A Knowledgeable Feature Selection Based on Set Theory for Web Intrusion Detection System

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 325)


Web intrusion detection systems are security programs to decide whether events and activities occurring in a Web application or network are legitimate. The objective of Web IDS is to identify intrusions with high false alarms and low detection rate while consuming minor properties. Similarly, intelligent Web IDS have snags of concert efficiency, false positive, and false negative, while today’s advance Web page creation approaches are also facing training/learning in the clouds, great false alarms, and truncated detection rate. In this paper, an efficient feature selection approach is proposed by selecting an optimum subset of features. Hybrid feature selection relevance algorithm is used for optimum subset feature selection that decouples relevance and redundancy analysis. Empirical results show that the new proposed system gives better and robust representation of an ideal intrusion detection system while having the reduced total number of features, truncated false alarms, great detection rate, and least computation cost.


IDS Feature selection Hybrid approach Web IDS 


  1. 1.
    A. Jain, D. Zongker, Feature selection: evaluation, application, and small sample performance. IEEE Trans. Pattern Anal. Mach. Intell. 19(2), 153–158 (1997)CrossRefGoogle Scholar
  2. 2.
    P. Bhoria, K. Garg, Determining feature set of DOS attacks. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 3(5) (2013)Google Scholar
  3. 3.
    R. Lath, M. Shrivastava, Analytical study of different classification technique for KDD Cup Data’99. Int. J. Appl. Inf. Syst. 3(6) (2012)Google Scholar
  4. 4.
    K. Kira, L.A. Rendell, A practical approach to feature selection, in Proceedings of the 9th International Workshop Google Scholar
  5. 5.
    Z. Zhao, H. Liu, Searching for interacting features, in Proceedings of International Joint Conference on Artificial Intelligence (2007), pp. 1156–1161Google Scholar
  6. 6.
    H. Peng, F. Long, C. Ding, Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans. Pattern Anal. Mach. Intell. 27(8), 1226–1238 (2005)CrossRefGoogle Scholar
  7. 7.
    Y. Sun, J. Li, Iterative RELIEF for feature weighting, in Proceedings of the 23rd International Conference on Machine Learning (2006), pp. 913–920Google Scholar
  8. 8.
    S. Dudoit, J. Fridlyand, T.P. Speed, Comparison of discrimination methods for the classification of tumors using gene expression data. J. Am. Stat. Assoc. 97(457), 77–87 (2002)CrossRefMATHMathSciNetGoogle Scholar
  9. 9.
    A.L. Blum, D.P. Langley, Selection of relevant features and examples in machine learning. Artif. Intell. 97(1–2), 245–271 (1997)CrossRefMATHMathSciNetGoogle Scholar
  10. 10.
    H. Liu, L. Yu, Toward integrating feature selection algorithms for classification and clustering. IEEE Trans. Knowl. Data Eng. 17(4), 491–502 (2005)Google Scholar
  11. 11.
    F. Fleuret, Fast binary feature selection with conditional mutual information. J. Mach. Learn. Res. 5, 1531–1555 (2004)Google Scholar
  12. 12.
    I. Guyon, A. Elisseeff, An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157–1182 (2003)MATHGoogle Scholar
  13. 13.
    M.J. Martin-Bautista, M.-A. Vila, A survey of genetic feature selection in mining issues. Proc. Congr. Evol. Comput. 2, 1314–1321 (1999)Google Scholar
  14. 14.
    I. Guyon, J. Weston, S. Barnhill, V. Vapnik, Gene selection for cancer classification using support vector machines. Mach. Learn. 46(1–3), 389–422 (2002)CrossRefMATHGoogle Scholar
  15. 15.
    R. Tibshirani, T. Hastie, B. Narasimhan, G. Chu, Diagnosis of multiple cancer types by shrunken centroids of gene expression. Proc. Natl. Acad. Sci. USA. 99(10), 6567–6572 (2002)CrossRefGoogle Scholar
  16. 16.
    S. Beniwal, J. Aror, Classification and feature selection techniques in data mining. Int. J. Eng. Res. Technol. 1(6) (2012)Google Scholar

Copyright information

© Springer India 2015

Authors and Affiliations

  1. 1.Department of Information Science and TechnologyAnna UniversityChennaiIndia
  2. 2.Department of Software Engineering, Saveetha Engineering CollegeAnna UniversityChennaiIndia

Personalised recommendations