DEVELOPMENT OF A COMPREHENSIVE INDICATOR FOR DIAGNOSING MASSIVE MISSILE STRIKES

Main Article Content

Oleksandr Laktionov
Oleksandr Shefer
Serhii Fryz
Viktors Gopejenko
Viktor Kosenko

Abstract

Objective. Enhancing the efficiency of diagnosing the threat level of massive missile strikes by developing a comprehensive indicator. Methodology. The study examines the process of developing a comprehensive indicator based on a dataset of massive missile strikes. This involves preliminary data processing, the development of a comprehensive indicator model, and the integration of individual indicators. One of the integrated indicators is assigned a weight coefficient, which is determined using artificial intelligence methods and constrained by a sigmoid activation function. A comparative analysis of the proposed comprehensive indicator against existing indicators was conducted based on the standard deviation criterion. The assessments obtained using the comprehensive indicator are employed to determine the threat level of massive missile strikes. Results. Based on an existing dataset of massive missile strikes on Ukraine, a comprehensive indicator has been developed, consolidating attack characteristics into a unified assessment. The comprehensive indicator's evaluations regarding massive missile strikes are utilized to determine the threat level (Cluster 1 – low threat level, Cluster 2 – high threat level). Scientific novelty. The proposed comprehensive indicator model differs from existing approaches in that its integrated indicators account for mean values and variations in assessments, serving as a prototype of the regularization concept. As a result, the standard deviation is reduced to 0.0925, whereas the existing approach demonstrates a deviation of 0.447 on a single experimental set of assessments. Practical significance. The proposed comprehensive indicator of massive missile strikes serves as an additional measure for determining the state's threat level or may be considered an element of a decision-making system.

Article Details

How to Cite
Laktionov , O. ., Shefer , O. ., Fryz , S. ., Gopejenko , V. ., & Kosenko , V. . (2025). DEVELOPMENT OF A COMPREHENSIVE INDICATOR FOR DIAGNOSING MASSIVE MISSILE STRIKES. Advanced Information Systems, 9(2), 44–50. https://doi.org/10.20998/2522-9052.2025.2.06
Section
Methods of information systems synthesis
Author Biographies

Oleksandr Laktionov , National University “Yuri Kondratyuk Poltava Polytechnic”, Poltava

Candidate of Technical Sciences, Associate Professor of the Department of Automation, Electronics and Telecommunications

Oleksandr Shefer , National University “Yuri Kondratyuk Poltava Polytechnic”, Poltava

Doctor of Technical Sciences, Professor, Head of the Department of Automation, Electronics and Telecommunications

Serhii Fryz , Zhytomyr Korolov Military Institute, Zhуtomуr

Doctor of Technical Sciences, Professor, Professor of the Department of Telecommunications and Radio Engineering

Viktors Gopejenko , ISMA University of Applied Sciences, Riga

Doctor of Sciences (Engineering), Professor, Vice-Rector for Research, Director of Study Programme Computer Systems (MSc) ISMA University of Applied Sciences, Riga; Leading Researcher Ventspils University of Applied Sciences, Ventspils

Viktor Kosenko , National University "Yuri Kondratyuk Poltava Polytechnic", Poltava

Doctor of Sciences (Engineering), Professor, Professor of the Department of Automation, Electronics and Telecommunications

References

Argenti, F., Landucci, G., Spadoni, G. and Cozzani, V. (2015), “The assessment of the attractiveness of process facilities to terrorist attacks”, Safety Science, vol. 77, pp. 169–181, doi: https://doi.org/10.1016/j.ssci.2015.02.013

Huang, C.-N., Liou, J. J. H., Lo, H.-W. and Chang, F.-J. (2021), “Building an assessment model for measuring airport resilience”, Journal of Air Transport Management”, vol. 95, 102101, doi: https://doi.org/10.1016/j.jairtraman.2021.102101

Li, J.-F., Hu, Y.-L. and Zou, W.-G. (2023)., “Dynamic risk assessment of emergency evacuation in large public buildings: A case study”, International Journal of Disaster Risk Reduction, 103659, doi: https://doi.org/10.1016/j.ijdrr.2023.103659

Bygun, V. and Kruk, S. (2024), “Development of software for calculating evacuation time for visitors to a shopping and entertainment center”, Mathematical Machines and Systems, vol. 3-4, pp. 78–92, available at: http://www.immsp.kiev.ua/publications/articles/2024/2024_3_4/03_04_24_Begun.pdf

Onyshchenko S., Yanko A., Hlushko A., Maslii O. and Cherviak A. (2023), “ Cybersecurity and Improvement of the Information Security System”, Journal of the Balkan Tribological Association, vol. 29(5), pp. 818–835, doi: https://scibulcom.net/en/article/L8nV7It2dVTBPX09mzWB

Pushkarenko, Y. and Zaslavskyi, V. (2024), “Research on the state of areas in Ukraine affected by military actions based on remote sensing data and deep learning architectures”, Radioelectronic and Computer Systems, vol. 2024(2), pp. 5–18, doi: https://doi.org/10.32620/reks.2024.2.01

Fedorovich, O., Lukhanin, M., Prokhorov, O., Slomchynskyi, O., Hubka, O. and Leshchenko, Y. (2023), “Simulation of arms distribution strategies by combat zones to create military parity of forces”, Radioelectronic and Computer Systems, vol. 2023(4), pp. 208–219, doi: https://doi.org/10.32620/reks.2023.4.15

Trunov, O., Skiter, I., Dorosh, M., Trunova, E. and Voitsekhovska, M. (2024), “Modeling of the Information Security Risk of a Transport and Logistics Center Based on Fuzzy Analytic Hierarchy Process”, in: Kazymyr, V., et al. Mathematical Modeling and Simulation of Systems. MODS 2023, Lecture Notes in Networks and Systems, vol. 1091. Springer, Cham, doi: https://doi.org/10.1007/978-3-031-67348-1_23

Sidchenko, S. O., Leshchenko, S. P., Tsyupka, P. R., Baturynskyi, M. P. and Sinchuk, A. V. (2024), “Methodology for forming information support for a mobile fire group in the air situational awareness system”, Systemy obrobky informatsii, vol. 2(177), pp. 85–93, doi: https://doi.org/10.30748/soi.2024.177.10

Kamak, M. D., Kazymyr, V. V. and Kamak, D. O. (2024), “Method of detection the quadcopters and octocopters based on YOLOv8 model”, Mathematical machines and systems, vol. 2, pp. 65–77, doi: https://doi.org/10.34121/1028-9763-2024-2-65-77

Bondarenko, O., Kravchenko, S., Tkachenko, M. and Tkachenko, K. (2023), “A methodical approach to prediction of the possible scale of air adversary actions on the basis of determining the size of the needed equipment of air attack means to defect the main objects of units and military units in battle”, Scientific Collection «InterConf+», vol. 32(151), pp. 731–740, doi: https://doi.org/10.51582/interconf.19-20.04.2023.077

Ostroverkhov, M., Silvestrov, А. and Kryvoboka, G. (2021), “The problem of identification in the theory of identification”, 2021 IEEE 2nd KhPI Week on Advanced Technology, doi: https://doi.org/10.1109/khpiweek53812.2021.9569971

Perekrest, A., Mamchur, D., Zavaleev, A., Vadurin, K., Malolitko, V. and Bakharev, V. (2023), “Web-Based Technology of Intellectual Analysis of Environmental Data of an Industrial Enterprise”, 2023 IEEE 5th International Conference on Modern Electrical and Energy System (MEES), IEEE, doi: https://doi.org/10.1109/mees61502.2023.10402523

Shkarlet, S., Dorosh, M., Druzhynin, O., Voitsekhovska, M., Bohdan, I. (2021), “Modeling of Information Security Management System in the Project”, in: Shkarlet, S., Morozov, A., Palagin, A. (eds) Mathematical Modeling and Simulation of Systems (MODS'2020), MODS 2020, Advances in Intelligent Systems and Computing, vol 1265. Springer, Cham, doi: https://doi.org/10.1007/978-3-030-58124-4_35

Han, Q., Pang, B., Li, S., Li, N., Guo, P.-s., Fan, C.-l. and Li, W. (2023), “Evaluation method and optimization strategies of resilience for air & space defense system of systems based on kill network theory and improved self-information quantity”, Defence Technology, doi: https://doi.org/10.1016/j.dt.2023.01.005

Chen, Q., Zhao, Q., Zou, Z., Qian, Q., Zhou, J. and Yuan, R. (2024), “A novel air combat target threat assessment method based on three-way decision and game theory under multi-criteria decision-making environment”, Expert Systems with Applications, 125322, doi: https://doi.org/10.1016/j.eswa.2024.125322

Ma, S., Zhang, H. and Yang, G. (2017), “Target threat level assessment based on cloud model under fuzzy and uncertain conditions in air combat simulation”, Aerospace Science and Technology, vol. 67, pp. 49–53, doi: https://doi.org/10.1016/j.ast.2017.03.033

Kuchuk, H. and Malokhvii, E. (2024), “Integration of IoT with Cloud, Fog, and Edge Computing: A Review”, Advanced Information Systems, vol. 8, no. 2, pp. 65–78, doi: https://doi.org/10.20998/2522-9052.2024.2.08

Hunko, M., Tkachov, V., Kuchuk, H. and Kovalenko, A. (2023), “Advantages of Fog Computing: A Comparative Analysis with Cloud Computing for Enhanced Edge Computing Capabilities”, 2023 IEEE 4th KhPI Week on Advanced Technology, KhPI Week 2023 – Conf. Proc., 02-06 October 2023, Code 194480, doi: https://doi.org/10.1109/KhPIWeek61412.2023.10312948

Petrovska, I., Kuchuk, H. and Mozhaiev, M. (2022), “Features of the distribution of computing resources in cloud systems”, 2022 IEEE 4th KhPI Week on Advanced Technology, KhPI Week 2022 - Conference Proceedings, 03-07 October 2022, Code 183771, doi: https://doi.org/10.1109/KhPIWeek57572.2022.9916459

Holub, S., Salapatov, V. and Nemchenko, V. (2024), “Representation of the program model using predicates”, Radioelectronic and Computer Systems, vol. 2024(1), pp. 6–16, doi: https://doi.org/10.32620/reks.2024.1.01

Eguchi, Y., Murakami, T., Hirakuchi, H., Sugimoto, S. and Hattori, Y. (2017), “An Evaluation Method for Tornado Missile Strike Probability with Stochastic Correlation”, Nuclear Engineering and Technology, vol. 49(2), pp. 395–403, doi: https://doi.org/10.1016/j.net.2016.12.007

Dando, B. D. E., Goertz-Allmann, B. P., Brissaud, Q., Köhler, A., Schweitzer, J., Kværna, T. and Liashchuk, A. (2023), “Identifying attacks in the Russia–Ukraine conflict using seismic array data”, Nature, doi: https://doi.org/10.1038/s41586-023-06416-7

Peng, Z., Zhang-song, S. and Cheng-fei, W. (2011), “A Trajectory Prediction Method of Ship-to-air Missiles for Dynamic Firepower Compatibility”, Procedia Engineering, vol. 15, pp. 321–325, doi: https://doi.org/10.1016/j.proeng.2011.08.062

(2024), Massive Missile Attacks on Ukraine, Kaggle: Your Machine Learning and Data Science Community, available at: https://www.kaggle.com/datasets/piterfm/massive-missile-attacks-on-ukraine

Zeng, X. and Liangqu, L. (2022), Beginning Deep Learning with TensorFlow 2: Work with Keras, MNIST Data Sets, and Advanced Neural Networks. Apress L. P., 713 p., available at: https://link.springer.com/book/10.1007/978-1-4842-7915-1

Shefer, O., Laktionov, O., Pents, V.., Hlushko, A. and Kuchuk, N. (2024), “Practical principles of integrating artificial intelligence into the technology of regional security predicting”, Advanced Information Systems, vol. 8, no. 1, pp. 86–93, doi: https://doi.org/10.20998/2522-9052.2024.1.11