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CS-PSO based Intrusion Detection System in Cloud Environment

  • Partha Ghosh
  • Arnab KarmakarEmail author
  • Joy Sharma
  • Santanu Phadikar
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 755)

Abstract

Cloud Computing is a provider of different types of services to the user in the Internet to make it cost-effective, time-effective and contributes an effort-less environment. For all these reasons it attracts huge number of users and makes it vulnerable to security threats. Cloud deals with huge number of data and to prohibit intrusion with huge dataset is quite hectic, so to overcome these security issues Intrusion Detection System (IDS) are deployed. To predict all the intrusions instantly and accurately an appropriate training is required for the IDS. Existence of trivial feature set in the training data enhances the memory space and training time. In this paper the authors have implemented a novel CS-PSO-based IDS to classify attacks rapidly and easily. Here NSL-KDD dataset have been chosen to demonstrate the proficiency of the IDS.

Keywords

Cloud computing Intrusion detection system (IDS) Host intrusion detection system (HIDS) Network intrusion detection system (NIDS) Cuckoo search (CS) Particle swarm optimization (PSO) NSL-KDD dataset 

References

  1. 1.
    Moorthy, M.S., Rajeswari, M.: Virtual host based intrusion detection system for cloud. Int. J. Eng. Technol. 5(6), 5023–5029 (2013)Google Scholar
  2. 2.
    Ghosh, P., Shakti, S., Phadikar, S.: A cloud intrusion detection system using novel PRFCM clustering and KNN based dempster-shafer rule. Int. J. Cloud Appl. Comput. 6(4), 18–35 (2016). IGI GlobalGoogle Scholar
  3. 3.
    Vijayarani, S., Sylviaa, M.: Intrusion detection system- a study. Int. J. Secur. Priv. Trust Manag. 4(1), 31–44 (2015)CrossRefGoogle Scholar
  4. 4.
    Prabhu, G.N., Jain, K., Lawande, N., Kumar, N., Zutshi, Y., Singh, R., Chinchole, J.: Network intrusion detection system. Int. J. Eng. Res. Appl. 4(4), 69–72 (2014)Google Scholar
  5. 5.
    Kumar, B.S., Sekhara, T.C., Raju, P., Ratnakar, M., Baba, S.D., Sudhakar, N.: Intrusion detection system- types and prevention. Int. J. Comput. Sci. Inf. Technol. 4(1), 77–82 (2013)Google Scholar
  6. 6.
    Tavallaee, M., Bagheri, E., Lu, W., Ghorbani, A.: A detailed analysis of the KDD CUP 99 data set. In: IEEE Symposium on Computational Intelligence in Security and Defense Applications (CISDA 2009), pp. 1–6 (2009)Google Scholar
  7. 7.
    Hashizume, K., Rosado, D.G., Fernández-Medina, E., Fernandez, E.B.: An analysis of security issues for cloud computing. J. Internet Serv. Appl. 4(1), 1–13 (2013)CrossRefGoogle Scholar
  8. 8.
    Kene, S.G., Theng, D.P.: A review on intrusion detection techniques for cloud computing and security challenges. In: IEEE 2nd International Conference on Electronics and Communication Systems (ICECS 2015), pp. 227–232 (2015)Google Scholar
  9. 9.
    Ghosh, P., Mandal, A.K., Kumar, R.: An efficient cloud network intrusion detection system. In: Information Systems Design and Intelligent Applications, Advances in Intelligent Systems and Computing, vol. 339, pp. 91–100. Springer, New Delhi (2015)Google Scholar
  10. 10.
    Suguna, N., Thanushkodi, K.: An improved k-nearest neighbor classification using genetic algorithm. Int. J. Comput. Sci. 7(4), 18–21 (2010)Google Scholar
  11. 11.
    Datti, R., Verma, B.: Feature reduction for intrusion detection using linear discriminant analysis. Int. J. Comput. Sci. Eng. 2(4), 1072–1078 (2010)Google Scholar
  12. 12.
    Ghosh, P., Saha, A., Phadikar, S.: Penalty-reward based instance selection method in cloud environment using the concept of nearest neighbor. Procedia Comput. Sci. 89, 82–89 (2016). Science Direct, ElsevierCrossRefGoogle Scholar
  13. 13.
    Pereira, L.A.M., Rodrigues, D., Almeida, T.N.S., Ramos, C.C.O., Souza, A.N., Yang, X., Papa, J.P.: A binary cuckoo search and its application for feature selection. In: Cuckoo Search and Firefly Algorithm, Studies in Computational Intelligence, vol. 512, pp. 141–154. Springer, Cham (2014)Google Scholar
  14. 14.
    Seal, A., Ganguly, S., Bhattacharjee, D., Nasipuri, M., Gonzalo-Martin, C.: Feature selection using particle swarm optimization for thermal face recognition. Applied Computation and Security Systems, Advances in Intelligent Systems and Computing, vol. 304, pp. 25–35, Springer, New Delhi (2015)Google Scholar
  15. 15.
    Ghosh, P., Debnath, C., Metia, D., Dutta, R.: An efficient hybrid multilevel intrusion detection system in cloud environment. IOSR J. Comput. Eng. 16(4), 16–26 (2014)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Partha Ghosh
    • 1
  • Arnab Karmakar
    • 1
    Email author
  • Joy Sharma
    • 1
  • Santanu Phadikar
    • 2
  1. 1.Netaji Subhash Engineering CollegeKolkataIndia
  2. 2.Maulana Abul Kalam Azad University of TechnologyKolkataIndia

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