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Performance Evaluation of Antivirus Systems for Computer Networks

Performance Evaluation of Antivirus Systems for Computer Networks 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Automatic Control and Computer Sciences Springer Journals

Performance Evaluation of Antivirus Systems for Computer Networks

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Publisher
Springer Journals
Copyright
Copyright © Allerton Press, Inc. 2022. ISSN 0146-4116, Automatic Control and Computer Sciences, 2022, Vol. 56, No. 8, pp. 883–887. © Allerton Press, Inc., 2022. Russian Text © The Author(s), 2022, published in Problemy Informatsionnoi Bezopasnosti, Komp’yuternye Sistemy.
ISSN
0146-4116
eISSN
1558-108X
DOI
10.3103/s0146411622080016
Publisher site
See Article on Publisher Site

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.

Journal

Automatic Control and Computer Sciences – Springer Journals

Published: Dec 1, 2022

Keywords: computer network; virus attack; antivirus system; performance

References

  • Problems of security in digital production and its resistance to cyber threats
    Vasil’ev, Yu.S.; Zegzhda, D.P.; Poltavtseva, M.A.
  • The problem of innovative development of information security systems in the transport sector
    Anisimov, V.G.; Anisimov, E.G.; Zegzhda, P.D.; Suprun, A.F.
  • Modelling artificial immunization processes to counter cyberthreats
    Zegzhda, D.; Pavlenko, E.; Aleksandrova, E.
  • Methodology control function realization within the electronic government concept framework
    Saurenko, T.N.; Anisimov, V.G.; Anisimov, E.G.; Sonkin, M.A.
  • Approach to the evaluation of the efficiency of information security in control systems
    Zegzhda, P.D.; Anisimov, V.G.; Sem’yanov, P.V.; Suprun, A.F.; Anisimov, E.G.; Saurenko, T.N.; Los’, V.P.
  • Detection of threats in cyberphysical systems based on deep learning methods using multidimensional time series
    Kalinin, M.O.; Lavrova, D.S.; Yarmak, A.V.
  • Approach to the evaluation of the efficiency of information security in control systems
    Zegzhda, P.D.; Anisimov, V.G.; Sem’yanov, P.V.; Suprun, A.F.; Anisimov, E.G.; Saurenko, T.N.; Los’, V.P.
  • Security analysis based on controlling dependences of network traffic parameters by wavelet transformation
    Lavrova, D.S.; Alekseev, I.V.; Shtyrkina, A.A.
  • A risk-oriented approach to the control arrangement of security protection subsystems of information systems
    Anisimov, V.G.; Zegzhda, P.D.; Anisimov, E.G.; Bazhin, D.A.
  • Approach to presenting network infrastructure of cyberphysical systems to minimize the cyberattack neutralization time
    Lavrova, D.S.; Zaitseva, E.A.; Zegzhda, D.P.
  • Indices of the effectiveness of information protection in an information interaction system for controlling complex distributed organizational objects
    Anisimov, V.G.; Anisimov, E.G.; Zegzhda, P.D.; Saurenko, T.N.; Prisyazhnyuk, S.P.
  • A model of optimal complexification of measures providing information security
    Zegzhda, P.D.; Anisimov, V.G.; Suprun, A.F.; Anisimov, E.G.; Saurenko, T.N.; Los’, V.P.
  • Indices of the effectiveness of information protection in an information interaction system for controlling complex distributed organizational objects
    Anisimov, V.G.; Anisimov, E.G.; Zegzhda, P.D.; Saurenko, T.N.; Prisyazhnyuk, S.P.
  • Model and method for optimizing computational processes in parallel computing systems
    Anisimov, V.G.; Zegzhda, P.D.; Anisimov, E.G.; Saurenko, T.N.; Kasatkin, V.V.
  • Efficiency of ensuring the survivability of logistics information and control systems, E3S Web.
    Anisimov, V.; Anisimov, E.; Saurenko, T.
  • Information theory and statistical mechanics
    Jaynes, E.T.