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Документ Devising an approach to the identification of system users by their behavior using machine learning methods(РС Теshnology Сentеr, 2022) Martovytskyi, Vitalii; Sievierinov, Оleksandr; Liashenko, Oleksii; Koltun, Yuri; Liashenko, Serhii; Kis, Viktor; Sukhoteplyi, Vladyslav; Nosyk, Andrii; Konov, Dmytro; Yevstrat, DmytroOne of the biggest reasons that lead to violations of the security of companies’ services is obtaining access by the intruder to the legitimate accounts of users in the system. It is almost impossible to fight this since the intruder is authorized as a legitimate user, which makes intrusion detection systems ineffective. Thus, the task to devise methods and means of protection (intrusion detection) that would make it possible to identify system users by their behavior becomes relevant. This will in no way protect against the theft of the data of the accounts of users of the system but will make it possible to counteract the intruders in cases where they use this account for further hacking of the system. The object of this study is the process of protecting system users in the case of theft of their authentication data. The subject is the process of identifying users of the system by their behavior in the system. This paper reports a functional model of the process of ensuring the identification of users by their behavior in the system, which makes it possible to build additional means of protecting system users in the case of theft of their authentication data. The identification model takes into consideration the statistical parameters of user behavior that were obtained during the session. In contrast to the existing approaches, the proposed model makes it possible to provide a comprehensive approach to the analysis of the behavior of users both during their work (in a real-time mode) and after the session is over (in a delayed mode). An experimental study on the proposed approach of identifying users by their behavior in the system showed that the built patterns of user behavior using machine learning methods demonstrated an assessment of the quality of identification exceeding 0.95.Документ Intelligent approaches to organizing remote quality control of storage of grain products(Національний технічний університет "Харківський політехнічний інститут", 2021) Diachenko, Vladyslav; Liashenko, Oleksii; Mikhal, Oleg; Umanets, MariiaCereals are an essential part of the diet of Homo sapiens. Since late Neolithic times, with the transition to sedentary farming, working with grain (growing, storing, processing, cooking food) has become a traditional type of professional human activity. As part of the accumulated historical experience, numerous technological processes have been developed and optimized for this type of activity. The relevant technologies evolved in close correlation with the changing conditions of life, literally under the pressure of Darwinian natural selection, because they were directly related to the survival of the Homo sapiens. Further development of grain-processing technologies remains invariably urgent today, as evidenced by the report [1] presented by the UN on the state of food security and nutrition in the world - with horrifying figures depicting the need and misery of the wide masses of the population of the planet. An important component of grain processing is the technology associated with the storage of grain products. Part of the stored grain products is used as seed stock for a new cycle of grain sales, the other - a significant part - for processing into food products. At the same time, new developed (optimized, improved) grain storage technologies must be safe, low-cost, maximally compatible with previously developed (available) equipment, and scalable to large volumes of stored material. Of course, the technology must ensure proper efficiency, an indicator of which should be a reduction in the percentage of grain product losses. In this regard, management methods used in the technological processes of grain products storage are substantially important, as well as methods of control over the current state of grain products for the correct organization of the technological processes. In particular, methods using elements of artificial intelligence are of high interest. Among them, neural networks are promising, especially those capable of learning "without a teacher" - Kohonen Maps (KK). Modified KK algorithm [2] implements reduced learning time[3], which is relevant in the implementation of adaptive procedures for processing the results of measurements of controlled parameters. The purpose of this paper is to consider the principles of using modified Kohonen maps to classify situations with applicability to remote quality control of grain products storage.