D. Vasyk, V. Kharchenko

Èlektron. model. 2026, 48(3):03-17

ABSTRACT

An Intelligent Assistance Systems (IAS) study was conducted on the integration of Large Language Models (LLMs) into control loops for a safety-critical systems context, specifically Unmanned Aerial Vehicles (UAVs) performing relevant missions. The key drawbacks of using generative artificial intelligence in operational control tasks were analyzed, among which the stochastic nature of the models, susceptibility to hallucinations, and insufficient predictability of responses were highlighted. To address the identified issues, a method for organizing adaptive automatic control is proposed, implemented as a separate architectural layer of the AI system. This approach ensures deterministic filtering of the language modelʼs output data, enabling the safe transformation of LLM results into clear executive commands. A set of metrics for analyzing system performance indicators and determining the limits of LLM capabilities in interpreting commands and context was identified.

Full text: PDF

KEYWORDS

large language models, human-machine interaction, safety-critical systems, unmanned aerial vehicles, finite-state machines.

REFERENCES

1. Choutri, K., Fadloun, S., Khettabi, A., Lagha, M., Meshoul, S., & Fareh, R. (2025). Leveraging Large Language Models for Real-Time UAV Control. Electronics, 14(21), 4312. https://doi.org/10.3390/electronics14214312
2. Yigit, Y., Ferrag, M.A., Ghanem, M.C., Sarker, I.H., Maglaras, L.A., Chrysoulas, C., Moradpoor, N., Tihanyi, N., & Janicke, H. (2025). Generative AI and LLMs for Critical Infrastructure Protection: Evaluation Benchmarks, Agentic AI, Challenges, and Opportunities. Sensors, 25(6), 1666. https://doi.org/10.3390/s25061666
3. Comisky, T., Smith, L., Roberts, M., Lovejoy, J., Garg, A., Nam, L., Li, A., & Samuels, L. (2025). Towards siloed LLM-based systems for mission-critical planning. AAAI Workshop LM4Plan.
4. Acharya R. (2025) LLM integration in autonomous vehicle systems. World Journal of Advanced Research and Reviews; 26(1): 4107-4116. https://doi.org/10.30574/wjarr.2025.26. 1473
5. Thapaliya A., Jeong D., Kwon G. (2025) Failure Analysis in Safety Critical Systems Using Failure State Machine. Lecture Notes in Electrical Engineering. pp. 540-545 https://doi.org/10.1007/978-981-10-7605-3_89?urlappend=%3Futm_source%3Dresearchgate.net%26utm_ medium%3Darticle
6. Ghimire, A. (2025) Enhancing Cybersecurity in Critical Infrastructure with LLM-Assisted Explainable IoT Systems. Assured and Trusted Computing (SATC). https://doi.org/1109/SATC65530.2025.11137104
7. Kharchenko, V.S., Fesenko, H.V., Kliushnikov, I.M., Brezhniev, Ye.V., Stirenko, S.H., & Mokhor, V.V. (2025). Heterogeneous unmanned systems in hazardous spaces: Classification, usage scenarios, and achievement of situation awareness. Electronic Modeling, 47(3), 46-66
8. Kanarskyi, Y., Kharchenko, V., Orekhov, O., & Ponochovnyi, Y. (2025). Markov modelling of human-machine interaction in an augmented reality environment for UAV/UGV-based hazardous area monitoring systems. Radioelectronic and Computer Systems, 2025(4), 35-54 https://doi.org/10.32620/reks.2025.4.03
9. Tian, Y., Lin F., Li Y., Zhang T. (2025) UAVs Meet LLMs: Overviews and Perspectives. Information Fusion, Volume 122. https://doi.org/10.48550/arXiv.2501.02341
10. Yuan, L., Deng C., Han D., Hwang I. (2025) Next-Generation LLM for UAV (NELV). https://doi.org/10.48550/arXiv.2510.21739
11. Mamodiya, U., Kishor I., Syed A., Sankalkar P. (2025) An Adaptive Human-Robot Interaction Framework. IEEE Access, vol. pp. 198762-198777. https://doi.org/10.1109/ACCESS.2025.3603738
12. Neretin, O., Kharchenko, V. (2025) A Model of Ensuring LLM Cybersecurity. Radioelectronic and Computer Systems, no. 2(114), pp. 201-215 https://doi.org/10.32620/reks.2025.2.13
13. Dharmalingam, B., Mukherjee R., Piggott B., Feng G. (2025) Aero-LLM: A Distributed Framework for Secure UAV Communication. https://doi.org/10.48550/arXiv.2502.05220
14. Glushnkov V.M. Synthesis of digital automata. M., Physmathsted, 1962, 476 p.

Received 23.02.2026

V.F. Zaluzhnyi

Èlektron. model. 2026, 48(3):18-30

ABSTRACT

The existing methodological framework for assessing the survivability of a distributed organizational automated system for troop command and control is analyzed, which identifies its shortcomings, and proposes possible approaches to improving the methodology of its development.

It is well known that one of the priority tasks for enhancing the capabilities of the Armed Forces (AF) of Ukraine in the context of the Russian-Ukrainian war is the digitalization of the AF command and control system as the foundation of the state’s defense force management system, aimed at improving the speed of decision-making by military command bodies and the soundness of those decisions during operations. However, the state of the AFU command and control system, in terms of modern information technologies implementation, still does not fully meet the requirements placed upon it. This state of digitization of Ukraine’s Armed Forces command and control system is caused by the imperfection of the existing methodological framework for evaluating the properties of automated troop command and control systems, particularly their survivability, which does not provide for adherence to unified approaches.

In view of the above, possible conceptual approaches to developing such a methodological framework are proposed along two main lines. The first direction involves the development (improvement) of analytical models for assessing partial survivability indicators of automated troop command and control systems (ATC) using specific methods, with the mandatory formation of an integral (overall) survivability indicator for the system as a whole. The second direction involves determining the survivability indicator of an automated troop command and control system based on simulation modeling of bilateral armed combat between opposing sides in an operation. The positive and negative aspects of the approaches are examined, and structural-logical diagrams of their possible implementation are presented.

Full text: PDF

KEYWORDS

automated troop command and control system, analytical model, simulation model, system survivability, distributed organizational system.

REFERENCES

1. President (2019—…, V.O. Zelenskyy). (2021). On the decision of the National Security and Defense Council of Ukraine dated March 25, 2021 "On the Military Security Strategy of Ukraine". Decree of the President of Ukraine No. 121/2021 dated March 25, 2021. Kyiv. APU. 20 p.
2. President (2019—…, V.O. Zelenskyy). (2021). On the decision of the National Security and Defense Council of Ukraine dated August 20, 2021 "On the Strategic Defense Bulletin of Ukraine". Decree of the President of Ukraine No. 473/2021 dated September 17, 2021. Kyiv. APU. 53 p.
3. Shuenkin, V.O. (2010). On the issue of evaluating the effectiveness of the troop (force) command and control system. Nauka i Oborona (Science and Defense), (4), 23-28.
4. Khazanovych, O.I. (2007). Problematic issues of substantiating requirements for a perspective logistics system of the Armed Forces of Ukraine. Bulletin of Military-Scientific Information of the Central Research Institute of the Armed Forces of Ukraine, Kyiv, (2), 9-13.
5. Bogovik, A.V., Zagorulko, S.S., Kovalev, I.S., Kotenko, I.V., & Masanovets, O.V. (2001). Control theory in military systems. Edited by I.V. Kotenko. Moscow. Ministry of Defense. 320 p.
6. Zagorka, O.M., Mosov, S.P., Sbytnev, A.I., & Stuzhuk, P.I. (2005). Elements of research on complex military systems. Kyiv. National Defense Academy of Ukraine. 100 p.
7. Kirsanov, S.O., & Ostrovskyi, S.M. (2018). Methodological approach to assessing the level of automation of command bodies' work. Modern Information Technologies in the Sphere of Security and Defence, 3(33), 77-80.
8. Tkachuk, P.P., Budaretskyi, Yu.I., Shchavinskyi, Yu.V., & Prokopenko, V.V. (2015). Influence of automated control systems for subunits and artillery fire on the efficiency of its employment. Military-Technical Collection (Viiskovo-tekhnichnyi zbirnyk), (12), 75-82.
9. Romanchenko, I.S., Shuenkin, V.O., Svyda, I.Yu., & Khomchak, R.B. (2020). Development of the theory of contributions of military formations from the established grouping of troops in ensuring their combat capability during combat operations. Monograph. Kyiv. Central Research Institute of the Armed Forces of Ukraine. 288 p.
10. Altukhov, P.K. (Ed.). (1984). Fundamentals of the theory of troop command and control. Moscow. Voenizdat. 221 p.
11. Busel, V.T. (Ed.). (2005). Large explanatory dictionary of the modern Ukrainian language. Kyiv. Irpin. VTF "Perun". 1728 p.
12. Kupriyanov, A.I., & Sakharov, A.V. (n.d.). Theoretical foundations of electronic warfare. Textbook. Moscow. Vuzovskaya Kniga. 356 p.
13. Lenkov, S., Zhurov, G., Zaytsev, D., Tolok, I., Lenkov, E., Bondarenko, T., Gunchenko, Y., Zagrebnyuk, V., & Antonenko, O. (2017). Features of modeling failures of recoverable complex technical object with a hierarchical constructive structure. Eastern-European Journal of Enterprise Technologies, 4(88), 34-42.
14. Anfilatov, V.S., & Emelyanov, A.A. (2003). Systems analysis in management. Moscow. Finance and Statistics. 368 p.
15. Cherkashchenko, A.I. (Ed.). (1981). Methods for evaluating the effectiveness of automated troop command and control systems. Methodological manual. Moscow. Military Publishing House of the USSR Ministry of Defense. 112 p.
16. Lon, V.L., & Malyshev, V.A. (2020). Methodology for constructing conflict models of opposing parties to evaluate the promptness of decision-making for combat operations in the troop command and control system. Aerospace Forces (Vozdushno-kosmicheskie sily), (15), 10-16.

Received 25.03.2026

P.A. Frolov, Master Student,
orcid.org/0009-0007-6264-9402
О.D. Міkhnova, Assoc. Prof.,
e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.;
orcid.org/0000-0002-6558-8509
А.V. Міkhnova, Assoc. Prof.
orcid.org/0000-0001-9877-4298
Kharkiv National University of Radio Electronics
Ukraine, 61166, Kharkiv, Nauky av., 14

Èlektron. model. 2026, 48(3):31-43

ABSTRACT

The problem of choosing a technology stack at the initial stage of information technology (IT) projects in the logistics sector is examined. The wrong choice of technologies can lead to delays, cost overruns, and the final product not meeting business needs. Existing approaches to forming a technology stack are analyzed, their limitations are identified, a method for developing a technology stack that considers the specifics of logistics companies is proposed. This method includes a detailed breakdown of business processes, an in-depth analysis of stakeholders, a tho­rough development of functional (using MoSCoW prioritization) and non-functional require­ments, an assessment of contextual factors (IT infrastructure, staff competencies, budget, and deadlines), as well as the use of practical tools such as a comprehensive technology assessment table based on weighted criteria, a hierarchical model of technology stack stratification, and a quantitative technology compliance index.

Full text: PDF

KEYWORDS

logistics information system, technology stack, IT project management, prioritization of functional requirements

REFERENCES

1. Romanenko, O.M. (2022). Informatsiini tekhnolohii v upravlinni lohistychnymy systema­my: navch. posibnyk. Kyiv: KNEU (in Ukrainian).
2. Buriak, S.I., & Korzhenko, V.M. (2023). Didzhytalizatsiia v lohistytsi: vyklyky ta mozh­lyvosti. Odesa: ONU (in Ukrainian).
3. Savchenko, V.P. (2024) Avtomatyzatsiia lohistychnykh protsesiv: innovatsiini rishennia. Kyiv: Akademiia (in Ukrainian).
4. Chyzhova, H.L., & Serediuk, A.O. (2023). Upravlinnia lantsiuhamy postavok: suchasni pidkhody ta tekhnolohii. Lviv: LNU (in Ukrainian).
5. Meller, R.D., & Ellis, K.P. (2023). Trends in Logistics Optimization: AI and Machine Lear­ning Applications. International Journal of Logistics Management. 2023, 34(2), 215—230.
6. Krykavskyi, Ye.V., Pokhylchenko, O.A., & Fertch, M. (2019). Lohistyka ta upravlinnia lantsiuhamy postavok: navch. posibnyk. Lviv: Lvivska politekhnika (in Ukrainian).
7. Mishchuk, I.P., & Falovska, I.D. (2020). Informatsiini systemy i tekhnolohii v upravlinni lohistychnymy protsesamy. Visnyk Lvivskoho torhovelno-ekonomichnoho universytetu. 2020, 60, pp. 92—98 (in Ukrainian).
8. Lysenko, S.M., & Hrebeniuk, D.S. (2021). Arkhitektura ta tekhnolohichnyi stek suchasnykh informatsiinykh system. Visnyk Khmelnytskoho natsionalnoho universytetu. 2021, Vol. 295, 50—54 (in Ukrainian).
9. Martseniuk, V.P., & Zhyliaiev, I.B. (2018). Metodyka analizu iierarkhii u modeliuvanni skladnykh system. Medychna informatyka ta inzheneriia. 2018, Vol. 4, 20—28 (in Ukrainian).
10. Kovalenko, O.O., & Yatskovska, R.O. (2019). Metodolohiia stvorennia informatsiinykh system dlia lohistychnykh pidpryiemstv: prototypuvannia ta otsinka efektyvnosti. Visnyk Vinnytskoho politekhnichnoho instytutu. 2019, Vol. 3, 84—91 (in Ukrainian).
11. Bereza, A.M., & Kozak, I.A. (2018). Modeliuvannia ta analiz biznes-vymoh v umovakh tsyfrovoi transformatsii. Kyiv: KNEU. (in Ukrainian)
12. Hordiienko, T.M. (2019). Analiz steikkholderiv proektu: metodolohiia, metodyka, instrumenty. Kyiv: KNEU (in Ukrainian).
13. Rozanski, N., & Woods, E. (2011). Software Systems Architecture: Working with Stakeholders Using Viewpoints and Perspectives, 2nd ed. Boston: Addison-Wesley.
14. Kovtun, T.A., & Smokova, T.M. (2020). Vykorystannia matrytsi vidpovidalnosti pry vprovadzhenni informatsiinykh system na pidpryiemstvi. Rynkova ekonomika: suchasna teoriia i praktyka upravlinnia. 2020, Vol. 19, no. 2(45), 324—335 (in Ukrainian).
15. Agile Business Consortium. MoSCoW Prioritisation. Available at: https://www.agilebusiness.org/page/projectframework_moscow [Accessed: 10 October 2025].
16. Glinz, M. (2016). Handling Non-Functional Requirements in Software Projects. Cham: Springer.
17. Bourque, P., & Fairley, R. (2019). Guide to the Software Engineering Body of Knowledge (SWEBOK), ver. 3.0. IEEE Computer Society.

Received 22.10.2025;
after evision 03.11.2025

S.Ya. Hilgurt, A.V. Kovylin

Èlektron. model. 2026, 48(3):44-52

ABSTRACT

A review and comparative analysis of open-source datasets for cyber-defense tasks in digital electrical substations based on artificial intelligence methods are presented in the paper. Open-access articles and thematic repositories containing real network traffic files, labeled scenarios, or other data suitable for use in machine learning tasks were analyzed. A comparative analysis of the sources was performed based on publication type, protocols covered, data presentation format, information openness, and overall suitability for further AI applications. It is shown that datasets focused on the Generic Object Oriented Substation Events (GOOSE) protocol are most frequently available in open access, while multi-protocol and Sampled Values (SV)-oriented resources are less common. It is concluded that for modern AI research in the field of cybersecurity for digital electrical substations, the most useful sources are open-access resources that combine the availability of real-world files, a comprehensive description of the data structure, and a direct focus on such substations.

Full text: PDF

KEYWORDS

digital electrical substation, cyber security, intrusion detection system, artificial intelligence, dataset, dataset publication.

REFERENCES

1. Hilhgrt, S.Ya. (2018). Analysis of the use of hardware acceleration of information protection in automated systems of the energy industry. Modeling and information technologies. Collection of scientific works of the G.E. Pukhov IPME of the NAS of Ukraine, (83), 154—164.  http://nbuv.gov.ua/j-pdf/Mtit_2018_83_21.pdf
2. Ashraf, S., Shawon, M.H., Khalid, H.M., & Muyeen, S.M. (2021). Denial-of-Service attack on IEC 61850-based substation automation system: A crucial cyber threat towards smart substation pathways. Sensors, 21(19), 6415.  https://doi.org/10.3390/s21196415
3. Reda, H.T., Ray, B., Peidaee, P., Anwar, A., Mahmood, A., Kalam, A., & Islam, N. (2021). Vulnerability and impact analysis of the IEC 61850 GOOSE protocol in the smart grid. Sensors, 21(4), 1554.  https://doi.org/10.3390/s21041554
4. Hilhgrt, S.Ya. (2024). Review of the possibilities of using artificial intelligence technologies for cyber protection of digital substations. Proceedings of the scientific-practical conference. NAS of Ukraine, Kyiv (с. 31—36). PIMEE of the NAS of Ukraine.  https://ipme.kiev.ua/wp-content/uploads/2024/06/Матеріали-КБЕ-2024.pdf
5. Quincozes, S.E., Albuquerque, C., Passos, D., & Mossé, D. (2021). A survey on intrusion detection and prevention systems in digital substations. Computer Networks, 184, 107679.  https://doi.org/10.1016/j.comnet.2020.107679
6. Lahza, H., Radke, K., & Foo, E. (2018). Applying domain-specific knowledge to construct features for detecting distributed denial-of-service attacks on the GOOSE and MMS protocols. International Journal of Critical Infrastructure Protection, 20, 48—67.  https://doi.org/10.1016/j.ijcip.2017.12.002
7. Mlot, E.D.G., Saldana, J., Rodríguez, R.J., Kotsiuba, I., & Gañan, C.H. (2024). A dataset to train intrusion detection systems based on machine learning models for electrical substations. Data in Brief, 111153.  https://doi.org/10.1016/j.dib.2024.111153
8. Damian, G.M.E., Jose, S., J.R.R., Igor, K., & Carlos, H.G. (2024, 26 квітня). Dataset to train intrusion detection systems based on machine learning models for electrical substations. Zenodo.  https://zenodo.org/records/15487636
9. PowerDuck: A GOOSE data set of cyberattacks in substations. (б. д.). arXiv.org.  ttps://arxiv.org/abs/2207.04716
10. Zemanek, S., Hacker, I., Wolsing, K., Wagner, E., Henze, M., & Serror, M. (2022, 8 липня). PowerDuck: A GOOSE data set of cyberattacks in substations. Zenodo.  https://zenodo.org/ records/6974112
11. Tobar-Rosero, O.A., Roa-Romero, O.A., Rueda-Carvajal, G.D., Leal-Piedrahita, A., Botero-Vega, J.F., Gutierrez-Betancur, S.A., Branch-Bedoya, J.W., & Zapata-Madrigal, G.D. (2024). GOOSE secure: A comprehensive dataset for in-depth analysis of GOOSE spoofing attacks in digital substations. Energies, 17(23), 6098.  https://doi.org/10.3390/en17236098
12. GitHub — CSCRC-SCREED/QUT-ZSS-2023-GOOSE: The datasets contain a wide variety of network and physical behaviours of an iec-61850-compliant zone substation. the datasets are compatible with actual substation network traffic, including benign GOOSE packets, MALICIOUS GOOSE packets, and benign SV packets. the datasets consist of two versions, including raw datasets and labelled datasets. GitHub.  https://github.com/CSCRC-SCREED/QUT-ZSS-2023-GOOSE
13. GitHub — CSCRC-SCREED/QUT-ZSS-2023-SV: The datasets contain a wide variety of network and physical behaviours of an iec-61850-compliant zone substation. the datasets are compatible with actual substation network traffic, including benign GOOSE packets, benign SV packets, and MALICIOUS SV packets. the datasets consist of two versions, including raw datasets and labelled datasets. (б. д.). GitHub.  https://github.com/CSCRC-SCREED/QUT-ZSS-2023-SV
14. GitHub — smartgridadsc/IEC61850SecurityDataset. GitHub.  https://github.com/smartgridadsc/IEC61850SecurityDataset

Received 22.04.26