MANAGEMENT OF ACCESS TO ELECTRONIC INFORMATION AND EDUCATIONAL ENVIRONMENT OF UNIVERSITIES OF FEDERAL EXECUTIVE AUTHORITIES
Authors:   Laboratory of Computer Security Problems at St. Petersburg Federal Research Center of the Russian Academy of Sciences (SPC RAS), Kotenko Igor , Saenko Igor ,   Laboratory of Computer Security Problems at St. Petersburg Federal Research Center of the Russian Academy of Sciences (SPC RAS), Zakharchenko Roman ,   Krasnodar Higher Military School named after General of the Army S.M. Shtemenko, Kapustin Alexander ,   Krasnodar Higher Military School named after General of the Army S.M. Shtemenko, Al‑Barri Mazen ,   Adjunct at Military Academy of Communications named after Marshal of the Soviet Union S.M. Budyonny
Published: April 2023

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