Skip to main content
SpringerLink
Log in

Application of Artificial Immune System Algorithms for Intrusion Detection

  • Conference paper
  • First Online:
Book cover Computational Intelligence, Communications, and Business Analytics (CICBA 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 776))

  • 1407 Accesses

Abstract

Intrusion Detection (ID) is one of the most challenging problems in today’s era of computer security. New innovative ideas are used by the hackers to break the security, hence the challenge for developing better ID systems are increasing day-by-day. In this paper, we applied the Artificial Immune System (AIS) based classifiers for intrusion detection. Each classifier is evaluated based on high accuracy and detection rate with low false alarm rate. The results are compared using percentage split (80%) and cross validation (10 fold) test options basing on two nominal target attributes i.e. type of attacks and protocol types having 5 and 3 sub-classes respectively. The experimental results indicate that the performance of CSCA (clonal selection classification algorithm) is better AIS based classifier for network based Intrusion Detection.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

References

  1. Tavallaee, M., Bagheri, E., Lu, W., Ghorbani, A.A.: A detailed analysis of the KDD CUP 99 data set. In: CISDA 2009 (2009)

    Google Scholar 

  2. Chan, F.T.S., Prakash, A., Tibrewal, R.K., Tiwari, M.K.: Clonal selection approach for network intrusion detection. In: Proceedings of the 3rd International Conference on Intelligent Computational Systems, ICICS 2013, Singapore, 29–30 April 2013

    Google Scholar 

  3. Mohammad, M.N., Sulaiman, N., Muhsin, O.A.: A novel intrusion detection system by using intelligent data mining in WEKA environment. Procedia Comput. Sci. 3, 1237–1242 (2011)

    Article  Google Scholar 

  4. Matzinger, P.: Tolerance, danger and the extended family. Annu. Rev. Immunol. 12, 991–1045 (1994)

    Article  Google Scholar 

  5. Jain, Y.K., Upendra: Intrusion detection using supervised learning with feature set reduction. Int. J. Comput. Appl. 33(6), November 2011. ISSN: 0975-8887

    Google Scholar 

  6. de Castro, L.N., Von Zuben, F.J.: Learning and optimization using the clonal selection principle. IEEE Trans. Evol. Comput. 6(3), 239–251 (2002). doi:10.1109/tevc.2002.1011539. Special Issue on Artificial Immune Systems. IEEE

    Article  Google Scholar 

  7. Kalyani, G., Jaya Lakshmi, A.: Performance assessment of different classification techniques for intrusion detection. IOSRJCE 7(5), 25–29 (2012). ISSN: 2278-0661, ISBN: 2278-8727

    Article  Google Scholar 

  8. NSL-KDD data set for network-based intrusion detection systems, March 2009. http://nsl.cs.unb.ca/NSL-KDD/. Accessed 05 Dec 2016

  9. Dutt, I., Borah, S.: Some studies in intrusion detection using data mining techniques. 4(7), July 2015. doi:10.15680/IJIRSET.2015.0407090

  10. Burnet, F.M.: A modification of Jerne’s theory of antibody production using the concept of clonal selection. CA Cancer J. Clin. 26(2), 119–121 (1976). doi:10.3322/canjclin.26.2.119. PMID 816431

    Article  Google Scholar 

  11. Nguyen, H.A., Choi, D.: Application of data mining to network intrusion detection: classifier selection model. In: Ma, Y., Choi, D., Ata, S. (eds.) APNOMS 2008. LNCS, vol. 5297, pp. 399–408. Springer, Heidelberg (2008). doi:10.1007/978-3-540-88623-5_41

    Chapter  Google Scholar 

  12. Modi, U., Jain, A.: A survey of IDS classification using KDD CUP 99 dataset in WEKA. Int. J. Sci. Eng. Res. 6(11), 947–954 (2015)

    Google Scholar 

  13. Siddiqui, M.K., Naahid, S.: Analysis of KDD CUP 99 dataset using clustering based data mining. Int. J. Database Theory Appl. 6(5), 23–34 (2013)

    Article  Google Scholar 

  14. Jerne, N.K.: Towards a network theory of the immune system. Ann. Immunol. (Inst. Pasteur) 125C, 373–389 (1974)

    Google Scholar 

  15. Debar, H.: An Introduction to Intrusion-Detection Systems. IBM Research, Zurich Research Laboratory, Säumerstrasse 4, CH–8803 Rüschlikon, Switzerland

    Google Scholar 

  16. KDD Cup 1999 data. https://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html. Accessed 05 Dec 2016

  17. SIGKDD. https://en.wikipedia.org/wiki/SIGKDD. Accessed 05 Dec 2016

Download references

Author information

Authors and Affiliations

  1. National Informatics Centre, Berhampur, India

    Rama Krushna Das

  2. Parala Maharaja Engineering College, Berhampur, India

    Manisha Panda

  3. Department of Electronic Science, Berhampur University, Berhampur, India

    Sanghamitra Dash & Rabindra Kishore Mishra

Authors
  1. Rama Krushna Das
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Manisha Panda
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sanghamitra Dash
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Rabindra Kishore Mishra
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rama Krushna Das .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, University of Kalyani, Kalyani, West Bengal, India

    J. K. Mandal

  2. Department of Computer and System Sciences, Visva Bharati University, Bolpur Santiniketan, West Bengal, India

    Paramartha Dutta

  3. Department of Information Technology, Calcutta Business School, Kolkata, India

    Somnath Mukhopadhyay

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer Nature Singapore Pte Ltd.

About this paper

Cite this paper

Das, R.K., Panda, M., Dash, S., Mishra, R.K. (2017). Application of Artificial Immune System Algorithms for Intrusion Detection. In: Mandal, J., Dutta, P., Mukhopadhyay, S. (eds) Computational Intelligence, Communications, and Business Analytics. CICBA 2017. Communications in Computer and Information Science, vol 776. Springer, Singapore. https://doi.org/10.1007/978-981-10-6430-2_38

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-6430-2_38

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-6429-6

  • Online ISBN: 978-981-10-6430-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation