Decision Tree Techniques Applied on NSL-KDD Data and Its Comparison with Various Feature Selection Techniques

  • H. S. HotaEmail author
  • Akhilesh Kumar Shrivas
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 27)


Intrusion detection system (IDS) is one of the important research area in field of information and network security to protect information or data from unauthorized access. IDS is a classifier that can classify the data as normal or attack. In this paper, we have focused on many existing feature selection techniques to remove irrelevant features from NSL-KDD data set to develop a robust classifier that will be computationally efficient and effective. Four different feature selection techniques :Info Gain, Correlation, Relief and Symmetrical Uncertainty are combined with C4.5 decision tree technique to develop IDS . Experimental works are carried out using WEKA open source data mining tooland obtained results show that C4.5 with Info Gain feature selection technique has produced highest accuracy of 99.68% with 17 features, however result obtain in case of Symmetrical Uncertainty with C4.5 is also promising with 99.64% accuracy in case of only 11 features . Results are better as compare to the work already done in this area.


Decision Tree (DT) Feature Selection(FS) Intrusion Detection System(IDS) 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Pujari, A.K.: Data mining techniques, 4th edn. Universities Press (India), Private Limited (2001)Google Scholar
  2. 2.
    Tang, Z.H., MacLennan, J.: Data mining with SQL Server 2005. Willey Publishing, Inc., USA (2005)Google Scholar
  3. 3.
    Web sources, (last accessed on October 2013)
  4. 4.
    Han, J., Kamber, M.: Data Mining Concepts and Techniques, 2nd edn. Morgan Kaufmann, San Francisco (2006)zbMATHGoogle Scholar
  5. 5.
    Krzysztopf, J.C., Pedrycz, W., Roman, W.S.: Data mining methods for knowledge discovery, 3rd edn. Kluwer Academic Publishers (2000)Google Scholar
  6. 6.
    Zewdie, M.: Optimal feature selection for Network Intrusion Detection System: A data mining approach. Thesis, Master of Science in Information Science, Addis Ababa University, Ethiopia (2011)Google Scholar
  7. 7.
    Parimala, R., Nallaswamy, R.: A study of a spam E-mail classification using feature selection package. Global Journals Inc. (USA) 11, 44–54 (2011)Google Scholar
  8. 8.
    Aziz, A.S.A., Salama, M.A., Hassanien, A., Hanafi, S.E.-O.: Artificial Immune System Inspired Intrusion Detection System Using Genetic Algorithm. Informatica 36, 347–357 (2012)Google Scholar
  9. 9.
    Mukherjee, S., Sharma, N.: Intrusion detection using Bayes classifier with feature reduction. Procedia Technology 4, 119–128 (2012)CrossRefGoogle Scholar
  10. 10.
    Panda, M., Abrahamet, A., Patra, M.R.: A hybrid intelligent approach for network intrusion detection. Proceedia Engineering 30, 1–9 (2012)CrossRefGoogle Scholar
  11. 11.
    Imran, H.M., Abdullah, A.B., Hussain, M., Palaniappan, S., Ahmad, I.: Intrusion Detection based on Optimum Features Subset and Efficient Dataset Selection. International Journal of Engineering and Innovative Technology (IJEIT) 2, 265–270 (2012)Google Scholar
  12. 12.
    Bhavsar, Y.B., Waghmare, K.C.: Intrusion Detection System Using Data Mining Technique: Support Vector Machine. International Journal of Emerging Technology and Advanced Engineering 3, 581–586 (2013)Google Scholar
  13. 13.
    Web sources, (last accessed on October 2013)
  14. 14.
    Web sources, (last accessed on October 2013)
  15. 15.
    Hota, H.S., Shrivas, A.K.: Data Mining Approach for Developing Various Models Based on Types of Attack and Feature Selection as Intrusion Detection Systems (IDS). In: Intelligent Computing, Networking, and Informatics. AISC, vol. 243, pp. 845–851. Springer, Heidelberg (2014)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Guru Ghasidas Central UniversityBilaspurIndia
  2. 2.Dr. C.V. Raman UniversityBilaspurIndia

Personalised recommendations