pp. 78–83 (1998)
Jensen, R., Shen, Q.: Finding rough set Reducts with Ant Colony Optimization. In: Proceedings 2003 UK Workshop on Computational Intelligence (2003)
Pawlak, Z.: Rough Sets, Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers, Boston, MA (1991)
Jensen, R., Shen, Q.: Fuzzy-rough Data Reduction with Ant Colony Optimization. Journal of Fussy Sets and Systems 149, 5–20 (2005)
Monteiro, S., Uto, T.K., Kosugi, Y., Kobayashi, N., Watanabe, E., Kameyama, K.: Feature Extraction of Hyperspectral Data for Under Spilled Blood Visualization Using Particle Swarm Optimization. International Journal of Bioelectromagnetism 7(1), 232–235 (2005)
Sung, A.H., Mukkamala, S.: The Feature Selection and Intrusion Detection Problems. In: Maher, M.J. (ed.) ASIAN 2004. LNCS, vol. 3321, pp. 468–482. Springer, Heidelberg (2004)
Swiniarski, R.W., Skowron, A.: Rough set Methods in Feature Selection and Recognition. Pattern Recognition Letters 24, 833–849 (2003)
Chakraborty, B.: Feature Subset Selection by Neuro-rough Hybridization. LNCS, pp. 519–526. Springer, Heidelberg (2005)
Hassan, A., Nabi Baksh, M.S., Shaharoun, A.M., And Jamaluddin, H.: Improved SPC Chart Pattern Recognition Using Statistical Feature. International Journal of Production Research 41(7), 1587–1603 (2003)
Zhang, L.H., Zhang, G.H., Yu, L., Zhang, J., Bai, Y.C.: Intrusion Detection Using Rough Set Classification. Journal of Zheijiang University Science 5(9), 1076–1086 (2004)
Sung, W.S., Chi, H.L.: Using Attack-Specific Feature Subsets for Network Intrusion Detcetion. In: Sattar, A., Kang, B.-H. (eds.) AI 2006. LNCS (LNAI), vol. 4304, pp. 305–311. Springer, Heidelberg (2006)
Wang, X., Yang, J., Teng, X., Xia, W., Jensen, R.: Feature Selection based on Rough Sets and Particle Swarm Optimization. Pattern Recognition Letters 28(4), 459–471 (2007)
Liu, Y., Qin, Z., Xu, Z., He, X.: Feature Selection with Particle Swarms. In: Aizawa, K., Nakamura, Y., Satoh, S. (eds.) PCM 2004. LNCS, vol. 3331, pp. 425–430. Springer, Heidelberg (2004)
Wang, L., Yu, J.: Fault Feature Selection Based on Modified Binary PSO with Mutation and Its Application in Chemical Process Fault Diagnosis. In: Wang, L., Chen, K., Ong, Y.S. (eds.) ICNC 2005. LNCS, vol. 3612, pp. 832–840. Springer, Heidelberg (2005)
Kennedy, J., Eberhart, R.: Swarm Intelligence. Morgan Kaufmann Publishers, San Francisco, United States (2001)
Mukkamala, S., Hung, A.H., Abraham, A.: Intrusion detection using an ensemble of intelligent paradigms. Journal of Network and Computer Applications 28, 167–182 (2005)
Mukkamala, S., Sung, A.H.: Feature ranking and Selection for Intrusion detection Systems. In: Proceedings of International Conference on Information and Knowledge Engineering, Las Vegas, USA (2002)
Lee, H., Song, J., Park, D.: Intrusion Detection System based on Multiclass SVM. In: Ślęzak, D., Yao, J., Peters, J.F., Ziarko, W., Hu, X. (eds.) RSFDGrC 2005. LNCS (LNAI), vol. 3642, pp. 511–519. Springer, Heidelberg (2005)
Xu, X., Wang, X.: An Adaptive Network Intrusion Detection Method Based on PCA and Support Vector Machines. In: Li, X., Wang, S., Dong, Z.Y. (eds.) ADMA 2005. LNCS (LNAI), vol. 3584, pp. 696–703. Springer, Heidelberg (2005)
Burges, C.: A tutorial on Support Vector Machines for Pattern Recognition. Journal of Data Mining and Knowledge Discovery 2, 121–167 (1998)
Chen, W.H., Hsu, S.H., Shen, H.P.: Application of SVM and ANN for Intrusion Detection. Journal of Computers & Operations Research 32, 2617–2634 (2005)
Chih, C., Chih, J.: LIBSVM : A library for support vector machines. Tutorial and software (2001), available at http://www.csie.ntu.edu.tw/~cjlin/libsvm
Øhrn, A.: Technical Reference Manual, Department of Computer and Information Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway, pp. 1–66 (2000), http://rosetta.lcb.uu.se/general/resources/manual.pdf
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zainal, A., Maarof, M.A., Shamsuddin, S.M. (2007). Feature Selection Using Rough-DPSO in Anomaly Intrusion Detection. In: Gervasi, O., Gavrilova, M.L. (eds) Computational Science and Its Applications – ICCSA 2007. ICCSA 2007. Lecture Notes in Computer Science, vol 4705. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74472-6_42
Download citation
DOI: https://doi.org/10.1007/978-3-540-74472-6_42
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74468-9
Online ISBN: 978-3-540-74472-6
eBook Packages: Computer ScienceComputer Science (R0)