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A Harmony Search-Based Feature Selection Technique for Cloud Intrusion Detection

  • Widad Mirghani Makki
  • Maheyzah M.D. Siraj
  • Nurudeen Mahmud IbrahimEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1073)

Abstract

Recently cloud computing has enjoyed widespread patronage due to its economy of scale and flexibility. However Cloud computing is confronted with security challenges. Intrusion detection can be used to protect computer resources from unauthorized access. However the presence of insignificant features in Intrusion Detection dataset may have a negative effect on the accuracy of Intrusion Detection System (IDS). Feature selection is utilized to remove noisy and insignificant attribute to improve IDS performance. However, existing feature selection techniques proposed for cloud IDS cannot guarantee optimal performance. Therefore, this research article proposes a Harmony Search based feature selection technique to improve the performance of cloud IDS. The attributes selected were assessed using Random Forest classifier and experimental results of the Harmony Search based technique achieved an attack detection rate of 79% and a false alarm rate of 0.012%. In addition performance comparison shows that the proposed Harmony search outperforms existing feature selection technique proposed for cloud IDS.

Keywords

Feature selection Intrusion detection Harmony Search Cloud computing 

Notes

Acknowledgement

We would like to thank Malaysia’s Ministry of Higher Education (MOHE) and Universiti Teknologi Malaysia (UTM) for funding this work through research grant with vote number (0G73).

References

  1. 1.
    Mell, P., Grance, T.: The NIST definition of cloud computing. http://faculty.winthrop.edu/domanm/csci411/Handouts/NIST.pdf. Accessed Feb 2016
  2. 2.
    Khorshed, M.T., Ali, A.S., Wasimi, S.A.: A survey on gaps, threat remediation challenges and some thoughts for proactive attack detection in cloud computing. Future Gener. Comput. Syst. 28, 833–851 (2012)CrossRefGoogle Scholar
  3. 3.
    Scarfone, K., Mell, P.: Guide to intrusion detection and prevention systems (idps). NIST special publication 800-94 (2007)Google Scholar
  4. 4.
    Liao, H.-J., Lin, C.-H.R., Lin, Y.-C., Tung, K.-Y.: Intrusion detection system: a comprehensive review. J. Netw. Comput. Appl. 36(1), 16–24 (2014)CrossRefGoogle Scholar
  5. 5.
    Aghdam, M.H., Kabiri, P.: Feature selection for intrusion detection system using ant colony optimization. Int. J. Netw. Secur. 18(3), 420–432 (2016)Google Scholar
  6. 6.
    Kannan, A., Maguire, G.Q., Sharma, A., Schoo, P.: Genetic algorithm based feature selection algorithm for effective intrusion detection in cloud networks. In: Proceedings of the 2012 IEEE 12th International Conference on Data Mining Workshops (ICDMW), Brussels, Belgium, 10 December 2012, pp. 416–423. IEEE (2012)Google Scholar
  7. 7.
    Kang, S.-H., Kim, K.J.: A feature selection approach to find optimal feature subsets for the network intrusion detection system. Cluster Comput., 1–9 (2016)Google Scholar
  8. 8.
    Hira, Z.M., Gillies, D.F.: A review of feature selection and feature extraction methods applied on microarray data. Advances in bioinformatics (2015). https://www.hindawi.com/journals/abi/2015/198363/abs/. Accessed January 2018
  9. 9.
    Osanaiye, O., Cai, H., Choo, K.-K.R., Dehghantanha, A., Xu, Z., Dlodlo, M.: Ensemble-based multi-filter feature selection method for DDoS detection in cloud computing. EURASIP J. Wirel. Commun. Netw. 1, 130 (2016)CrossRefGoogle Scholar
  10. 10.
    Chandrashekar, G., Sahin, F.: A survey on feature selection methods. Comput. Electr. Eng. 40(1), 16–28 (2014)CrossRefGoogle Scholar
  11. 11.
    Muthurajkumar, S., Kulothungan, K., Vijayalakshmi, M., Jaisankar, N., Kannan, A.: A rough set based feature selection algorithm for effective intrusion detection in cloud model. In: Proceedings of the International Conference on Advances in Communication, Network and Computing, pp. 8–13 (2013)Google Scholar
  12. 12.
    Zhou, L.-H., Liu, Y.-H., Chen, G.-L.: A feature selection algorithm to intrusion detection based on cloud model and multi-objective particle swarm optimization. In: Proceedings of the 2011 Fourth International Symposium on Computational Intelligence and Design (ISCID), Hangzhou, China, 28–30 October 2011, pp. 182–185. IEEE (2011)Google Scholar
  13. 13.
    Ibrahim, N.M., Zainal, A.: A feature selection technique for Cloud IDS using Ant Colony Optimization and Decision Tree. Adv. Sci. Lett. 23(9), 9163–9169 (2017)CrossRefGoogle Scholar
  14. 14.
    Jensen, R., Shen, Q.: Fuzzy-rough data reduction with ant colony optimization. Fuzzy Sets Syst. 149(1), 5–20 (2005)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Aljawarneh, S., Aldwairi, M., Yassein, M.B.: Anomaly-based intrusion detection system through feature selection analysis and building hybrid efficient model. J. Comput. Sci. 25, 152–160 (2018)CrossRefGoogle Scholar
  16. 16.
    Pham, N.T., Foo, E., Suriadi, S., Jeffrey, H., Lahza, H.F.M.: Improving performance of intrusion detection system using ensemble methods and feature selection. In: Proceedings of the Australasian Computer Science Week Multiconference, Brisbane, Australia, 29 January–2 February 2018, p. 2. ACM (2018)Google Scholar
  17. 17.
    Geem, Z.W.: Novel derivative of harmony search algorithm for discrete design variables. Appl. Math. Comput. 199, 223–230 (2008)MathSciNetzbMATHGoogle Scholar
  18. 18.
    Hall, M.A., Smith, L.A.: Feature subset selection: a correlation based filter approach. In: Proceedings of the 1997 International Conference on Neural Information Processing and Intelligent Information Systems, pp. 855–858. Springer, Berlin (1997)Google Scholar
  19. 19.
    Singh, D., Patel, D., Borisaniya, B., Modi, C.: Collaborative ids framework for cloud. Int. J. Netw. Secur. 18(4), 699–709 (2016)Google Scholar
  20. 20.
    Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Widad Mirghani Makki
    • 1
  • Maheyzah M.D. Siraj
    • 1
  • Nurudeen Mahmud Ibrahim
    • 1
    Email author
  1. 1.Faculty of Engineering, School of ComputingUniversiti Teknologi MalaysiaJohor BahruMalaysia

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