PRACTICAL PRINCIPLES OF INTEGRATING ARTIFICIAL INTELLIGENCE INTO THE TECHNOLOGY OF REGIONAL SECURITY PREDICTING
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Abstract
Objective. The aim is to enhance the efficiency of diagnostics for determining the level of air attack safety through the practical integration principles of artificial intelligence. Methodology. Models and technologies for safety diagnostics of the region (territorial community) have been explored. The process of building an artificial intelligence model requires differentiation of objects at a level to accumulate assessments-characteristics of aerial vehicles. The practical integration principles of artificial intelligence into the forecasting technology are based on the Region Safety Index, used for constructing machine learning models. The optimal machine learning model of the proposed approach is selected from a list of several models. Results. A technology for predicting the level of regional safety based on the Safety Index has been developed. The recommended optimal model is the Random Forest model ([('max_depth', 13), ('max_features', 'sqrt'), ('min_samples_leaf', 1), ('min_samples_split', 2), ('n_estimators', 79)]), demonstrating the most effective quality indicators of MAE; MAX; RMSE 0.005; 0.083; 0.0139, respectively. Scientific Novelty. The proposed approach is based on a linear model of the Region Safety Index, which, unlike existing ones, takes into account the interaction of factors. This allows for advantages of the proposed method over existing approaches in terms of the root mean square error of 0.496; 0.625, respectively. In turn, this influences the quality of machine learning models. Practical Significance. The proposed solutions are valuable for diagnosing the level of safety in the region of Ukraine, particularly in the context of air attacks.
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References
(2018), About the national security of Ukraine, the law of Ukraine, Verkhovna Rada information, 21.06.2018, No. 2469-VII, available at: https://zakon.rada.gov.ua/laws/show/2469-19#Text
Wamba, S.F., Bawack, R.E. and Carillo, K.D.A. (2019), “The State of Artificial Intelligence Research in the Context of National Security: Bibliometric Analysis and Research Agenda”, Lecture Notes in Computer Science, 11701 LNCS, pp. 255–266, doi: https://doi.org/10.1007/978-3-030-29374-1_21
Francisco, M. (2023), “Artificial intelligence for environmental security: national, international, human and ecological perspectives”, Current Opinion in Environmental Sustainability, 61, doi: https://doi.org/10.1016/j.cosust.2022.101250
Montasari, R. (2023), “Internet of Things and Artificial Intelligence in National Security: Applications and Issues”, Advances in Information Security, vol. 101, pp. 27–56, doi: https://doi.org/10.1007/978-3-031-21920-7_3
Kuchuk, N., Kovalenko, A., Ruban, I., Shyshatskyi, A., Zakovorotnyi, O. and Sheviakov, I. (2023), “Traffic Modeling for the Industrial Internet of NanoThings”, 2023 IEEE 4th KhPI Week on Advanced Technology, KhPI Week 2023 – Conference Proceedings, 194480, doi: http://dx.doi.org/10.1109/KhPIWeek61412.2023.10312856
De Ruvo, G. (2022), “Data mining, artificial intelligence and national security: The geopolitical use of American legal infrastructure as an obstacle for a global data governance”, Rivista Italiana di Informatica e Diritto, 2022(1), pp. 113–124, doi: http://dx.doi.org/10.32091/RIID0056
Kovalenko, A. and Kuchuk, H. (2022), “Methods to Manage Data in Self-healing Systems”, Studies in Systems, Decision and Control, vol. 425, pp. 113–171, doi: https://doi.org/10.1007/978-3-030-96546-4_3
Montasari, R. (2022), “Artificial Intelligence and National Security”, Artificial Intelligence and National Security, 230 p, doi: https://doi.org/10.1007/978-3-031-06709-9
Kharazishvili, Y. and Kwilinski, A. (2022), “Methodology for determining the limit values of national security indicators using artificial intelligence methods”, Virtual Economics, vol. 5(4), pp. 7–26, doi:
https://doi.org/10.34021/ve.2022.05.04(1)
Dun B., Zakovorotnyi, O. and Kuchuk, N. (2023), “Generating currency exchange rate data based on Quant-Gan model”, Advanced Information Systems, Vol. 7, no. 2, pp. 68–74, doi: http://dx.doi.org/10.20998/2522-9052.2023.2.10
Al-Suqri, M.N. and Gillani, M. (2022), “A Comparative Analysis of Information and Artificial Intelligence Toward National Security”, IEEE Access, 10, pp. 64420–64434, doi: http://dx.doi.org/10.1109/ACCESS.2022.3183642
Dotsenko, N., Chumachenko, I., Galkin, A., Kuchuk, H. and Chumachenko, D. (2023), “Modeling the Transformation of Configuration Management Processes in a Multi-Project Environment”, Sustainability (Switzerland), Vol. 15(19), 14308, doi: https://doi.org/10.3390/su151914308
Sanclemente, G.L. (2022), “Reliability: understanding cognitive human bias in artificial intelligence for national security and intelligence analysis”, Security Journal, 35(4), pp. 87–91, doi: http://dx.doi.org/10.1057/s41284-021-00321-2
Datsenko, S., and Kuchuk, H. (2023), “Biometric authentication utilizing convolutional neural networks”, Advanced Information Systems, vol. 7, no. 2, pp. 67–73. Doi: https://doi.org/10.20998/2522-9052.2023.3.10
Park, W.Y., Kim, S.H., Vu, D.-S., Jung, H.S. and Jo, H. (2022), “A Security System for National Network”, Lecture Notes in Networks and Systems, 508 LNNS, pp. 789–803, doi: https://doi.org/10.1007/978-3-031-10467-1_48
Yaloveha, V., Podorozhniak, A. and Kuchuk, H. (2022), “Convolutional neural network hyperparameter optimization applied to land cover classification”, Radioelectronic and Computer Systems, No. 1(2022), pp. 115–128, doi: https://doi.org/10.32620/reks.2022.1.09
Amponsah, A.A., Adekoya, A.F. and Weyori, B.A. (2022), “Improving the Financial Security of National Health Insurance using Cloud-Based Blockchain Technology Application”, International Journal of Information Management Data Insights, vol. 2(1), 100081, doi: https://doi.org/10.1016/j.jjimei.2022.100081
Kuchuk, N., Mozhaiev, O., Semenov, S., Haichenko, A., Kuchuk, H., Tiulieniev, S., Mozhaiev, M., Davydov, V., Brusakova, O. and Gnusov, Y. (2023), “Devising a method for balancing the load on a territorially distributed foggy environment”, Eastern-European Journal of Enterprise Technologies, vol. 1(4 (121), pp. 48–55, doi: https://doi.org/10.15587/1729-4061.2023.274177
Petrovska, I. and Kuchuk, H. (2023), “Adaptive resource allocation method for data processing and security in cloud environment”, Advanced Information Systems, vol. 7(3), pp. 67–73, doi: https://doi.org/10.20998/2522-9052.2023.3.10
Hashimov, E.G. and Khudeynatov, E.K. (2023), “The effectiveness of air defense system”, Advanced trends in the development of information and communication technologies and management tools, 13 NTC, Baku, Kharkiv, Zhylina, vol. 1, pp. 17–18, doi: https://doi.org/10.32620/ICT.23.t1
Vavrin M., Zatonatska T. and Poltoratska A. (2023), “Clusterization of Ukraine regions according to the integrated index of ecological and economic security in the destructive phenomena conditions”, IOP Conference Series: Earth and Environmental Science, 1150, no. 012019, pp. 1–10, available at: https://iopscience.iop.org/article/10.1088/1755-1315/1150/1/012019/pdf
Onyshchenko, S., Yanko, A., Hlushko, A. and Sivitska, S. (2022), “Increasing Information Protection in the Information Security Management System of the Enterprise”, Lecture Notes in Civil Engineering, LNCE, vol 181, Springer, Cham., pp. 725–738, doi: https://doi.org/10.1007/978-3-030-85043-2_67
Onyshchenko, S., Shchurov, I., Cherviak, A. and Kivshyk, O. (2023), “Methodical approach to assessing financial and credit institutions’ economic security level”, Financial and credit activity problems of theory and practice, vol. 2, no. 49, pp. 65–78, doi: https://doi.org/10.55643/fcaptp.2.49.2023.4037
Ontalba, M.Á., Corbacho, J.Á., Baeza, A., Vasco, J., Caballero, J.M., Valencia, D. and Baeza, J.A. (2022), “Radiological Alert Network of Extremadura (RAREx) at 2021:30 years of development and current performance of on-real time monitoring”, Nuclear Engineering and Technology, doi: https://doi.org/10.1016/j.net.2021.08.007
Zhu, W., Rodrigues, J.J.P.C., Niu, J., Zhou, Q., Li, Y., Xu, M. and Huang, B. (2019), “Detecting air-gapped attacks using machine learning”, Cognitive Systems Research, vol. 57, pp. 92–100, doi: https://doi.org/10.1016/j.cogsys.2018.10.018
Van Dijcke, D., Wright, A. L. and Polyak, M. (2023), “Public response to government alerts saves lives during Russian invasion of Ukraine”, Proceedings of the National Academy of Sciences, vol. 120, no. 18, doi: https://doi.org/10.1073/pnas.2220160120
Michalski, D. and Adam, R. (2020), “Counting the Uncountable”, Safety & Defense, 2020, vol. 6, no. 2v pp. 100–112, doi: https://doi.org/10.37105/sd.91
Laktionov, A. (2021), “Improving the methods for determining the index of quality of subsystem element interaction”, Eastern-European Journal of Enterprise Technologies, vol. 6(3 (114), pp. 72–82, doi: https://doi.org/10.15587/1729-4061.2021.244929
Borowska-Stefańska, M., Goniewicz, K., Grama , V., Hornak , M., Masierek, E., Morar , C., Penzes, J., Rochovska, A. and Wiśniewski, S. (2023), “Evaluating Approaches to Wartime Mass Evacuation Management in Eastern NATO Territories: A Literature Review”, Safety & Defense, vol. 9(1), pp. 1–13, doi: https://doi.org/10.37105/sd.197
(2023), Massive Missile Attacks on Ukraine, available at: URL: https://www.kaggle.com/datasets/piterfm/massive-missile-attacks-on-ukraine
Campbell, A. (2020), Python Guide: Clear Introduction to Python Programming and Machine Learning, Independently Publ., 278 p., available at: https://www.amazon.com/Python-Guide-Introduction-Programming-Learning/dp/B0B7F4K4NK