Advertisement

Intrusion Detection Model Based on GA Dimension Reduction and MEA-Elman Neural Network

  • Ze ZhangEmail author
  • Guidong Zhang
  • Yongjun Shen
  • Yan Zhu
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 773)

Abstract

Now people are using the network all the time, but the ensuing network attacks are constantly threatening people’s lives, so information security is becoming more and more important. In this paper, an intrusion detection model based on the MEA-Elman neural network is proposed. Firstly, GA algorithm is used to reduce the dimension of the dataset, and then verified by the MEA-Elman network model. The experiment results show that the detection model has high accuracy, which can meet the basic requirements of intrusion detection.

References

  1. 1.
    Aminanto, E.M., Kim, K.: Deep learning in intrusion detection system: an overview. In: International Research Conference on Engineering and Technology (2016)Google Scholar
  2. 2.
    Yang, Y., Huang, H.: Research on intrusion detection based on incremental GHSOM. Chin. J. Comput. 37(5), 1216–1224 (2014)Google Scholar
  3. 3.
    Aditya Shrivastava, M.B., Gupta, H.: A novel hybrid feature selection and intrusion detection based on PCNN and support vector machine. Comput. Technol. Appl. 4, 922–927 (2013)Google Scholar
  4. 4.
    Gautam, S.K., Om, H.: Computational neural network regression model for host based intrusion detection system. Perspect. Sci. 8, 93–95 (2016)CrossRefGoogle Scholar
  5. 5.
    El Farissi, I., Saber, M., Chadli, S., Emharraf, M., Belkasmi, M.G.: The analysis performance of an intrusion detection systems based on neural networkGoogle Scholar
  6. 6.
    Kosek, A.M., Gehrke, O.: Ensemble regression model-based anomaly detection for cyber-physical intrusion detection in smart grids. In: 2016 IEEE Electrical Power and Energy Conference (EPEC) (2016)Google Scholar
  7. 7.
    Sasanka Potluri, C.D.: Accelerated deep neural networks for enhanced intrusion detection system (2016)Google Scholar
  8. 8.
    Tang, J., Cao, Y., Xiao, J., et al.: Predication of plasma concentration of remifentanil based on Elman neural network. J. Central South Univ. 20(11), 3187–3192 (2013)CrossRefGoogle Scholar
  9. 9.
    Zhang, Q., Xu, Z., Zhao, K.: Prediction of data from pollution sources based on Elman neural network. J. South Chin. Univ. Technol. (Nat. Sci. Edn.) 37(5), 135–138 (2009)Google Scholar
  10. 10.
    Chengyi, S., Keming, X., Mingqi, C.: Mind-evolution-based machine learning framework and new development. J. Taiyuan Univ. Technol. 5, 453–457 (1999)Google Scholar
  11. 11.
    Karegowda, A.G., Jayaram, M.A., Manjunath, A.S., et al.: GA based Dimension Reduction for enhancing performance of k-means and fuzzy k-means: a case study for categorization of medical dataset. Adv. Intell. Syst. Comput. 201, 169–180 (2013)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • Ze Zhang
    • 1
    Email author
  • Guidong Zhang
    • 1
  • Yongjun Shen
    • 1
  • Yan Zhu
    • 1
  1. 1.School of Information Science and EngineeringLanzhou UniversityLanzhouChina

Personalised recommendations