Cluster Computing

, Volume 22, Supplement 3, pp 5573–5585 | Cite as

Research on classification and recognition of attacking factors based on radial basis function neural network

  • Huan Wang
  • Jian Gu
  • Xiaoqiang Di
  • Dan Liu
  • Jianping ZhaoEmail author
  • Xin Sui


In order to identify the network attack elements better, and solve the nonlinear data multi-classification problem of the network attack elements, this paper presents a classification model and training method based on radial basis neural network. The model uses the training sample error to construct the cost function to solve the minimum value of the cost function and improve the classification accuracy. In the training process of the model, the K-mean algorithm is improved by constructing the average difference between the samples, the number of the hidden layer nodes and the initial value of the basis function center are determined, and the influence of the hidden layer structure on the classification accuracy is reduced. The learning rate in the gradient algorithm is optimized by Q learning method, and the interference of the learning rate to the training of the network parameters is reduced. The OLS algorithm is used to adjust the weights of the hidden layer to the output layer to improve the accuracy of the model classification output. The simulation results show that the model can solve the nonlinear classification problem of network attack well, and the average accuracy rate is improved by about 9% compared with the existing classification methods.


Radial basis function Neural network Attacking elements Nonlinear data multi-classification 



This work was supported in part by Key Science and Technology Project of Jilin Province (20160204019GX) and National High-tech R&D (863) Program of China (2015AA015701).


  1. 1.
    Billings, S.A., Wei, H.L., Balikhin, M.A.: Generalized multi scale radial basis function networks. Neural Netw. 20, 1081–1094 (2007)CrossRefGoogle Scholar
  2. 2.
    Bradley, P.S.: Mangasarian, L.K-plane clustering. J. Glob. Optim. 16(1), 23–37 (2000)CrossRefGoogle Scholar
  3. 3.
    Zhu, L., Chung, F.L., Wang, S.: Generalized fuzzy c-means clustering algorithm with improved fuzzy partitions[J]. IEEE Trans. Syst. Man Cybernet.-Part B 39(3), 578–591 (2009)CrossRefGoogle Scholar
  4. 4.
    Xiu, Y., Wang, S., Wu, X., et al.: The directional similarity-based clustering method DSCM[J]. J. Comput. Res. Dev. 43(8), 1425–1431 (2006)CrossRefGoogle Scholar
  5. 5.
    Jinna, L., Bai, Y.P.: Research and simulation on RBFNN optimized by particle swarm algorithm. Appl. Mech. Mater. 303, 1431–1434 (2013)Google Scholar
  6. 6.
    Flyer, N., Lehto, E., Blaise, S., Wright, G.B., St-Cyr, A.: A guide to RBF-generated finite differences for nonlinear transport: shallow water simulations on a sphere. Lam. J. Comput. Phys. 231, 4078–4095 (2012)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Sun, D., Wan, L.M., Sun, Y.F., Liang, Y.C.: An improved hybrid learning algorithm for RBF neural network. J. Jilin Univ. (Sci. Ed.) 48(5), 817-–822 (2010)zbMATHGoogle Scholar
  8. 8.
    Ding, J.C., Zhang, J., Huang, W.Q., Chen, S.: Laser gyro temperature compensation using modified RBFNN. Sensors 14, 18711–18727 (2014)CrossRefGoogle Scholar
  9. 9.
    Yan, G.H., Lee, R., Kent A., et al.: Towards a Bayesian network game framework for evaluating DDoS attacks and defense. In: CCS’12 Proceedings of the 2012 ACM Conference on Computer and Communications Security. USA:ACM, pp. 553–566 (2012)Google Scholar
  10. 10.
    Hu, G.Y., Qiao, P.L.: An efficient improvement of CMA-ES algorithm for the network security situation prediction. Open Autom. Control Syst. J. 7(1), 1499–1517 (2015)CrossRefGoogle Scholar
  11. 11.
    Aleroud, A., Karabatis, G., Sharma, P., He, P.: Context and semantics for detection of cyber attacks. J. Inf. Comput. Sec. 6(1), 63–92 (2014)Google Scholar
  12. 12.
    Salah, S., Maciá-Fernández, G., Díaz-Verdejo, J.E.A.: A model-based survey of alert correlation techniques. Comput. Netw. 57(5), 1289–1317 (2013)CrossRefGoogle Scholar
  13. 13.
    Bateni, M., Baraani, A., Ghorbani, A.A.: Using artificial immune system and fuzzy logic for alert correlation. J. Netw. Sec. 15, 160–174 (2013)Google Scholar
  14. 14.
    Wang, C.H., Chiou, Y.C.: Alert correlation system with automatic extraction of attack strategies by using dynamic feature weights. J. Comput. Commun. Eng. 5(1), 1–10 (2016)CrossRefGoogle Scholar
  15. 15.
    Han, L.Y., Gao, B., Yang, L.: Study of temperature compensation for laser gyro SINS of land-based missile. Tactical Missile Technol. 4, 81–85 (2013)Google Scholar
  16. 16.
    Shen, J., Miao, L.J., Wu, J.W.: Application and compensation for startup phase of FOG based on RBF neural network. Infrared Laser Eng. 42, 119–124 (2013)Google Scholar
  17. 17.
    Endsley, M.R.: Final reflections: Situation awareness models and measure. J. Cognit. Eng. Decis. Mak. 9(1), 101–111 (2015)CrossRefGoogle Scholar
  18. 18.
    Lenders, V., Tanner, A., Blarer, A.: Gaining an edge in cyberspace with advanced situational awareness. Sec. Privacy IEEE 13(2), 65–74 (2015)CrossRefGoogle Scholar
  19. 19.
    Li, M.Q., Tian, J., Chen, F.Z.: Improving multiclass pattern recognition with a co-evolutionary RBFNN. Pattern Recognit. Lett. 4, 392–406 (2008)CrossRefGoogle Scholar
  20. 20.
    Endsley, M.: Situation awareness misconceptions and misunderstandings. J. Cognit. Eng. Decis. Mak. 9(1), 4–32 (2015)CrossRefGoogle Scholar
  21. 21.
    Sedaghatbaf, A., Azgomi, M.A.: Attack modeling and security evaluation based on stochastic activity networks. Sec. Commun. Netw. 7(4), 715–737 (2014)zbMATHGoogle Scholar
  22. 22.
    Wang, Y.: Research on network security situation prediction based on Markov game theory. Int. J. Sec. Its Appl. 9, 301–308 (2016)Google Scholar
  23. 23.
    Chuang, A.S., Wu, F., Varaiya, P.: A game-theoretic model for generation expansion planning: problem formulation and numerical comparisons. IEEE Trans. Power Syst. 4, 885–891 (2001)CrossRefGoogle Scholar
  24. 24.
    Wang, Y.Z., Yu, M., Li, J.Y., et al.: Stochastic game net and applications in security analysis for enterprise network. Int. J. Inf. Sec. 11(1), 41–52 (2012)CrossRefGoogle Scholar
  25. 25.
    Hu, G.Y., Qiao, P.L.: Cloud belief rule base model for network security situation prediction. IEEE Commun. Lett. 20(5), 1–1 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2017

Authors and Affiliations

  • Huan Wang
    • 1
  • Jian Gu
    • 1
  • Xiaoqiang Di
    • 1
  • Dan Liu
    • 1
  • Jianping Zhao
    • 1
    Email author
  • Xin Sui
    • 1
  1. 1.Department of Computer Science and TechnologyChangchun University of Science and TechnologyChangchunChina

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