MODEL OF VULNERABILITY IDENTIFICATION IN UNSTABLE NETWORK INTERACTIONS WITH AUTOMATED SYSTEM

The article gives a description of the vulnerabilities detection model in case of unstable network interactions with the  automated system (AS). The process of detection of AS vulnerabilities in these conditions has the following drawbacks: a  narrow scope of application of the existing models, low ...

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Bibliographic Details
Main Authors: V. A. Minaev, I. D. Korolev, A. V. Mazin, S. A. Konovalenko
Format: Article
Language:English
Published: CRI «Electronics» 2018-06-01
Series:Радиопромышленность
Subjects:
Online Access:https://www.radioprom.org/jour/article/view/305
Description
Summary:The article gives a description of the vulnerabilities detection model in case of unstable network interactions with the  automated system (AS). The process of detection of AS vulnerabilities in these conditions has the following drawbacks: a  narrow scope of application of the existing models, low responsiveness level and lack of completeness of the AS actual  condition, low reliability of control results. On the basis of the automata theory a structural model has been constructed  that ensures detection of AS vulnerabilities in the reviewed conditions. The process of the model’s operation in conditions  of unstable network interactions with the AS is described. A flow chart of the algorithm that provides a possibility for  practical implementation of the proposed model is constructed. The vulnerability detection model, which is presented in  the article makes it possible to ensure promptness, completeness and reliability of monitoring the real condition of ASs  functioning in unstable network interactions; to expand the scope of application of the existing models and methods for identifying vulnerabilities for dynamic AS featured by a relatively short time interval of operation; to automate the process  of restoring control over AS interrupted by external factors; adaptively manage the decision-making time; to provide the  iterative approach to identifying the AS vulnerabilities.
ISSN:2413-9599
2541-870X