Shapran O. Methods of increasing the security of personal data of users of the distance learning system of the Armed Forces of Ukraine.

When solving the problem of cyber security of the distance learning system of the Armed Forces of Ukraine, the scientific task of improving existing and developing new models and methods of increasing the security of personal data of users of the distance learning system of the Armed Forces of Ukraine based on artificial intelligence is relevant. This dissertation is dedicated to solving this problem. The scientific novelty of the obtained results is that: for the first time, a method of increasing the security of personal data of users of the distance learning system of the Armed Forces of Ukraine was proposed, which provides an effective response to the flow of threats and is based on the implementation of models and methods of fuzzy logic and hybrid networks. The methodology combines models and methods aimed at comprehensively solving the following tasks: 1) identification of the threat and determination of the state of the personal data protection system, namely, whether the system provides full protection or does not provide protection and requires the use of methods and means that were not previously used; 2) formation of an appropriate response to the threat from the arsenal of existing methods, methods, means and procedures or, in the case of their low efficiency, formation of a strategy for improvement and development of new ones; 3) forecasting the state of the protection system in the future in order to prevent the harmful effects of threats. The practical implementation of the methodology allows for permanent protection of personal data of users of the distance learning system of the Armed Forces of Ukraine; the model of the personal data protection system has been improved, which, unlike the existing ones, is based on fuzzy logical deduction (Mamdani algorithm). The model provides an optimal defuzzification method, rational weight and number of rules, optimal parameters of membership functions. The application of the model allows obtaining a clear quantitative assessment of the system’s effectiveness, which is necessary for making a decision for further improvement; for the first time, a method of predicting the state of the personal data protection system with the aim of preventing the harmful influence of threats was proposed, which is built on the basis of the technology of the adaptive neural fuzzy network system of ANFIS (Adaptive Neuro-Fuzzy Inference System). Using the method allows you to predict the effectiveness of the protection system and plan measures to ensure the improvement of the security of personal data. According to the test results, the average forecast error is 0,006 ÷ 0,26 %. The practical significance of the obtained results is that their implementation is expedient in mathematical and software as a component of the personal data protection management system. The implementation of the results of the dissertation research made it possible to increase the effectiveness of personal data protection in the distance learning system of the Armed Forces of Ukraine, namely, security during crisis periods improved by 26-28%, and response time to data security threats - by 28-30%. The results of dissertation research are implemented in the educational process of the Distance Learning Scientific Center of the National Defense University of Ukraine during the organization and conduct of the training course for scientific and pedagogical workers, acts of implementation are No. 609/10/268 dated 26.09.2023 and No. 609/10/269 dated 26.09.2023. The new scientifically based practical recommendations for increasing the security of personal data of users of the distance learning system of the Armed Forces of Ukraine allow us to consider the possibility of using these approaches both in other distance learning systems and in various computer systems. Scientific papers, in which the main scientific results of the dissertation are published: 1. Shapran O.O., Kravchenko Yu.V., Tyshchenko M.G., Makhno E.P. A model of intellectualization of planning time for the completion of an educational task in the distance learning system. Modern information technologies in the field of security and defense. No. 1 (40), 2021, pp. 143-152. 2. Shapran O., Gawliczek, P., Krykun, V., Tarasenko, N., Tyshchenko, M. Computer Adaptive language testing according to NATO Stanag 6001 requirements. Advanced Education, Vol. 8(17), 2021, pp. 19-26. (Web of Science) 3. Shapran O. Methodology for increasing the security of personal data of users of the distance learning system of the Armed Forces of Ukraine. Modern information technologies in the field of security and defense. No. 2 (45), 2023, pp. 33-45.