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A New Intrusion Detection System Based on Gated Recurrent Unit (GRU) and Genetic Algorithm

  • Mahdi ManaviEmail author
  • Yunpeng Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11611)

Abstract

Distributed systems are extensive nowadays. The challenge of preventing network penetration by malware and hackers in these systems has been extensively considered and many types of research have been carried out in this field. Due to the high volume of input and output data in distributed systems, definitive and static algorithms that are used for small environments are not appropriate. For this problem, one of the new techniques is the deep learning method, which allows one to find optimal answers. In this paper, deep learning is used to investigate the behavior patterns of requests that enter the distributed network and then attacks are detected based on these patterns, which send an alarm to administrators (Anomaly Detection). In the next step, the genetic algorithm is used with the rule-based database to examine misuse detection. In this paper, considering the results obtained, it can be seen that the proposed algorithm provides high accuracy in detecting attacks with a low false alarm rate.

Keywords

Intrusion detection Recurrent neural network Gated recurrent unit Genetic algorithm KDD Hybrid detection method 

References

  1. 1.
    Mishraa, P., Varadharajan, V., Pilli, E., Varadharajan, V., Tupakulab, U.: Intrusion detection techniques in cloud environment: a survey. J. Netw. Comput. Appl. 77, 18–47 (2017)CrossRefGoogle Scholar
  2. 2.
    Barbara, D., Jordia, S.: Applications of Data Mining in Computer Security, vol. 6. Springer, New York (2002).  https://doi.org/10.1007/978-1-4615-0953-0CrossRefGoogle Scholar
  3. 3.
    Milenkoski, A., Vieira, M., Kounev, S., Avritzer, A., Payne, B.D.: Evaluating computer intrusion detection systems: a survey of common practices. ACM Comput. Surv. (CSUR) 48(1), 12 (2015)CrossRefGoogle Scholar
  4. 4.
    Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. In: EMNLP (2014)Google Scholar
  5. 5.
    Subba, B., Biswas, S., Karmakar, S.: A neural network based system for intrusion detection and attack classification. In: Twenty Second National Conference on Communication (NCC). IEEE, India (2016)Google Scholar
  6. 6.
    Xu, C., Shen, J., Du, X., Zhang, F.: An intrusion detection system using a deep neural network with gated recurrent units. IEEE Access 6, 48697–48707 (2018)CrossRefGoogle Scholar
  7. 7.
    Nguyen, S., Nguyen, V., Choi, J., Kim, K.: Design and implementation of intrusion detection system using convolutional neural network for DoS detection. In: Proceedings of the 2nd International Conference on Machine Learning and Soft Computing, ICMLSC 2018. ACM, Vietnam (2018)Google Scholar
  8. 8.
    Fu, Y., Lou, F., Meng, F., Tian, Z., Zhang, H., Jiang, F.: An intelligent network attack detection method based on RNN. In: Third International Conference on Data Science in Cyberspace (DSC). IEEE, China (2018)Google Scholar
  9. 9.
    Balamurugan, V., Saravanan, R.: Enhanced intrusion detection and prevention system on cloud environment using hybrid classification and OTS generation. Cluster Comput. 1–13 (2017)Google Scholar
  10. 10.
    Kim, J., Shin, N., Jo, S., Kim, S.: Method of intrusion detection using deep neural network. In: International Conference on Big Data and Smart Computing (BigComp). IEEE, South Korea (2017)Google Scholar
  11. 11.
    Maleh, Y., Ezzati, A., Qasmaoui, Y., Mbida, M.: A global hybrid intrusion detection system for wireless sensor networks. Procedia Comput. Sci. 52, 1047–1052 (2015)CrossRefGoogle Scholar
  12. 12.
    Kim, J., Kim, J, Thu, HLT., Kim, H.: Long short term memory recurrent neural network classifier for intrusion detection. In: International Conference on Platform Technology and Service. IEEE, South Korea (2016)Google Scholar
  13. 13.
    Ishitaki, R.T., Obukata, Y., Oda, T., Barolli, L.: Application of deep recurrent neural networks for prediction of user behavior in Tor networks. In: 31st International Conference on Advanced Information Networking and Applications Workshops. IEEE, Taiwan (2017)Google Scholar
  14. 14.
    Dong, B., Wang, X.: Comparison deep learning method to traditional methods using for network intrusion detection. In: 8th IEEE International Conference on Communication Software and Networks. IEEE, China (2016)Google Scholar
  15. 15.
    Roy, S.S., Mallik, A., Gulati, R., Obaidat, M.S., Krishna, P.V.: A deep learning based artificial neural network approach for intrusion detection. In: Giri, D., Mohapatra, R.N., Begehr, H., Obaidat, M.S. (eds.) ICMC 2017. CCIS, vol. 655, pp. 44–53. Springer, Singapore (2017).  https://doi.org/10.1007/978-981-10-4642-1_5CrossRefGoogle Scholar
  16. 16.
    Kingma, D., Ba, J.: Adam: a method for stochastic optimization. In: 3rd International Conference for Learning Representations. arXiv, USA (2015)Google Scholar
  17. 17.
    Thaseen, I.S., Kumar, C.A.: Intrusion detection model using fusion of chi-square feature selection and multi class SVM. J. King Saud Univ. Comput. Inf. Sci. 29(4), 462–472 (2017)CrossRefGoogle Scholar
  18. 18.
    Ali, M.H., Al Mohammed, B.A.D., Ismail, A., Zolkipli, M.F.: A new intrusion detection system based on fast learning network and particle swarm optimization. IEEE Access 6, 20255–20261 (2018)CrossRefGoogle Scholar
  19. 19.
    Agarap, A.F.M.: A neural network architecture combining Gated Recurrent Unit (GRU) and Support Vector Machine (SVM) for intrusion detection in network traffic data. In: Proceedings of the 2018 10th International Conference on Machine Learning and Computing, ICMLC 2018. ACM, China (2018)Google Scholar
  20. 20.
    Gurley Bace, R.: Intrusion Detection. Sams Publishing, USA (2000)Google Scholar
  21. 21.
    Yao, J., Zhao, S., V. Saxton, L.: A study on fuzzy intrusion detection. In: Proceedings Volume 5812, Data Mining, Intrusion Detection, Information Assurance, and Data Networks Security (2005)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Mirdamad Institute of Higher Education of GorganGorganIran
  2. 2.Department of Information and Logistics TechnologyUniversity of HoustonHoustonUSA

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