MODELS FOR ENHANCED PROTECTION OF PERSONAL DATA OF USERS OF THE DISTANCE LEARNING SYSTEM OF THE ARMED FORCES OF UKRAINE

O. O. Shapran
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Abstract

The problem 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 substantiated. It has been proven that to ensure a high level of security of personal data of users of distance learning systems in modern conditions, progressive organizational, hardware and software solutions are actively used. An analysis of foreign and domestic experience in the development and implementation of personal data protection systems for users of the distance learning system is provided, and a conclusion is made about the possibility of significantly increasing their efficiency due to the development of mathematical and software. It is justified that the most relevant in this direction is the use of models and methods of artificial intelligence, namely, fuzzy logic and hybrid networks. Research materials are presented on the development of a methodology for improving the security of personal data of users of the distance learning system of the Armed Forces of Ukraine, 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 model for determining the state of the personal data protection system and the method of forecasting the state of the personal data protection system are described. The results of computer modeling are presented.
加强保护乌克兰武装部队远程教育系统用户个人数据的模式
改进现有和开发新模型和方法的问题,以提高基于人工智能的乌克兰武装部队远程学习系统用户个人数据的安全性。事实证明,在现代条件下,为了确保远程学习系统用户的个人数据的高度安全,积极使用先进的组织,硬件和软件解决方案。本文分析了国内外在远程教育系统用户个人数据保护系统的开发和实施方面的经验,并得出结论,认为由于数学和软件的发展,可以显著提高其效率。在这个方向上最相关的是使用人工智能的模型和方法,即模糊逻辑和混合网络。研究材料提出了改进乌克兰武装部队远程学习系统用户个人数据安全的方法,该方法提供了对威胁流动的有效响应,并基于模糊逻辑和混合网络的模型和方法的实施;介绍了个人资料保护制度状态的判定模型和个人资料保护制度状态的预测方法。给出了计算机模拟的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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