ISSN 0021-3454 (print version)
ISSN 2500-0381 (online version)

vol 67 / May, 2024

DOI 10.17586/0021-3454-2016-59-10-795-800

UDC 004.891.3


I. B. Saenko
St. Petersburg Institute for Informatics and Automation of the RAS, Laboratory of Computer Security Problems; Professor

I. V. Kotenko
St. Petersburg Institute for Informatics and Automation of Russian Academy of Sciences, Laboratory of Computer Security Problems ; Professor

F. A. Skorik
S. M. Budenny Military Academy of Telecommunications; Department of Automated Control Systems for Special Purposes;

Read the full article 

Abstract. Application of for computer networks state monitoring and forecasting based on high adaptability and resistance of hybrid neural networks to external noise is considered. Models for monitoring and forecasting of computer network states using hybrid neural networks are analyzed. Results of experiments demonstrate that the proposed models afford a rather high precision of classification of current and predicted states of computer network.
Keywords: hybrid neural networks, Kohonen map, computer networks, forecasting, monitoring, state indicator

  1. Kotenko I.V., Saenko I.B., Polubelova O.V., Chechulin A.A. Trudy SPIIRAN (SPIIRAS Proceedings), 2012, no. 1(20), pp. 27–56. (in Russ.)
  2. Kotenko I.V., Saenko I.B. Herald of the Russian Academy of Sciences, 2014, no. 11(84), pp. 993–1001. (in Russ.)
  3. Awan Z.K., Khan A., Iftikhar A. Hybrid Neural Networks: from Application Point of View, LAP Lambert Academic Publishing, 2012.
  4. Wermter S., Sun R. An Overview of Hybrid Neural Systems, in: Hybrid Neural Systems, NY, Heidelberg, Springer, 2000.
  5. Chen Y., Kak S., Wang L. Intelligent Information Management, 2010, no. 2, pp. 253–261.
  6. Lawrence S., Giles C.L., Tsoi A.C., Back A.D. Face Recognition: A Hybrid Neural Network Approach, Technical Report, 1996.
  7. Wan L., Zhu L., Fergus R.  Proceedings of the 15th International Conference on Artificial Intelligence and Statistics (AISTATS), La Palma, Canary Islands, 2012, no. 22, pp. 1287–1294.
  8. Psichogios D.C., Ungar L.H. AIChE Journal, 1992, no. 10(38), pp. 1499–1511.
  9. Azruddin A., Gobithasan R., Rahmat B., Azman S., Sureswaran R. Proceedings of the Arab Conference on Information Technology ACIT, 2002, pp. 746–752.
  10. Mishra A., Zaheeruddin Z. Journal of Intelligent Learning Systems and Applications, 2010, no. 2(2), pp. 97–109.
  11. Bahrololum M., Salahi E., Khaleghi M. International Journal of Computer Networks & Communications (IJCNC), 2009, no. 2(1), pp. 26–33.
  12. Garcıa-Teodoro P., Dıaz-Verdejo J., Macia-Fernandez G., Vazquez E. Computers & Security, 2009, no. 28, pp. 18–28.
  13. Zhang Z., Manikopoulos C.  Journal of Neural Network World, 2001, no. 3, pp. 305–316.
  14. Souza L. G.M., Barreto G.A. Revista da Sociedade Brasileira de Redes Neurais (SBRN), 2006, no. 2(4), pp. 112–123.
  15. Kotenko I., Saenko I., Skorik F., Bushuev S. Proceedings of the XVIII International Conference on Soft Computing and Measurements (SCM'2015), IEEE Xplore, 2015, pp. 133–135. DOI: 10.1109/SCM.2015.7190434.
  16. Kasabov N., Hamed H.N.A. International Journal of Artificial Intelligence, 2011, no. 7, pp. 114–124.
  17. Kasabov N.K., Song Q. IEEE Transactions on Fuzzy Systems, 2002, no. 2(10), pp. 144–154.
  18. Kohonen T. Proceedings of the IEEE, 1990, no. 9(78), pp. 1464–1480.