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Документ Паралельні та розподілені обчислення(Національний технічний університет "Харківський політехнічний інститут", 2022) Черних, Олена Петрівна; Челак, Віктор ВолодимировичДаний практикум містить 8 лабораторних робіт. У кожній роботі є теоретичні відомості, тексти програм з докладними коментарями та результат їх виконання, які необхідні для проведення лабораторних робіт. Призначений студентам спеціальностей 123 «Комп’ютерна інженерія».Документ Parallel and distributed calculations. Part 1(National Technical University "Kharkiv Polytechnic Institute", 2024) Chernykh, Olena Petrivna; Chelak, Viktor Volodymyrovych; Limarenko, Viacheslav VolodymyrovychThe laboratory course includes 8 laboratory works, among which the first one introduces students to the procedure of installing the MS-MPI environment on the machines of the computer room with the task of all the necessary configuration parameters. The works (from the second to the ninth inclusive) after some refinement of the texts of known parallel computational problems acquaint students with the methodology of parallel programs building, the computing environment configuring, interpretation and study of the results of the parallel program on a set of source data and laboratory performance report. Designed for students of computer science specialities.Документ Машинне навчання(2022) Гавриленко, Світлана Юріївна; Челак, Віктор Володимирович; Горносталь, Олексій Андрійович; Зозуля, Владислав ДенисовичЛабораторний практикум містить необхідний матеріал для виконання лабораторних робіт: варіанти завдань, стислий теоретичний матеріал, методичні вказівки та зразок виконання лабораторних робіт, приклади текстів програм та екранних форм, які демонструють результати роботи методів машинного навчання для класифікації, кластеризації та попередньої обробки даних. Призначено для студентів комп’ютерних спеціальностей вищих навчальних закладів.Документ Розробка методу побудови нечітких дерев рішень для задач ідентифікації стану комп'ютерної системи(ФОП Тарасенко В. П., 2022) Челак, Віктор Володимирович; Гавриленко, Світлана ЮріївнаДокумент Розробка системи генерації 3Д карт для комп'ютерних ігор використовуючи клітинні автомати(Тарасенко В. П., 2021) Юрчик, Д. О.; Челак, Віктор ВолодимировичДокумент Дослідження методів оцінки криптостійкості випадкових та псевдовипадкових послідовностей(Тарасенко В. П., 2021) Надірян, Г. О.; Челак, Віктор ВолодимировичДокумент Method of computer system state identification based on boosting ensemble with special preprocessing procedure(Національний технічний університет "Харківський політехнічний інститут", 2022) Chelak, Viktor; Gavrylenko, SvitlanaThe subject of the research is methods of identifying the state of the Computer System. The object of research is the process of identifying the state of a computer system for information protection. The aim of the research is to develop the method for identifying the state of a computer system for information protection. This article is devoted to the development of method (boosting ensemble) to increase the accuracy of detecting anomalies in computer systems. Methods used: artificial intelligence methods, machine learning, decision tree methods, ensemble methods. The results were obtained: a method of computer system identification based on boosting ensemble with special preprocessing procedure is developed. The effectiveness of using machine learning technology to identify the state of a computer system has been studied. Experimental researches have confirmed the effectiveness of the proposed method, which makes it possible to recommend it for practical use in order to improve the accuracy of identifying the state of the computer system. Conclusions. According to the results of the research, ensemble classifier of computer system state identification based on boosting was proposed. It was found that the use of the proposed classifier makes it possible to reduce the variance to 10%. In addition, due to the optimization of the initial data, the efficiency of identifying the state of the computer was increased. Prospects for further research may be to develop an ensemble of fuzzy decision trees based on the proposed method, optimizing their software implementation.Документ Development of a method for identifying the state of a computer systemusing fuzzy cluster analysis(Національний технічний університет "Харківський політехнічний інститут", 2020) Gavrylenko, Svitlana; Chelak, Viktor; Hornostal, Oleksii; Vassilev, VelizarThe subject of this article is the study of methods for identifying the state of computer systems. The purpose of the article is to develop a method for identifying the abnormal state of a computer system based on fuzzy cluster analysis. Objective: to analyze methods for identifying the state of computer systems; to conduct research on the selection of source data; to develop a method for identifying the state of a computer system with a small sample or fuzzy source data; to investigate and justify the procedure for comparing fuzzy distances between grouping centers and clustering objects; to develop a software and test. The methodsused in the paper: cluster analysis, fuzzy logic tools. The following resultswere obtained: a method was theoretically substantiated and investigated for identifying the state of a computer system with a small sample or fuzziness of the initial data, which is distinguished by the use of the method based on fuzzy cluster analysis by the refined grouping procedure. To solve the clustering problem, we used a special procedure for comparing fuzzy distances between grouping centers and clustering objects. Software was developed and testing of the developed method was performed. The quality of classification based on the ROC analysis is assessed. Conclusions. The scientific novelty of the results is as follows: a study was conducted on the selection of source data for analysis; a method for identifying the state of a computer system based on fuzzy cluster analysis using a special procedure for comparing fuzzy distances between grouping centers and clustering objects has been developed. This allowed to improve the classification quality to 22 %.Документ Processing information on the state of a computer system using probabilistic automata(Institute of Electrical and Electronics Engineers, 2017) Semyonov, S. G.; Gavrylenko, Svitlana; Chelak, ViktorThe paper deals with the processing of information about the state of a computer system using a probabilistic automaton. A model of an intelligent system for detection and classification of malicious software is proposed, which compares a set of features that are characteristic for different classes of viruses with multiple states of the machine. The analysis process is reduced to modeling the operation of the automaton taking into account the probability of transition from state to state, which at each step is recalculated depending on the reaction of the environment. The received results of research allow to reach a conclusion about the possibility of using the offered system for detection of the harmful software.Документ Development of anomalous computer behavior detection method based on probabilistic automaton(National University of Civil Protection of Ukraine, 2019) Chelak, Viktor; Chelak, E.; Gavrylenko, Svitlana; Semenov, SerhiiThis work proposes anomalous computer system behavior detection method based on probabilistic automaton. Main components of the method are automaton structure generation model and its modification procedure. The distinctive feature of the method is the adaptation of the automaton structure generation procedure for detecting attack scenarios of the same type, by restructuring the automaton upon a match and by recalculating the probability of state changes. Proposed method allows to speed up the detection of anomalous computer behavior, as well as to detect anomalies in computer systems, scenario profiles of which only partially match the instances used to generate automaton structure. The obtained results allow us to conclude that the developed meth-od can be used in heuristic analyzers of anomaly detection systems.