Skip to main content
SpringerLink
Log in

Performance Evaluation of Antivirus Systems for Computer Networks

  • Published:
Automatic Control and Computer Sciences Aims and scope Submit manuscript

Abstract

Computer networks are an important part of modern civilization. They are used in almost all spheres of human activity. The significant losses due to failures of these networks mean there are high requirements for the stability of their operation. In particular, their stability relies on protection against virus attacks. For this purpose, corresponding antivirus systems are developed. As a performance measure for these systems, it is proposed to use the number of network computers that a virus manages to infect before it is detected and removed. In this case, the empirical basis for evaluating the performance of antivirus systems is the data obtained by field tests and/or operational experience. These data are random in nature and their availability is generally rather limited. In this paper we consider an approach to the performance evaluation of antivirus systems for computer networks that takes into account the empirical data mentioned above. The approach is based on a representation of the empirical data as a small sample from a general set of values of a random variable that characterizes the number of network computers the virus manages to infect before it is detected and removed. The distribution function of this variable is used as a test model. This distribution function is constructed based on the principle of maximum uncertainty. Shannon entropy is used as a measure of uncertainty.

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

REFERENCES

  1. Vasil’ev, Yu.S., Zegzhda, D.P., and Poltavtseva, M.A., Problems of security in digital production and its resistance to cyber threats, Autom. Control Comput. Sci., 2018, vol. 52, no. 8, pp. 1090–1100.  https://doi.org/10.3103/S0146411618080254

    Article  Google Scholar 

  2. Anisimov, V., Digital transformation and optimization models in the sphere of logistics, SHS Web Conf., 2018, vol. 44, p. 00009.  https://doi.org/10.1051/shsconf/20184400009

  3. Zegzhda, D., Lavrova, D., and Khushkeev, A., Detection of information security breaches in distributed control systems based on values prediction of multidimensional time series, 2019 IEEE Int. Conf. on Industrial Cyber Physical Systems (ICPS), Taipei, 2019, IEEE, 2019, pp. 780–784.  https://doi.org/10.1109/ICPHYS.2019.8780304

  4. Anisimov, V.G., Anisimov, E.G., Zegzhda, P.D., and Suprun, A.F., The problem of innovative development of information security systems in the transport sector, Autom. Control Comput. Sci., 2018, vol. 52, no. 8, pp. 1105–1110.  https://doi.org/10.3103/S0146411618080035

    Article  Google Scholar 

  5. Zegzhda, D., Pavlenko, E., and Aleksandrova, E., Modelling artificial immunization processes to counter cyberthreats, Symmetry, 2021, vol. 13, no. 12, p. 2453.  https://doi.org/10.3390/sym13122453

    Article  Google Scholar 

  6. Saurenko, T.N., Anisimov, V.G., Anisimov, E.G., and Sonkin, M.A., Methodology control function realization within the electronic government concept framework, Int. J. Sci. Technol. Res., 2020, vol. 9, no. 2, pp. 6259–6262.

    Google Scholar 

  7. Zegzhda, P.D., Anisimov, V.G., Sem’yanov, P.V., Suprun, A.F., Anisimov, E.G., Saurenko, T.N., and Los’, V.P., Approach to the evaluation of the efficiency of information security in control systems, Autom. Control Comput. Sci., 2020, vol. 54, no. 8, pp. 864–870.  https://doi.org/10.3103/S0146411620080362

    Article  Google Scholar 

  8. Anisimov, V.G., Anisimiov, E.G., Saurenko, T.N., and Zotova, E.A., Models of forecasting destructive influence risks for information processes in management systems, Inf. Control Syst., 2019, no. 5, pp. 18–23.  https://doi.org/10.31799/1684-8853-2019-5-18-23

  9. Kalinin, M.O., Lavrova, D.S., and Yarmak, A.V., Detection of threats in cyberphysical systems based on deep learning methods using multidimensional time series, Autom. Control Comput. Sci., 2018, vol. 52, pp. 912–917. https://doi.org/10.3103/S0146411618080151

    Article  Google Scholar 

  10. Zegzhda, P.D., Anisimov, V.G., Sem’yanov, P.V., Suprun, A.F., Anisimov, E.G., Saurenko, T.N., and Los’, V.P., Approach to the evaluation of the efficiency of information security in control systems, Autom. Control Comput. Sci., 2020, vol. 54, pp. 864–870.  https://doi.org/10.3103/S0146411620080362

    Article  Google Scholar 

  11. Lavrova, D.S., Alekseev, I.V., and Shtyrkina, A.A., Security analysis based on controlling dependences of network traffic parameters by wavelet transformation, Autom. Control Comput. Sci., 2018, vol. 52, pp. 931–935. https://doi.org/10.3103/S0146411618080187

    Article  Google Scholar 

  12. Anisimov, V.G., Zegzhda, P.D., Anisimov, E.G., and Bazhin, D.A., A risk-oriented approach to the control arrangement of security protection subsystems of information systems, Autom. Control Comput. Sci., 2016, vol. 50, no. 8, pp. 717–721.  https://doi.org/10.3103/S0146411616080289

    Article  Google Scholar 

  13. Lavrova, D.S., Zaitseva, E.A., and Zegzhda, D.P., Approach to presenting network infrastructure of cyberphysical systems to minimize the cyberattack neutralization time, Autom. Control Comput. Sci., 2019, vol. 53, no. 8, pp. 387–392.  https://doi.org/10.3103/S0146411619050067

    Article  Google Scholar 

  14. Anisimov, V.G., Anisimov, E.G., Zegzhda, P.D., Saurenko, T.N., and Prisyazhnyuk, S.P., Indices of the effectiveness of information protection in an information interaction system for controlling complex distributed organizational objects, Autom. Control Comput. Sci., 2017, vol. 51, no. 8, pp. 824–828.  https://doi.org/10.3103/S0146411617080053

    Article  Google Scholar 

  15. Zegzhda, P.D., Anisimov, V.G., Suprun, A.F., Anisimov, E.G., Saurenko, T.N., and Los’, V.P., A model of optimal complexification of measures providing information security, Autom. Control Comput. Sci., 2020, vol. 54, no. 8, pp. 930–936.  https://doi.org/10.3103/S0146411620080374

    Article  Google Scholar 

  16. Anisimov, V.G., Anisimov, E.G., Zegzhda, P.D., Saurenko, T.N., and Prisyazhnyuk, S.P., Indices of the effectiveness of information protection in an information interaction system for controlling complex distributed organizational objects, Autom. Control Comput. Sci., 2017, vol. 51, no. 8, pp. 824–828.  https://doi.org/10.3103/S0146411617080053

    Article  Google Scholar 

  17. Zotova, E., Tebekin, A., Yastrebov, O., and Borremans, A., Realization of the energy strategy of the Russian Federation regarding the transportation of energy resources, E3S Web Conf., 2019, vol. 110, p. 02053. https://doi.org/10.1051/e3sconf/201911002053

  18. Anisimov, V.G., Zegzhda, P.D., Anisimov, E.G., Saurenko, T.N., and Kasatkin, V.V., Model and method for optimizing computational processes in parallel computing systems, Autom. Control Comput. Sci., 2019, vol. 53, no. 8, pp. 1038–1044.  https://doi.org/10.3103/S0146411619080054

    Article  Google Scholar 

  19. Anisimov, V., Anisimov, E., and Saurenko, T., Efficiency of ensuring the survivability of logistics information and control systems, E3S Web. Conf., 2020, vol. 217, p. 07025.  https://doi.org/10.1051/e3sconf/202021707025

    Article  Google Scholar 

  20. Zotova, E., Anisimov, V., Anisimov, E., Melnik, D., and Chernysh, A., A model for setting up development programs for logistics systems in the electric power industry to achieve electric power security, E3S Web Conf., 2021, vol. 258, p. 02027. https://doi.org/10.1051/e3sconf/202125802027

  21. Jaynes, E.T., Information theory and statistical mechanics, Phys. Rev., 1957, vol. 106, p. 620. https://doi.org/10.1103/PhysRev.106.620

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Peter the Great St. Petersburg Polytechnic University, 195251, St. Petersburg, Russia

    V. G. Anisimov

  2. Peoples’ Friendship University of Russia (RUDN University), 117198, Moscow, Russia

    E. G. Anisimov & T. N. Saurenko

  3. Russian Technological University (MIREA), 119454, Moscow, Russia

    V. P. Los’

Authors
  1. V. G. Anisimov
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. E. G. Anisimov
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. T. N. Saurenko
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. V. P. Los’
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to V. G. Anisimov.

Ethics declarations

The authors declare that they have no conflicts of interest.

Additional information

Translated by Yu. Kornienko

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Anisimov, V.G., Anisimov, E.G., Saurenko, T.N. et al. Performance Evaluation of Antivirus Systems for Computer Networks. Aut. Control Comp. Sci. 56, 883–887 (2022). https://doi.org/10.3103/S0146411622080016

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.3103/S0146411622080016

Keywords:

Search

Navigation