Developing a methodological approach to assessing state information security
Abstract
The object of the research is the system of information security of the state.
Investigated problem: The experience of operations (combat operations) of recent years shows the growing role of information influence measures on the systems of collection, processing and transmission of special purpose information and decision-making officials.
The specificity of measures to ensure the information security of the state is that, on the one hand, it is necessary to solve the task of collecting, processing and transmitting information, and on the other hand, it is necessary to counteract measures of information influence on the systems of collecting, processing and transmitting information and decision-making officials.
Given this, information attacks have become a real threat and are one of the priority problems of national security and risk management. Information security covers all security measures that can be taken to protect against these impacts. A significant increase in the complexity and intensity of information attacks in recent years has forced most developed countries to strengthen their defenses and adopt national information security strategies.
The area of practical use of the research results: It is advisable to use the proposed scientific results when conducting research and development works on the creation of intelligent systems for collecting, processing and analyzing information about the state of information security of the state, and developing requirements for hardware and software of this type of systems.
Field of application: software, information systems, decision support systems.
Downloads
References
Kuchuk, N., Mohammed, A. S., Shyshatskyi, A., Nalapko, O. (2019). The method of improving the efficiency of routes selection in networks of connection with the possibility of self-organization. International Journal of Advanced Trends in Computer Science and Engineering, 8 (1.2), 1–6.
Sova, O., Turinskyi, O., Shyshatskyi, A., Dudnyk, V., Zhyvotovskyi, R., Prokopenko, Y. et al. (2020). Development of an algorithm to train artificial neural networks for intelligent decision support systems. Eastern-European Journal of Enterprise Technologies, 1 (9 (103)), 46–55. doi: https://doi.org/10.15587/1729-4061.2020.192711
Makarenko, S. I., Mikhailov, R. L. (2013). Otcenka ustoichivosti seti sviazi v usloviiakh vozdeistviia na nee destabiliziruiushchikh faktorov. Radioengineering and Telecommunication Systems, 4, 69–79.
Bodyanskyy, E. V., Strukov, V. M., Uzlov, D. Yu. (2017). Generalizedmetrics in the problem of analysis of multidimensional data with different scales. Zbirnyk naukovykh prats Kharkivskoho natsionalnoho universytetu Povitrianykh Syl, 3 (52), 98–101.
Semenov, V. V., Lebedev, I. S. (2019). Processing of signal information in problems of monitoring information security of unmanned autonomous objects. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 19 (3), 492–498. doi: https://doi.org/10.17586/2226-1494-2019-19-3-492-498
Zhou, S., Yin, Z., Wu, Z., Chen, Y., Zhao, N., Yang, Z. (2019). A robust modulation classification method using convolutional neural networks. EURASIP Journal on Advances in Signal Processing, 2019 (1). doi: https://doi.org/10.1186/s13634-019-0616-6
Shaheen, E. M., Samir, M. (2013). Jamming Impact on the Performance of MIMO Space Time Block Coding Systems over Multi-path Fading Channel. REV Journal on Electronics and Communications, 3 (1-2), 68–72. doi: https://doi.org/10.21553/rev-jec.56
Malik, S., Kumar, S. (2017). Optimized Phase Noise Compensation Technique using Neural Network. Indian Journal of Science and Technology, 10 (5), 1–6. doi: https://doi.org/10.17485/ijst/2017/v10i5/104348
Rotshteyn, A. P (1999). Intellektual'nyye tekhnologii identifikatsii: nechotkiye mnozhestva, geneticheskiye algoritmy, neyronnyye seti. Vinnitsa: “UNIVERSUM”, 320.
Mazhara, O. A. (2015). Treat algorithm implementation by the basic matchalgorithm based on CLIPS programming environmen. Elektronnoye modelirovaniye, 37 (5), 61‒75.
Bolotova, S. Yu., Makhortov, S. D. (2011). Algoritmy relevantnogo obratnogo vyvoda na osnove resheniya produktsionno-logicheskikh uravneniy. Iskusstvennyy intellekt prinyatiye resheniyi, 2, 40‒50.
Zhyvotovskyi, R. M., Shyshatskyi, A. V., Petruk, S. N. (2017). Structural-semantic model of communication channel. Problems of Infocommunications. Science and Technology. Kharkiv, 524–529. doi: https://doi.org/10.1109/infocommst.2017.8246454

Copyright (c) 2023 Halyna Andriishena, Roman Chunakov, Mykola Zaitsev, Andrii Shyshatskyi

This work is licensed under a Creative Commons Attribution 4.0 International License.
Our journal abides by the Creative Commons CC BY copyright rights and permissions for open access journals.
Authors, who are published in this journal, agree to the following conditions:
1. The authors reserve the right to authorship of the work and pass the first publication right of this work to the journal under the terms of a Creative Commons CC BY, which allows others to freely distribute the published research with the obligatory reference to the authors of the original work and the first publication of the work in this journal.
2. The authors have the right to conclude separate supplement agreements that relate to non-exclusive work distribution in the form in which it has been published by the journal (for example, to upload the work to the online storage of the journal or publish it as part of a monograph), provided that the reference to the first publication of the work in this journal is included.