Cluster Computing

, Volume 22, Supplement 1, pp 265–275 | Cite as

An improved model based on genetic algorithm for detecting intrusion in mobile ad hoc network

  • P. BharathisindhuEmail author
  • S. Selva Brunda


In any data communication between networks, it is very essential to maintain a high level of security to make sure that the data communication is safe and trusted. There may be chance for intrusions and misuses of information in a network. Intrusion detection systems (IDSs) has become an important component in terms of computer and network security. Detection can be host based or network based. The existing approaches being utilized in intrusion detection are not efficient. This paper presents a genetic algorithm based IDS to detect the malicious nodes in the network. Cloud computing understands users in network nodes in ad hoc or fixed manner to have long term connectivity for service utilization. The proposed approach performs better compared to Gaussian Naïve Bayes classifier approach. The objective value of chromosome is calculated through a set of iterations. The experimental results show that the computation time is reduced with high successful detection and network performance is improved to provide reliable transmission. The model is also suitable for users on cloud framework.


Cloud computing Genetic algorithm Intrusion detection system Malicious packets 


  1. 1.
    Lee, J.M., Yu, M.J., Yoo, Y.H., Choi, S.G.: A new scheme of global mobility management for inter-VANETs handover of vehicles in V2V/V21 network environments. In: Fourth International Conference on Networked Computing and Advanced Information Management, pp. 114–118 (2008)Google Scholar
  2. 2.
    Axelsson, S.: Intrusion Detection Systems: A Survey And Taxonomy. Technical report, 99-15 (2000)Google Scholar
  3. 3.
    Friedman, N., Goldszmidt, M.: Building classifiers using Bayesian networks. In: Proceedings of American Association for Artificial Intelligence Conference (AAAI’96), Portland, Oregon, pp. 99–106 (1996)Google Scholar
  4. 4.
    Saravanan, S., Chandrasekaran, R.M.: Intrusion detection system using Bayesian approach. Int. J. Eng. Innov. Technol. 4, 108–116 (2015)Google Scholar
  5. 5.
    Gujar, S.S., Patil, B.M.: Intrusion detection using Naïve Bayes for real time data. Int. J. Adv. Eng. Technol. 7(2), 568–574 (2014)Google Scholar
  6. 6.
    Gumus, F., Okan Sakar, C., Erdem, Z., Kursun, O.: Online naive Bayes classification for network intrusion detection. In: IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 670–674 (2014)Google Scholar
  7. 7.
    Deshmukh, D.H., Ghorpade, T., Padiya, P.: Intrusion detection system by improved preprocessing methods and Naïve Bayes classifier using NSL-KDD 99 dataset. In: International Conference on Electronics and Communication, pp. 1–7 (2014)Google Scholar
  8. 8.
    Sangeetha, K., Periasamy, P.S., Prakash, S.: Identification of network intrusion with efficient Genetic Algorithm using Bayesian classifier. In: International Conference on Computer Communication and Informatics (ICCCI), pp. 1–4 (2015)Google Scholar
  9. 9.
    Senthil Kumar, T., Suresh, A., Karumathil, A.: Improvised classification model for cloud based authentication using keystroke dynamics. In: Frontier and Innovation in Future Computing and Communications. Lecture Notes in Electrical Engineering (LNEE), vol. 301, pp. 885–893. Springer, Heidelberg (2014)Google Scholar
  10. 10.
    Desale, K.S., Ade, R.: Genetic algorithm based feature selection approach for effective intrusion detection system. In: International Conference on Computer Communication and Informatics (ICCCI), pp. 1–6 (2015)Google Scholar
  11. 11.
    Al-Ghazal, M., El-Sayed, A., Kelash, H.: Routing optimization using genetic algorithm in ad hoc networks. In: Proceedings of the 2007 IEEE International Symposium on Signal Processing and Information Technology, pp. 497–503 (2007)Google Scholar
  12. 12.
    Thamilarasu, G.: Genetic algorithm based intrusion detection system for wireless body area networks. In: IEEE Symposium on Computers and Communication (ISCC), pp. 160–165 (2015)Google Scholar
  13. 13.
    Hoque, M.S., Mukit, Md.A., Bikas, Md.A.N.: An implementation of intrusion detection system using genetic algorithm. Int. J. Netw. Secur. Appl. (2012).
  14. 14.
    Pastrana, S., Mitrokotsa, A., Orfila, A., Peris-Lopez, P.: Evaluation of classification algorithms for intrusion detection in MANETs. Knowl. Based Syst. (2012).
  15. 15.
    Sharma, S., Singh, T.P.: An effective intrusion detection system for detection and correction of gray hole attack in MANETs. Int. J. Comput. Appl. (2013).
  16. 16.
    Senthil Kumar, T., Ohhm Prakash, K.I.: A queueing model for e-learning system. In: Artificial Intelligence and Evolutionary Algorithms in Engineering Systems. Advances in Intelligent Systems and Computing, vol. 325, pp. 89–95 (2015).
  17. 17.
    Bawa, K., Rana, S.B.: Prevention of black hole attack in MANET using addition of genetic algorithm to bacterial foraging optimization. Int. J. Curr. Eng. Technol. (2015).
  18. 18.
    Cheng, H., Yang, S.: Genetic algorithms with elitism-based immigrants for dynamic load balanced clustering problem in mobile ad hoc networks. In: IEEE Conference on Computational Intelligence in Dynamic and Uncertain Environments, pp. 1–7 (2011)Google Scholar
  19. 19.
    Poongothai, T., Duraiswamy, K.: Intrusion detection in mobile ad hoc networks using machine learning approach. In: International Conference on Information Communication and Embedded Systems (ICICES2014), Chennai, pp. 1–5 (2014)Google Scholar
  20. 20.
    Barolli, L., Koyama, A., Shiratori, N.: A QoS routing method for ad-hoc networks based on genetic algorithm. In: Proceedings of the 14th International Workshop on Database and Expert Systems Applications, pp. 175–179 (2003)Google Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Bharathiar UniversityCoimbatoreIndia
  2. 2.Cheran College of EngineeringKarurIndia

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