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Information Systems Design and Intelligent Applications pp 442–451Cite as

An Ensemble Classifier Approach on Different Feature Selection Methods for Intrusion Detection

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 672))

Abstract

Knowing a day’s monitoring and analyzing events of network for intrusion detection system is becoming a major task. Intrusion detection system (IDS) is an essential element to detect, identify, and track the attacks. Network attacks are divided into four classes like DoS, Probe, R2L, and U2R. In this paper, ensemble techniques like AdaBoost, Bagging, and Stacking are discussed which helps to build IDS. Ensemble technique is used by combining several machine learning algorithms. Selection of features is one of the important stages in intrusion detection model. Some feature selection methods like Cfs, Chi-square, SU, Gain Ratio, Info Gain, and OneR are used in this paper with suitable search technique to select the relevant features. The selected features are applied on AdaBoost, Bagging, and Stacking with J48 as a base classifier and along with that J48 and PART are used as single classifies. Finally, results are shown that the use of AdaBoost improves the classification accuracy. Experiments and evaluation of the approaches are performed in WEKA data mining tool by using benchmark dataset NSL-KDD ‘99.

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References

  1. Vinutha H P and Poornima B: A Survey—Comparative Study on Intrusion Detection System, IJARCCE, Vol 4, Issue 7, July 2015, ISSN 2778-1021.

    Google Scholar 

  2. K. Umamaheswari and S. Janakiraman: Machine Learning in Networking Intrusion Detection System, ARPN journal of Engineering and Applied Sciences, Vol 11, No 2, January 2016, ISSN 1891-6608.

    Google Scholar 

  3. Rajender Kaur, Monika Sachdeva and Gulshan Kumar: An Empirical Analysis of Classification Approaches for Feature Selection in Intrusion Detection, IJARCSSE, Issue 9, Vol 6, September 2016, ISSN: 2277 128X.

    Google Scholar 

  4. Samah Osama M Kamel, Nadia H Hegazi, Hany M Harb, Adl Y S Tag El Dein, Hala Hala M Abd El Kader: AdaBoost Ensemble Learning Technique for Optimal Feature Subset Selection, IJCNCS, Vol 4, No 1, January 2016, 1–11.

    Google Scholar 

  5. Annkita Patel, Risha Tiwari: Bagging Ensemble Technique for Intrusion Detection System, International Journal for Technological Research in Engineering, Issue 4, Vol 2, December 2014, ISSN 2347-2718.

    Google Scholar 

  6. Riyad A M and M S Irfan Ahmed: An Ensemble Classification Approach for Intrusion Detection, Vol 80, No 2, October 2013.

    Google Scholar 

  7. Snehalata S Dongre and Kail K Wankhade: Intrusion Detection System Using New Ensemble Boosting Approach, International Journal of Modeling and Optimization, Vol 2, No. 4, August 2012.

    Google Scholar 

  8. S. Vanaja and K Ramesh Kumar: Analysis of Feature Selection Algorithms on Classification: A Survey, International Journal of Computer Application, Vol 96, No 17, June 2014.

    Google Scholar 

  9. Theyazn H Aldhyani, Manish R Joshi: Analysis of Dimensionality Reduction in Intrusion Detection, International Journal of Computational Intelligence and Informatics, Vol. 4, No. 3, October–December 2014.

    Google Scholar 

  10. Sheena, Krishan Kumar, Gulshan Kumar: Analysis of Feature selection Techniques: A Data Mining Approach, International Journal of Computer Applictions, ICAET 2016, IJCA2016(1):17–21.

    Google Scholar 

  11. S Vanaja and K Ramesh Kumar: Analysis of Feature Selection Algorithms on Classification: A Survey, IJCA, Vol 96, No 17, June 2014.

    Google Scholar 

  12. Subhajit Dey Sarkar, Saptarsi Goswami: Empirical Study on Filter based Feature Selection Methods for Text Classification, International Journal of Computer Applications (0975 – 8887), Volume 81, No. 6, November 2013.

    Google Scholar 

  13. Zahra Karimi, Mohammad Mansour and Ali Harpunabadi: Feature Ranking in Intrusion Detection Dataset using Combination of Filtering Methods, IJCA, Vol 78, No 4, September 2013.

    Google Scholar 

  14. Megha Aggarwal, Amritha: Performance Analysis of Difference Feature Selection Method in Intrusion Detection: IJSTR, Vol 2, Issue 6, June 2013.

    Google Scholar 

  15. Krishan Kumar, Gulshan Kumar, Yogesh Kumar: Feature Selection Approach for Intrusion Detection System, International Journal of Advanced Trends in Computer Science and Engineering (IJATCSE), Vol. 2, No. 5, Pages: 47–53 (2013) Special Issue of ICCECT 2013.

    Google Scholar 

  16. Iwan Syaif, Ed Zaluska, Adam Pruge-Bennett, Gary Wills: Application of Bagging, Boosting and Stacking to Intrusion Detection, Vol 28, Issues 1–2, pp, 2012.

    Google Scholar 

  17. NilufarZaman D P, Gaikwad: Comparision of Stacking and SVM method for KDD dataset, International Journal of Engineering Research and General Science, Issue 3, Vol 3, part 2, May–June 2015.

    Google Scholar 

  18. Neeraj Bisht, Amir Ahmad and A K Pant: Analysis of Classifier Ensembles for Network Intrusion Detection Systems. CAE, Vol 6, No 7, February 2017.

    Google Scholar 

  19. Jasmina D. Novakovic and Alempije Velijovic: AdaBoost as classifier Ensemble in classification problems, INFOTEH-JAHORINA Vol. 13, March 2014.

    Google Scholar 

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Authors and Affiliations

  1. Bapuji Institute of Engineering & Technology, Davangere, Karnataka, India

    H. P. Vinutha & B. Poornima

Authors
  1. H. P. Vinutha
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  2. B. Poornima
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Corresponding author

Correspondence to H. P. Vinutha .

Editor information

Editors and Affiliations

  1. Department of Electronics and Communication Engineering, Shri Ramswaroop Memorial Group of Professional Colleges, Lucknow, Uttar Pradesh, India

    Vikrant Bhateja

  2. Technology and Engineering Division, Duy Tan University, Da Nang, Vietnam

    Bao Le Nguyen

  3. Duy Tan University, Da Nang, Vietnam

    Nhu Gia Nguyen

  4. Department of CSE, PVP Siddhartha Institute of Technology, Vijayawada, Andhra Pradesh, India

    Suresh Chandra Satapathy

  5. Faculty of Information Technology , Hai Phong University, Hai Phong, Vietnam

    Dac-Nhuong Le

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Vinutha, H.P., Poornima, B. (2018). An Ensemble Classifier Approach on Different Feature Selection Methods for Intrusion Detection. In: Bhateja, V., Nguyen, B., Nguyen, N., Satapathy, S., Le, DN. (eds) Information Systems Design and Intelligent Applications. Advances in Intelligent Systems and Computing, vol 672. Springer, Singapore. https://doi.org/10.1007/978-981-10-7512-4_44

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  • DOI: https://doi.org/10.1007/978-981-10-7512-4_44

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-7511-7

  • Online ISBN: 978-981-10-7512-4

  • eBook Packages: EngineeringEngineering (R0)

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