Skip to main content
SpringerLink
Log in

Approach to Classifying the State of a Network Based on Statistical Parameters for Detecting Anomalies in the Information Structure of a Computing System

  • SOFTWARE–HARDWARE SYSTEMS
  • Published:
Cybernetics and Systems Analysis Aims and scope

Abstract

An approach to the classification of the state of a network based on of statistical parameters is investigated. Deficiencies of methods for classifying the state of a network are established and the basic implementation of a committee of classifier is considered. A modification of the committee of classifiers using a neural network as a metaclassifier is proposed. Experiments on classifying the state of a network were carried out.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. C. Bronk and E. Tikk-Ringas, “The cyber attack on Saudi Aramco,” Survival, Vol. 55, No. 2, 81–96 (2013).

    Article  Google Scholar 

  2. Flamer — the Most Complex Virus Threat of the Last Time. URL: https://xakep.ru/2013/10/19/flamer-samaya-slozhnaya-virusnaya-ugroza-poslednego-vremeni (access date 07. 04.2017).

  3. I. Ruban, V. Martovitsky, and N. Lukova-Chuiko, “Designing a monitoring model for cluster super-computers,” Eastern-European Journal of Enterprise Technologies, Vol. 6, No. 2, 32–37 (2016).

    Article  Google Scholar 

  4. K. V. Vorontsov, Machine Learning (a Course of Lectures) (2009). URL: http://bit. ly/ml_course (access date 07.04.2017).

  5. K. S. Smelyakov, “Synthesis of effectual segmentation strategy with the use of decision tree,” Systems of Arms and Military Equipment, No. 4, 125–127 (2013).

  6. G. Oke, G. Loukas, and E. Gelenbe, “Detecting denial of service attacks with Bayesian classifiers and the random neural network,” in: Fuzzy Systems Conference (2007) (FUZZ-IEEE 2007), IEEE International, IEEE (2007), pp. 1–6.

  7. O. S. Vysochina, S. I. Shmatkov, and L. M. Salman, “Analysis of telecommunications networks monitoring systems,” Radio Electronics, Computer Science, Control, Vol. 23, No. 2, 139–142 (2010).

    Google Scholar 

  8. V. N. Vapnik and A. Ya. Chervonenkis, Theory of Pattern Recognition [in Russian], Nauka, Moscow (1974).

    Google Scholar 

  9. A. G. Ivakhnenko, Systems of Heuristic Self-Organization in Technical Cybernetics [in Russian], Tekhnika, Kyiv (1971).

    Google Scholar 

  10. G. S. Lbov. Methods of Processing Heterogeneous Experimental Data [in Russian], Nauka, Novosibirsk (1981).

    MATH  Google Scholar 

  11. K. V. Vorontsov, Mathematical Methods of Training on Precedents (Machine Learning Theory). URL: http://www.machinelearning.ru/wiki/images/6/6d/Voron-ML-1.pdf (access date 07.04.2017).

  12. D. H. Wolpert, “Stacked Generalization,” Neural Networks, Vol. 5, No. 2, 241–259 (1992).

    Article  Google Scholar 

  13. A. G. D’yakonov, “Solution methods for classification problems with categorical attributes,” Applied. Mathematics and Informatics, Vol. 46, 103–127 (2014).

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Kharkiv National University of Radioelectronics, Kharkiv, Ukraine

    I. Ruban & V. Martovytskyi

  2. Taras Shevchenko National University of Kyiv, Kyiv, Ukraine

    N. Lukova-Chuiko

Authors
  1. I. Ruban
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. V. Martovytskyi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. N. Lukova-Chuiko
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to I. Ruban.

Additional information

Translated from Kibernetika i Sistemnyi Analiz, No. 2, March–April, 2018, pp. 142–150.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ruban, I., Martovytskyi, V. & Lukova-Chuiko, N. Approach to Classifying the State of a Network Based on Statistical Parameters for Detecting Anomalies in the Information Structure of a Computing System. Cybern Syst Anal 54, 302–309 (2018). https://doi.org/10.1007/s10559-018-0032-1

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10559-018-0032-1

Keywords

Search

Navigation