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Linear Correlation-Based Feature Selection for Network Intrusion Detection Model

  • Heba F. Eid
  • Aboul Ella Hassanien
  • Tai-hoon Kim
  • Soumya Banerjee
Part of the Communications in Computer and Information Science book series (CCIS, volume 381)

Abstract

Feature selection is a preprocessing phase to machine learning, which leads to increase the classification accuracy and reduce its complexity. However, the increase of data dimensionality poses a challenge to many existing feature selection methods. This paper formulates and validates a method for selecting optimal feature subset based on the analysis of the Pearson correlation coefficients. We adopt the correlation analysis between two variables as a feature goodness measure. Where, a feature is good if it is highly correlated to the class and is low correlated to the other features. To evaluate the proposed Feature selection method, experiments are applied on NSL-KDD dataset. The experiments shows that, the number of features is reduced from 41 to 17 features, which leads to improve the classification accuracy to 99.1%. Also,The efficiency of the proposed linear correlation feature selection method is demonstrated through extensive comparisons with other well known feature selection methods.

Keywords

Network security Data Reduction Feature selection Linear Correlation Intrusion detection 

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References

  1. 1.
    Tsai, C., Hsu, Y., Lin, C., Lin, W.: Intrusion detection by machine learning: A review. Expert Systems with Applications 36(10), 11994–12000 (2009)CrossRefGoogle Scholar
  2. 2.
    Debar, H., Dacier, M., Wespi, A.: Towards a taxonomy of intrusion-detection systems. Computer Networks 31(8), 805–822 (1999)CrossRefGoogle Scholar
  3. 3.
    Kuchimanchi, G., Phoha, V., Balagani, K., Gaddam, S.: Dimension Reduction Using Feature Extraction Methods for Real-time Misuse Detection Systems. In: Proceedings of the Fifth Annual IEEE SMC Information Assurance Workshop, pp. 195–202 (2004)Google Scholar
  4. 4.
    Li, Y., Xia, J., Zhang, S., Yan, J., Ai, X., Dai, K.: An efficient intrusion detection system based on support vector machines and gradually feature removal method. Expert Systems with Applications 39(1), 424–430 (2012)CrossRefGoogle Scholar
  5. 5.
    Amiri, F., Yousefi, M., Lucas, C., Shakery, A., Yazdani, N.: Mutual information-based feature selection for intrusion detection systems. Journal of Network and Computer Applications 34(4), 1184–1199 (2011)CrossRefGoogle Scholar
  6. 6.
    Dash, M., Choi, K., Scheuermann, P., Liu, H.: Feature selection for clustering-a filter solution. In: Proceedings of the Second International Conference on Data Mining, pp. 115–122 (2002)Google Scholar
  7. 7.
    Koller, D., Sahami, M.: Toward optimal feature selection. In: Proceedings of the Thirteenth International Conference on Machine Learning, pp. 284–292 (1996)Google Scholar
  8. 8.
    Tsang, C., Kwong, S., Wang, H.: Genetic-fuzzy rule mining approach and evaluation of feature selection techniques for anomaly intrusion detection. Pattern Recognition 40(9), 2373–2391 (2007)zbMATHCrossRefGoogle Scholar
  9. 9.
    Elngar, A., Mohamed, D., Ghaleb, F.: A Real-Time Anomaly Network Intrusion Detection System with High Accuracy. Information Sciences Letters International Journal 2(2), 49–56 (2013)Google Scholar
  10. 10.
    Yu, L., Liu, H.: Efficient Feature Selection via Analysis of Relevance and Redundancy. Journal of Machine Learning Research 5(1), 1205–1224 (2004)zbMATHGoogle Scholar
  11. 11.
    Yu, L., Liu, H.: Feature selection for high-dimensional data: a fast correlation-based filter solution. In: Proceedings of the Twentieth International Conference on Machine Learning, pp. 856–863 (2003)Google Scholar
  12. 12.
    Kim, Y., Street, W., Menczer, F.: Feature selection for unsupervised learning via evolutionary search. In: Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 365–369 (2000)Google Scholar
  13. 13.
    Kohavi, R., John, G.H.: Wrappers for feature subset selection. Artificial Intelligence 1(2), 273–324 (1997)CrossRefGoogle Scholar
  14. 14.
    Peng, H., Long, F., Ding, C.: Feature selection based on mutual information criteria of max-dependency, max-relevance, and min redundancy. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(8), 1226–1238 (2005)CrossRefGoogle Scholar
  15. 15.
    Jin, X., Xu, A., Bie, R., Guo, P.: Machine learning techniques and chi-square feature selection for cancer classification using SAGE gene expression profiles. In: Li, J., Yang, Q., Tan, A.-H. (eds.) BioDM 2006. LNCS (LNBI), vol. 3916, pp. 106–115. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  16. 16.
    Ben-Bassat, M.: Pattern recognition and reduction of dimensionality. In: Handbook of Statistics II, vol. 1, North-Holland, Amsterdam (1982)Google Scholar
  17. 17.
    Holland, J.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975)Google Scholar
  18. 18.
    Quinlan, J.R.: Induction of Decision Trees. Machine Learning 1(1), 81–106 (1986)Google Scholar
  19. 19.
    Jemili, F., Zaghdoud, M., Ahmed, M.: Intrusion detection based on Hybrid propagation in Bayesian Networks. In: Proceedings of the IEEE International Conference on Intelligence and Security Informatics, pp. 137–142 (2009)Google Scholar
  20. 20.
    Press, W.H., Teukolsky, S.A., Vetterling, W.T., Flannery, B.P.: Numerical recipes in C. The art of scientific computing. Cambridge University Press, Cambridge (1988)zbMATHGoogle Scholar
  21. 21.
    Tavallaee, M., Bagheri, E., Lu, W., Ghorbani, A.A.: A Detailed Analysis of the KDD CUP 99 Data Set. In: Proceeding of IEEE Symposium on Computational Intelligence in Security and Defense Application, CISDA (2009)Google Scholar
  22. 22.
    KDD’99 dataset, Irvine, CA, USA (July 2010), http://kdd.ics.uci.edu/databases
  23. 23.
    Duda, R.O., Hart, P.E., Stork, P.E.: Pattern Classification, 2nd edn. JohnWiley & Sons, USA (2001)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Heba F. Eid
    • 1
    • 5
  • Aboul Ella Hassanien
    • 2
    • 5
  • Tai-hoon Kim
    • 3
  • Soumya Banerjee
    • 4
    • 5
  1. 1.Faculty of ScienceAl-Azhar UniversityCairoEgypt
  2. 2.Faculty of Computers and InformationCairo UniversityEgypt
  3. 3.Hannam UniversityKorea
  4. 4.Dept. of CSBirla Institute of Technology, MesraIndia
  5. 5.Scientific Research Group in Egypt (SRGE)Egypt

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