MANAGEMENT OF ACCESS TO ELECTRONIC INFORMATION AND EDUCATIONAL ENVIRONMENT OF UNIVERSITIES OF FEDERAL EXECUTIVE AUTHORITIES

Igor Kotenko, I. Saenko, R. Zakharchenko, A.A. Kapustin, Mazen Al-Barri
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Abstract

The purpose of the article: analysis of the problem of ensuring timely authorized access to the resources of the electronic information and educational environment of universities of federal executive authorities and identification of possible directions for its solution. Research methods: system analysis of the problem of ensuring access of officials of universities of federal executive authorities to the resources of the electronic information and educational environment. The result obtained: approaches to improving the existing access control model, optimizing the role-based access scheme and determining unauthorized access attempts based on machine learning methods are proposed. Scope of the proposed approach: access control system of the electronic information and educational environment of universities of federal executive authorities. Scientific novelty: consists in a comprehensive analysis of the problem of creating and functioning of the electronic information and educational environment of universities of federal executive authorities, during which the structure of this environment is determined and its characteristic features are highlighted. Based on the analysis of information security threats in the electronic information and educational environment, the necessity of creating an access control system to its resources, which provides timely authorized access, is substantiated. The proposed approaches to improving the access control system affect not only the improvement of the existing access model by supplementing it with solutions available in the attribute-based access model, but also the optimization of the role- based access scheme using the developed genetic algorithm and the detection of unauthorized access attempts associated with overcoming access rules, based on application of machine learning methods. Experimental results are presented that confirm the effectiveness of the proposed approaches. Contribution: Igor Kotenko – analysis of the state of the art in the creation and application of the electronic information and educational environment of universities of federal executive authorities, setting the task and developing proposals for developing the functionality of the access control system, development of approaches to genetic optimization of the access scheme and detection of unauthorized access attempts using machine learning methods; Igor Saenko – development of approaches to improving the access control system related to the use of an attribute-based access model, genetic optimization of the access scheme and detection of unauthorized access attempts using machine learning methods; Roman Zakharchenko – analysis of technical solutions that ensure the implementation of the access control system to the resources of the electronic information and educational environment of universities of federal executive authorities, Alexander Kapustin – analysis of security threats and access control models to resources of the electronic information and educational environment of universities of federal executive authorities, Mazen Al-Barri – development and experimental study of an approach to detect attempts of unauthorized access to the resources of the electronic information and educational environment of universities of federal executive authorities, based on the use of machine learning methods
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联邦行政机关大学电子信息获取与教育环境管理
本文的目的是:分析联邦行政机关大学电子信息资源和教育环境的及时授权访问问题,并确定其解决的可能方向。研究方法:系统分析联邦行政机关大学官员获取电子信息资源和教育环境的保障问题。结果:提出了改进现有访问控制模型、优化基于角色的访问方案和基于机器学习方法确定未经授权访问尝试的方法。建议的方法范围:联邦行政机关大学电子信息和教育环境的访问控制系统。科学新颖性:包括对联邦行政机关大学电子信息和教育环境的创建和运作问题的全面分析,在此期间确定该环境的结构并突出其特征。在分析电子信息和教育环境中信息安全威胁的基础上,论证了建立对其资源的访问控制系统以提供及时授权访问的必要性。所提出的改进访问控制系统的方法不仅通过补充基于属性的访问模型中可用的解决方案来改进现有的访问模型,而且利用所开发的遗传算法来优化基于角色的访问方案,以及基于机器学习方法的应用,通过克服访问规则来检测未经授权的访问企图。实验结果证实了所提方法的有效性。贡献:Igor Kotenko -分析了联邦行政机关大学电子信息和教育环境的创建和应用的最新技术,为开发访问控制系统的功能设定任务并制定建议,开发访问方案的遗传优化方法,并使用机器学习方法检测未经授权的访问尝试;Igor Saenko -开发改进访问控制系统的方法,涉及使用基于属性的访问模型,访问方案的遗传优化以及使用机器学习方法检测未经授权的访问企图;罗曼·扎哈尔琴科(Roman Zakharchenko) -分析了确保联邦行政机关大学电子信息资源和教育环境访问控制系统实施的技术解决方案,亚历山大·卡普斯廷(Alexander Kapustin) -分析了联邦行政机关大学电子信息资源和教育环境的安全威胁和访问控制模型,Mazen Al-Barri -基于机器学习方法的使用,开发和实验研究了一种方法,用于检测未经授权访问联邦行政机关大学电子信息资源和教育环境的企图
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