KEYWORDS: Internet of things, Risk assessment, Modeling, Computer programming, Failure analysis, Process modeling, Data modeling, Algorithm development, Visualization, Tunable filters
Currently, a very urgent problem is the development of approaches related to risk assessment in the Internet of Things systems. We consider a number of objects in the IoT system. For each of the objects, a certain set of risk factors can be designated. The approach that uses cluster analysis is considered. The criterion is used, that relate IoT objects with corresponding vulnerabilities. The illustration of the main steps of the algorithm for generating models for determining the risk associated with vulnerabilities in the Internet of Things is given. The results of analysis of the significance of risk factors and assessing the adequacy of the model for determining the degree of risk deterioration among objects of the IoT system are shown.
The paper is devoted to the investigation of the characteristics of the Internet of Things system, which is used in various organizations. It is proposed to apply leading indicators to form predictive models in the internet of things. The block diagram of the formation of a predictive model is considered. In the Internet of Things system, it is required to form an integral indicator. In the course of its calculation, it is proposed to use an expert approach. An assessment of the significance of individual indicators in the Internet of Things system has been carried out.
A large number of information systems are currently in use inside the operating systems. Various reforms are being implemented within organizations. This requires new approaches to the organization of management. The activities of organizations are based on a multichannel resource support system. The paper gives suggestions for building a structural model of a multichannel resource support system. It is shown how resources can be optimized within information systems. Results are presented that demonstrate the use of regression models. They allow you to describe the relationship of indicators that characterize the state of information systems in relation to the performance of the organization.
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