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Документ Mathematical model of the information system synthesis process(ФОП Петров В. В., 2021) Kuchuk, N. G.; Shyman, A.; Davydov, Viacheslav; Hrebeniuk, DarynaДокумент Research of the software security model and requirements(Національний технічний університет "Харківський політехнічний інститут", 2021) Semenov, Serhii; Davydov, Viacheslav; Hrebeniuk, DarynaThe subject of research in the article is a software security model. The aim of the work is to research the quality characteristics of the software and requirements for the software security in order to improve their safety. The article solves the following tasks: researching the shortcomings of the existing security model in order to identifyits main shortcomings; study of the quality characteristics of software that affect its security in order to identify the possibility of improving the quality of software. The following results were obtained: on the basis of the analysis of the existing model of software security, the main features of the attributes of this model were identified, their advantages and disadvantages were given. On the basis of the conducted analytical study, the necessity of improving the existing model of ensuring the security of software has been proved. Existing requirements for software and characteristics that affect its quality are considered. The characteristics of software security are highlighted, the indicators of which should be improved. Conclusions: a software security model has been studied. The need to develop this model is shown by introducing the possibility of adapting the existing requirements for the security of software tools throughout the entire life cycle of software development; the study of the quality characteristics of software showed that to ensure its security, it is necessary to improve the following characteristics: integrity, authentication, confidentiality, access control. However, it was shown that an increase in these characteristics can lead to a deterioration in other indicators of software quality: portability, maintainability, performance.Документ Development the resources load variation forecasting method within cloud computing systems(Національний технічний університет "Харківський політехнічний інститут", 2020) Davydov, Viacheslav; Hrebeniuk, DarynaThe subject of research in the article is the models and methods of resources load forecasting in cloud computing systems using the mathematical apparatus of neural networks. The aim of the work is increasing the efficiency of computing systems resources usage (such as RAM, disk space, CPU, network) by developing methods of resources load forecasting. The article addresses the following tasks: development of an integrated approach to the problems of resources load forecasting within cloud computing systems, which includes thesyn thesis of a combining forecasting neural network; development of a forecasting neural network model based on Elmanneural network; development of a method for training a neural network based on an artificial immunity algorithm; evaluation of the effectiveness of the developed method. To solve the set tasks, the approaches and methods of artificial neural and immune systems were used, as well as methods of theoretical research, which are based on the scientific provisions of the theory of artificial inte lligence, statistic, functional and systemic analyzes. The following resultswere obtained: on the basis of the analysis of resources load forecasting methods in cloud computing systems, the main results of the methods were revealed, the advantages and disadvantages were demonstrated. On the basis of the research resultsanalysis, the necessity of improving analytical methods for forecasting the load has been proved. The method of computing resources load forecasting in cloud computing systems has been improved, which makes it possible to obtain more accurate assessment results and prevent overloads in cloud computing systems. The results obtained are confirmed by the experiments carried out using the means of the infrastructure of private infrastructure services. Conclusions: improved the resources load forecasting method based on the mathematical apparatus of artificial neural networks to improve the efficiency of their usage.