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Automatic Control and Computer Sciences

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01-12-2023

Assessment of the Effectiveness of an Information Security System

Authors: D. P. Zegzhda, T. N. Saurenko, V. G. Anisimov, E. G. Anisimov

Published in: Automatic Control and Computer Sciences | Issue 8/2023

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Abstract

This article discusses a methodological approach to building models for assessing the effectiveness of a program (project) for creating or modernizing an information security system in the interests of ensuring the sustainability and competitiveness of a company in the face of increasing threats to the integrity, confidentiality, availability, and reliability of information that is important for its activities. At the same time, the effectiveness of a program (project) is understood as the degree of use of the opportunities allocated for their implementation of material, intangible, and temporary resources to achieve the set goals. When mathematically formalizing a generalized efficiency indicator, it is taken into account that the implementation of technical, technological, organizational, and other elements (events) included in this program (project) is accompanied by the influence of many random factors influencing the achievement of their particular goals. The proposed generalized indicator provides a dominant assessment of the effectiveness of programs (projects) taking into account the risks during their implementation.

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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/S0146411618080254CrossRef

Andreev, Yu.S., Dergachev, A.M., and Chernykh, A.K., Information security of automated working places in case of emergencies, Intellektual’nye Tekhnol. Transp., 2019, no. 1, pp. 27–32.

Zegzhda, P.D., Malyshev, E.V., and Pavlenko, E.Yu., The use of an artificial neural network to detect automatically managed accounts in social networks, Autom. Control Comput. Sci., 2017, vol. 51, no. 8, pp. 874–880. https://doi.org/10.3103/s0146411617080296CrossRef

Kalinin, M., Zegzhda, D., and Zavadskii, E., Protection of energy network infrastructures applying a dynamic topology virtualization, Energies, 2022, vol. 15, no. 11, p. 4123. https://doi.org/10.3390/en15114123CrossRef

Saurenko, T.N., Methodology control function realization within the electronic government concept framework, Int. J. Sci. Technol. Res., 2020, vol. 9, no. 2, pp. 6259–6262.

Anisimov, V.G., Saurenko, T.N., Anisimov, E.G., Kasatkin, V.V., and Los, V.P., Information security incident forecasting, Autom. Control Comput. Sci., 2021, vol. 55, no. 8, pp. 903–907. https://doi.org/10.3103/S0146411621080277CrossRef

Zegzhda, P., Zegzhda, D., Pavlenko, E., and Dremov, A., Detecting Android application malicious behaviors based on the analysis of control flows and data flows, Proc. 10th Int. Conf. on Security of Information and Networks, Jaipur, India, 2017, New York: Association for Computing Machinery, 2017, pp. 280–286. https://doi.org/10.1145/3136825.3140583

Solokhov, I.V., Problems of scientific-methodological support of interdepartmental information interaction, Voennaya Mysl’, 2017, no. 12, pp. 45–51.

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

10.

Zegzhda, D.P., Moskvin, D.A., and Dakhnovich, A.D., Protection of Wi-Fi network users against rogue access points, Autom. Control Comput. Sci., 2017, vol. 51, no. 8, pp. 978–984. https://doi.org/10.3103/s0146411617080302CrossRef

11.

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

12.

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/s0146411620080362CrossRef

13.

Zaitseva, E.A., Lavrova, D.S., and Zegzhda, D.P., Neutralization of consequences of destructive actions by means of applying the graph theory for reconfiguration of the system’s structure, Metody Tekh. Sredstva Obespecheniya Bezopasnosti Inf., 2019, no. 28, pp. 7–8.

14.

Zegzhda, P.D., Anisimov, V.G., Anisimov, E.G., Saurenko, T.N., and Los’, V.P., Operational efficiency of computer networks under conditions of malicious cyber activity, Autom. Control Comput. Sci., 2021, vol. 55, no. 8, pp. 1020–1024. https://doi.org/10.3103/s0146411621080332CrossRef

15.

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/s0146411616080289CrossRef

16.

Zegzhda, D.P., Sustainability as a criterion for information security in cyber-physical systems, Autom. Control Comput. Sci., 2016, vol. 50, no. 8, pp. 813–819. https://doi.org/10.3103/s0146411616080253CrossRef

17.

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/s0146411617080053CrossRef

18.

Zegzhda, D.P., Usov, E.S., Nikol’skii, A.V., and Pavlenko, E.Yu., Use of Intel SGX to ensure the confidentiality of data of cloud users, Autom. Control Comput. Sci., 2017, vol. 51, no. 8, pp. 848–854. https://doi.org/10.3103/s0146411617080284CrossRef

19.

Vilkov, V.B., Chernykh, A.K., and Dergachev, A.I., About one approach to creation of information-secure communication systems, Intellektual’nye Tekhnol. Transp., 2019, no. 2, pp. 15–20.

20.

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/s0146411620080374CrossRef

21.

Saurenko, T., Anisimov, V., Anisimov, E., and Levina, A., Comparing investment projects of innovative developing strategies of municipalities, based on a set of indicators, MATEC Web Conf., 2018, vol. 170, p. 01038. https://doi.org/10.1051/matecconf/201817001038

22.

Anisimov, V.G., Anisimov, E.G., and Botvin, G.A., Investitsionnyi analiz v usloviyakh neopredelennosti (Investment Analysis under Uncertainty Conditions), St. Petersburg: 2006, vol. 288.

23.

Il’in, I.V., Anisimov, V.G., Anisimov, E.G., Botvin, G.A., Gapov, M.R., Gasyuk, D.P., Il’yashenko, O.Yu., Levina, A.I., Rodionova, E.S., and Saurenko, T.N., Matematicheskie metody i instrumental’nye sredstva otsenivaniya effektivnosti investitsii v innovatsionnye proekty (Mathematical Methods and Instrumental Tools for Assessing Investment Efficiency to Innovation Projects), St. Petersburg: Strategii Budushchego, 2018.

24.

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/s0146411618080035CrossRef

25.

Kalinin, M., Ovasapyan, T., and Poltavtseva, M., Application of the Learning Automaton Model for Ensuring Cyber Resiliency, Symmetry, 2022, vol. 14, no. 10, p. 2208. https://doi.org/10.3390/sym14102208ADSCrossRef

26.

Saurenko, T., Anisimov, V., Anisimov, E., and Bagaeva, I., Conceptual provisions for assessing the effectiveness of energy saving, E3S Web Conf., 2018, vol. 110, p. 02066. https://doi.org/10.1051/e3sconf/201911002066

27.

Busygin, A., Konoplev, A., Kalinin, M., and Zegzhda, D., Floating genesis block enhancement for blockchain based routing between connected vehicles and software-defined VANET security services, Proc. 11th Int. Conf. on Security of Information and Networks, Cardiff, UK, 2018, New York: Association for Computing Machinery, 2018, p. 24. https://doi.org/10.1145/3264437.3264463

28.

Anisimov, V., Anisimov, E., and Chernysh, A., Model and algorithm for substantiating solutions for organization of high-rise construction project, E3S Web Conf., 2018, vol. 33, p. 03003. https://doi.org/10.1051/e3sconf/20183303003

29.

Saurenko, T., Anisimov, V., Anisimov, E., Smolenskiy, A., and Grashchenko, N., Justification of financing of measures to prevent accidents in power systems, International Scientific Conference Energy Management of Municipal Facilities and Sustainable Energy Technologies EMMFT 2018, Murgul, V. and Pasetti, M., Eds., Advances in Intelligent Systems and Computing, vol. 983, Cham: Springer, 2019, pp. 295–305. https://doi.org/10.1007/978-3-030-19868-8_30

30.

Zaychenko, I., Grashchenko, N., Saurenko, T., Anisimov, V., Anisimov, E., and Zhigulin, V., Models for predicting damage due to accidents at energy objects and in energy systems of enterprises, E3S Web Conf., 2019, vol. 110, p. 02041. https://doi.org/10.1051/e3sconf/201911002041

Title: Assessment of the Effectiveness of an Information Security System
Authors: D. P. Zegzhda
T. N. Saurenko
V. G. Anisimov
E. G. Anisimov
Publication date: 01-12-2023
Publisher: Pleiades Publishing
Published in: Automatic Control and Computer Sciences / Issue 8/2023
Print ISSN: 0146-4116
Electronic ISSN: 1558-108X
DOI: https://doi.org/10.3103/S0146411623080345

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Abstract

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