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Evidence of the complexity of the cybersecurity problem is the rapid increase in the number of information security breaches and losses on cybersecurity threats combined with an increase in the average loss from each of the breaches. Therefore, it is necessary to create requirements for a cybersecurity system that could provide more opportunities in the choice of methods in the management of the protection of automated information systems. The task of determining the optimal quality indicators of information resource management systems of automated systems is one of the most important problems in designing integrated information security systems. This is due to the complexity of such systems, the presence of many variable parameters, and the complexity of calculating quality indicators. In addition, the determined quality indicators should not only ensure the optimality of the target function, but also the stability of the protection system in a wide range of external adverse effects. The problem is that the existing methods of calculating integrated quadratic estimates (IQE) do not take into account errors in determining quality indicators, as well as the vector nature of these indicators. The aim of this work is to solve problems (development of algorithms), which are a problem of optimization of stable protection management systems using vector objective functions. Based on the model of information management system protection of information resources in the form of an automatic control system, the method of forming integrated quadratic estimates (IQE) of control error is proposed. This method takes into account the weights of the estimates at the desired installation time and standard transfer functions. Algorithms for calculating IQE according to the modified Katz formula and Ostrom's method for arbitrary order control systems are developed, including vector representation of the objective function of the protection system. The vector penalty function is proposed and the algorithm of its calculation is developed to display the degree of infringement of conditions of stability of parameters of the system of protection by the Rauss-Hurwitz criterion.

An urgent task at this moment is the use of blockchain technology to ensure the security of medical decision support systems (MDSS). Our research is devoted to development of blockchain-based MDSS (regarding possibility or impossibility of organ and tissue donation/transplantation, regarding possibility or impossibility of using reproductive technologies in the treatment of infertility). The developed blockchain-based medical decision support system provides reliable protection and security of medical information through the use of blockchain technology, provides support of decision regarding possibility or impossibility of organ and tissue donation/transplantation, provides support of decision regarding possibility or impossibility of use of reproductive technologies in the infertility treatment. The proposed blockchain-based medical decision support system: automates medical decision-making processes, minimizes the human factor and its influence on the medical decision process, and takes into account the norms of current legislation when making medical decisions, thereby allowing not to pay for the services of a hired lawyer, and also works with verified and protected medical data entered in the blockchain, which allows you to get rid of leaks of medical information and to ensure reliable protection of medical data.

The possibility of having a child is an important indicator of the reproductive health of the population. 10-15% of Ukrainian families are infertile. Most of them resort to the use of assisted reproductive technologies, which are currently developing rapidly in Ukraine. Today in Ukraine there is a need to automate decision-making processes on the possibility of providing extracorporeal fertilization, which can significantly increase the correctness of the contract in terms of civil and medical law. A conducted review and analysis of known decision support systems for the medical field showed the unsuitability of known systems to support decision-making on the possibility of providing extracorporeal fertilization. Therefore, the purpose of this study is to support decision-making on the possibility of extracorporeal fertilization. This paper develops the concept of the decision-making process on the possibility of providing extracorporeal fertilization. The result is a conclusion on the correctness or incorrectness of the content of the contract for extracorporeal fertilization, as well as a recommendation on the conclusion of the contract (in case of the correctness of its content) or revision of the contract (in case of incorrect content). In case of incorrect content of the contract, a hint is given regarding the missing essential conditions and it is proposed to supplement the contract with the missing essential conditions. Promising areas of work of future authors are the formation of an ideal ontology, which contains all the necessary essential conditions for concluding a contract, as well as the implementation of an intelligent agent to support decision-making on extracorporeal fertilization based on civil law.

This article was analyzed the threats to computer data in computer systems and classified these threats by their attributes. The relationships between threat classes, attributes, and related security methods and tools have defined the mathematical model of the computer threat classification problem. Based on the model analysis was concluded that the computer threats classification problem belongs to difficult formalized problems and requires for its solution methods of intellectual analysis, one of which is the subsystem of logical inference implemented in this article.

The paper proposes a neural-network model of software quality prediction based on quality attributes. The proposedmodel differs from the known models, because it provides considering the importance of each quality attribute and their interactionwithin each software quality characteristic. The artificial neural network (ANN) outputs correspond to the values of software qualitycharacteristics (functional suitability, performance efficiency, usability, reliability, compatibility, security, maintainabi lity, portability).The artificial neural network (ANN) outputs make it possible assessing the total impact of quality attributes on software qualitycharacteristics

The growing demand for IoT devices is accelerating the pace of their production. In an effort to accelerate the launch of a new device and reduce its cost, manufacturers often neglect to comply with cybersecurity requirements for these devices. The lack of security updates and transparency regarding the security status of IoT devices, as well as unsafe deployment on the Internet, makes IoT devices the target of cybercrime attacks. Quarterly reports from cybersecurity companies show a low level of security of the Internet of Things infrastructure. Considering the widespread use of IoT devices not only in the private sector but also in objects for various purposes, including critical infrastructure objects, the security of these devices and the IoT infrastructure becomes more important. Nowadays, there are many different methods of detecting cyberattacks on the Internet of Things infrastructure. Advantages of applying the machine-based methods in comparison with signature analysis are the higher detection accuracy and fewer false positive, the possibility of detecting both anomalies and new features of attacks. However, these methods also have certain disadvantages. Among them there is the need for additional hardware resources and lower data processing speeds. The paper presents an overview of modern methods aimed at detecting cyberattacks and anomalies in the Internet of Things using machine learning methods. The main disadvantages of the known methods are the inability to detect and adaptively respond to zero-day attacks and multi-vector attacks. The latter shortcoming is the most critical, as evidenced by the constantly increasing number of cyber attacks on the Internet of Things infrastructure. A common limitation for most known approaches is the need for significant computing resources and the significant response time of cyberattack detection systems.