Intelligent Detection of Major Network Attacks Using Feature Selection Methods

Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 131)


Intrusion detection system (IDS) detects an illegal exploitation of computer systems. In intrusion detection systems, feature selection plays an important role in a sense of improving classification performance and reducing the computational complexity. In this paper, we focus on improving identification of major network attacks like DoS, R2L and Probe using various feature selection techniques (IG, CHI2 and OCFS). This research work explored the possibility of employing a variety of classifiers, but limited to J48, Naive Bayes and AdaBoost. Empirical evaluations were completed based on a standard network intrusion data set (KDDCUP99). The Experimental results show that the feature selection approach gives considerable increase of performance in detecting network intrusions as compared to normal approach.


Feature Selection Intrusion Detection System Data Mining 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Mitra, P., Murthy, C.A., Pal, S.K.: Unsupervised feature selection using feature similarity. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(3), 301–312 (2002)CrossRefGoogle Scholar
  2. 2.
    Liu, H., Motoda, H., Yu, L.: Feature Selection with Selective Sampling. In: Proceedings of the19th International Conference on Machine Learning, pp. 395–402 (2002)Google Scholar
  3. 3.
    Robnik-Sikonja, M., Kononenko, I.: Theoretical and empirical analysis of Relief and ReliefF. Machine Learning - ML 53(1-2), 23–69 (2003)MATHCrossRefGoogle Scholar
  4. 4.
    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
  5. 5.
    Dash, M., Choi, K., Scheuermann, P., Liu, H.: Feature selection for clustering – a filter solution. In: Proceedings of IEEE International Conference on Data Mining, pp. 115–122 (2002)Google Scholar
  6. 6.
    Koller, Sahami, M.: Toward optimal feature selection. In: Proceedings of the Thirteenth International Conference on Machine Learning, pp. 84–292 (1996)Google Scholar
  7. 7.
    Kira, K., Rendell, L.A.: The Feature Selection Problem: Traditional methods and a new algorithm. In: Proceedings of Ninth National Conference on Artificial Intelligence, vol. 21, pp. 129–134 (1992)Google Scholar
  8. 8.
    Blum, L., Langley, P.: Selection of relevant features and examples in machine learning. Artificial Intelligence 97, 245–271 (1997)MathSciNetMATHCrossRefGoogle Scholar
  9. 9.
    Novakovic, J.: Using Information Gain Attribute Evaluation to Classify Sonar Targets. In: 17th Telecommunications forum TELFOR (2009)Google Scholar
  10. 10.
    Méndez, J.R., Fdez-Riverola, F., D´ıaz, F., Iglesias, E.L., Corchado, J.M.: A comparative performance study of feature selection methods for the anti-spam filtering domain. In: Industrial Conference on Data Mining, pp. 106–120 (2006)Google Scholar
  11. 11.
    Yan, J., Zhang, B., Liu, N., Yan, S., Cheng, Q., Fan, W., Yang, Q., Xi, W., Chen, Z.: Effective and Efficient Dimensionality Reduction for Large-Scale and Streaming Data Preprocessing. IEEE Transactions on Knowledge and Data Engineering 18(3), 320–333 (2006)CrossRefGoogle Scholar
  12. 12.
    Papadogiannakis, A., Polychronakis, M., Markatos, E.P.: Improving the Accuracy of Network Intrusion Detection System Under. Load Using Selective Packet Discarding. In: European Conference on Computer System, Paris, France (2010)Google Scholar
  13. 13.
    Faizal, M.A., Mohd Zaki, M., Sahib, S., Robiah, Y., Siti Rahayu, S., Asrul Hadi, Y.: Time Based Intrusion Detection on Fast Attack for Network Intrusion Detection System, netapps. In: Second International Conference on Network Applications, Protocols and Services, pp. 148–152 (2010)Google Scholar
  14. 14.
    Sung, A., Mukkamala, S.: Identifying important features for intrusion detection using SVM and neural networks. In: Symposium on Application and the Internet, pp. 209–216 (2003)Google Scholar
  15. 15.
    Elkan: Results of the KDD’99 Knowledge Discover Contest,
  16. 16.
    WEKA: Software machine learning, the University of Waikato, Hamilton, New-ZealandGoogle Scholar

Copyright information

© Springer India Pvt. Ltd. 2012

Authors and Affiliations

  1. 1.Department of Information TechnologyPune Institute of Computer TechnologyPuneIndia
  2. 2.Department of Computer Engineering and Information TechnologyCollege of Engineering PunePuneIndia

Personalised recommendations