Skip to main content
SpringerLink
Log in

Attack’s Feature Selection-Based Network Intrusion Detection System Using Fuzzy Control Language

  • Published:
International Journal of Fuzzy Systems Aims and scope Submit manuscript

Abstract

Network intrusion detection system has wide range of disputes due to lack of security over the networks. The network intrusion detection system must be reliable to detect the emerging threats over the networks and perform effectively, efficiently to manage large amount of traffic. This paper proposes entropy-based feature selection to select the important features, layered fuzzy control language to generate fuzzy rules, and layered classifier to detect various network attacks namely neptune, smurf, back, and mailbomb. Layered classifier has improved the performances and reduces the computational time. KDD Dataset which consists of three components, namely “Corrected Dataset,” “10 % Dataset,” and “Full Dataset,” are employed to evaluate the performances of the proposed system. The experiments are carried out using an open source java library called jFuzzyLogic and the results show considerable improvement in the detection rate, reduce the false positive rate, significant improvement in recall value and reduces the computational time.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Mohamed, A.B.,  Idris, N.B., Shanmugum, B.: A brief introduction to intrusion detection system. Commun. Comput. Inform. 330, 263–271 (2012)

    Article  Google Scholar 

  2. Gupta, K.K., Nath, B., Kotagiri, R.: Layered approach using conditional random fields for intrusion detection. IEEE Trans. Dependable Sec. Comput. 7(1), 35–49 (2010)

    Article  Google Scholar 

  3. Devaraju, S., Ramakrishnan, S.: Performance comparison of intrusion detection system using various Techniques: a review. ICTACT J. Commun. Tech. 4(3), 802–812 (2013)

    Article  Google Scholar 

  4. Sheikhan, M., Jadidi, Z., Farrokhi, A.: Intrusion detection using reduced-size RNN based on feature grouping. Neural Comput. Appl. 21(6), 1185–1190 (2010)

    Article  Google Scholar 

  5. Wei, N., Di, H.: A probability approach to anomaly detection with twin support vector machines. J. Shanghai Jiaotong Univ. (Sci.) 15(4), 385–391 (2010)

    Article  Google Scholar 

  6. Sarasamma, S.T., Zhu, Q.A., Huff, J.: Hierarchical Kohonenen net for anomaly detection in network security. IEEE Trans. Syst. Man Cybern. 35(2), 302–312 (2005)

    Article  Google Scholar 

  7. Devaraju, S., Ramakrishnan, S.: Performance analysis of intrusion detection system using various neural network classifiers. IEEE Proc. Int. Conf. Recent Trends Info. Tech. 4, 35–312 (2011)

    Google Scholar 

  8. Devaraju, S., Ramakrishnan, S.: Detection of accuracy for intrusion detection system using neural network classifier. Int. J. Emerg. Tech. Adv. Eng. 3(1), 338–345 (2013)

    Google Scholar 

  9. Wang, G., Hao, J., Ma, H., Huang, L.: A new approach to intrusion detection using Artificial Neural Networks and fuzzy clustering. Elsevier Expert Syst. Appl. 37, 6225–6232 (2010)

    Article  Google Scholar 

  10. Anuar, N.B., Sallehudin, H., Gani, A., Zakari, O.: Identifying false alarm for network intrusion detection system using hybrid data mining and decision tree. Malays. J. Comput. Sci. 21(2), 101–115 (2008)

    Google Scholar 

  11. Devaraju, S., Ramakrishnan, S.: Performance comparison for intrusion detection system using neural network with KDD dataset. ICTACT J. Soft Comput. 4(3), 743–752 (2014)

    Article  Google Scholar 

  12. Horng, S.-J., Ming-Yang, S., Chen, Y.-H., Kao, T.-W., Chen, R-Jian, Lai, J.-L., Perkasa, C.D.: A novel intrusion detection system based on hierarchical clustering and support vector machines. Expert Syst. Appl. 38, 306–313 (2011)

    Article  Google Scholar 

  13. Lin, S.-W., Ying, K.-C., Lee, C.-Y., Lee, Z.-J.: An intelligent algorithm with feature selection and decision rules applied to anomaly intrusion detection. Elsevier Appl. Soft Comput. 12(10), 3285–3290 (2012)

    Article  Google Scholar 

  14. Devaraju, S., Ramakrishnan, S.: Detection of attacks for IDS using association rule mining algorithm. IETE J. Res. 61(6), 624–633 (2015)

    Article  Google Scholar 

  15. Wang, M., Zhao, A.: Investigations of intrusion detection based on data mining. Recent Adv. Comput. Sci. Inform. Eng. 124, 275–279 (2012)

    Article  Google Scholar 

  16. Jiang, M., Gan, Z., Wang, C., Wang, Z.: Research of the intrusion detection model based on data mining. Elsevier Energy Proc 13, 855–863 (2011)

    Article  Google Scholar 

  17. Tajbakhsh, A., Rahmati, M., Mirzaei, A.: Intrusion detection using fuzzy association rules. Elsevier Appl. Soft Comput. 9, 462–469 (2009)

    Article  Google Scholar 

  18. Abuadlla, Y., Kvascev, G., Gajin, S., Jovanovic, Z.: Flow-based anomaly intrusion detection system using two neural network stages. Comput. Sci. Inform. Syst. 11(2), 601–622 (2014)

    Article  Google Scholar 

  19. Chan, G.-Y., Lee, C.-S., Heng, S.-H.: Discovering fuzzy association rule patterns and increasing sensitivity analysis of XML-related attacks. J. Netw. Comput. Appl 36, 829–842 (2013)

    Article  Google Scholar 

  20. Selvakani Kandeeban, S., Rajesh, R.S.: A genetic algorithm based elucidation for improving intrusion detection through condensed feature set by KDD 99 dataset. Inform. Knowl. Manag. 1(1), 1–9 (2011)

    Google Scholar 

  21. Tsang, C.-H., Kwong, S., Wang, H.: Genetic-fuzzy rule mining approach and evaluation of feature selection techniques for anomaly intrusion detection. Elsevier Pattern Recogn. 40, 2373–2391 (2007)

    Article  MATH  Google Scholar 

  22. Saniee A. M., Mohamadi, H., Habibi, J.: Design and analysis of genetic fuzzy systems for intrusion detection in computer networks. Expert Syst. Appl 38, 7067–7075 (2011)

    Article  Google Scholar 

  23. KDD Cup 1999 Intrusion detection data: http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html

  24. Cingolani, P., Alcala-Fdez, J.: jFuzzyLogic: a java library to design fuzzy logic controllers according to the standard for fuzzy control programming. Int. J. Compl. Intelg. Syst. 6(1), 61–75 (2013)

    Article  Google Scholar 

  25. Cingolani, P., Alcala-Fdez, J.: jFuzzyLogic: a robust and flexible fuzzy-logic inference system language implementation. WCCI 2012. IEEE World Congr. Comput. Intell. 76, 1090–1097 (2012)

    Google Scholar 

  26. Tanaka, K.: An Introduction to Fuzzy Logic for Practical Applications. Springer, New York (1996)

    MATH  Google Scholar 

  27. Timothy, J.: Ross: Fuzzy Logic with Engineering Applications. John Wiley & Sons Ltd, Hoboken (2010)

    Google Scholar 

  28. Cingolani P.: jFuzzyLogic: open source fuzzy logic library and FCL language implementation (fcl code). http://jfuzzylogic.sourceforge.net/html/example_fcl.html

  29. Cingolani P.: jFuzzyLogic: open source fuzzy logic library and FCL language implementation (java code). http://jfuzzylogic.sourceforge.net/html/example_java.html

  30. International Electrotechnical Commission (IEC): IEC 1131-Programmable Controllers, IECTC65/WG7/TF8, Januar. http://jfuzzylogic.sourceforge.net/doc/iec_1131_7_cd1.pdf (1997)

Download references

Author information

Authors and Affiliations

  1. Department of Information Technology, Dr. Mahalingam College of Engineering and Technology, Pollachi, Tamil Nadu, 642003, India

    S. Ramakrishnan

  2. Department of Computer Applications, Dr. Mahalingam College of Engineering and Technology, Pollachi, Tamil Nadu, 642003, India

    S. Devaraju

Authors
  1. S. Ramakrishnan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. S. Devaraju
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to S. Devaraju.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ramakrishnan, S., Devaraju, S. Attack’s Feature Selection-Based Network Intrusion Detection System Using Fuzzy Control Language. Int. J. Fuzzy Syst. 19, 316–328 (2017). https://doi.org/10.1007/s40815-016-0160-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40815-016-0160-6

Keywords

Search

Navigation