A.V. Skatkov, A.A. Bryukhovetskiy
Federal State Educational Institution of Higher Education «Sevastopol State University», RF, Sevastopol, Universitetskaya St., 33
E-mail: a.alexir@mail.ru
DOI: 10.33075/2220-5861-2022-3-84-96
UDC 004.56
Abstract:
The article presents an approach and research results to the construction of a strategy for detecting vulnerabilities of interfaces of information and measurement networks based on decentralized service disciplines on the basis of mutual testing of the states of natural and technical objects and systems. The proposed approach develops methods for dynamic detection of anomalies in information data flows in intelligent networks, taking into account the features of 5G technologies. The application of the proposed approach is aimed at increasing the reliability of decisions made in a stochastic highly dynamic environment characterized by a rapidly changing network topology, its mobility, spatial density, localization of nodes.
Primary sensors for measuring environmental parameters fail during operation due to exposure to an aggressive environment and deliberate actions. As a result, there are systematic measurement errors of controlled quantities due to the appearance of vulnerabilities. The presented approach, in contrast to the known ones, allows detecting vulnerabilities of device interfaces when testing their properties together. For this purpose, a model has been developed that forms virtual collaborations of network nodes and allows moving from processing the variable structure of the network topology to a quasi-permanent one. One of the variants of the network architecture, a graph model of virtual collaborations, and an algorithm for the formation of minimal coverages are presented, the characteristic ratio, the value of which confirms the effectiveness of the developed approach based on collaborations compared with traditional single testing is analyzed.
Keywords: decentralized processing, node collaborations, graph model, set coverage, decision-making, vulnerability detection.
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