Кафедра "Комп'ютерна інженерія та програмування"

Постійне посилання колекціїhttps://repository.kpi.kharkov.ua/handle/KhPI-Press/1095

Офіційний сайт кафедри https://web.kpi.kharkov.ua/cep

Від 26 листопада 2021 року кафедра має назву – "Комп’ютерна інженерія та програмування"; попередні назви – “Обчислювальна техніка та програмування”, “Електронні обчислювальні машини”, первісна назва – кафедра “Математичні та лічильно-вирішальні прилади та пристрої”.

Кафедра “Математичні та лічильно-вирішальні прилади та пристрої” заснована 1 вересня 1961 року. Організатором та її першим завідувачем був професор Віктор Георгійович Васильєв.

Кафедра входить до складу Навчально-наукового інституту комп'ютерних наук та інформаційних технологій Національного технічного університету "Харківський політехнічний інститут". Перший випуск – 24 інженери, підготовлених кафедрою, відбувся в 1964 році. З тих пір кафедрою підготовлено понад 4 тисячі фахівців, зокрема близько 500 для 50 країн світу.

У складі науково-педагогічного колективу кафедри працюють: 11 докторів технічних наук, 21 кандидат технічних наук, 1 – економічних, 1 – фізико-математичних, 1 – педагогічних, 1 доктор філософії; 9 співробітників мають звання професора, 14 – доцента, 2 – старшого наукового співробітника.

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Methodical guidelines for the implementation of the course project from the course "Compiler Design Theory"
(Національний технічний університет "Харківський політехнічний інститут", 2024) Gavrylenko, Svitlana; Chelak, Viktor
Researchers studying the appearance of intelligence on our planet believe that the appearance of language played a decisive role in its development, which allowed not only to express and store knowledge, but also to exchange it. With the creation of computers, there was a need to communicate with such devices, as it turned out to be necessary to give them orders, tasks, and a description of the work that they should perform. For this purpose, special languages began to be developed, which came to be called artificial, in contrast to the natural languages of human communication. Artificial languages should be, on the one hand, convenient and understandable for humans, and on the other hand, they should be perceived by devices. The combination of these requirements in one language turned out to be a difficult task, so there were tools for converting texts from a language understandable to a person to the language of a device. Such tools are called translators. The translator can be of the interpreting or compiling type. In the first case, it is called an interpreter of the input language, and in the second - a compiler. The interpreter sequentially reads the input language propositions, analyzes them and immediately executes them, while the compiler does not execute the language propositions, but builds a program that can later be run to obtain the result. The input of the compiler is a text written in an input language understood by a person, and the result of the compiler's work is a text in a language understood by the device. These methodological instructions consider the construction of a syntactic LR parser, which is one of the stages of the compiler. It is at the stage of syntactic analysis that the largest number of errors in the text of the program is revealed.
Method of Identifying the State of Computer System under the Condition of Fuzzy Source Data
(2020) Gavrylenko, Svitlana; Chelak, Viktor; Kazarinov, Michael
The purpose of this work is developing a method for identifying the abnormal state of a computer system based on the Bayes' Fuzzy classifier. It allowed us to create a Fuzzy expert identification system with an unlimited number of controlled indicators that belong to a finite interval. Estimation of informativeness of such indicators does not depend on the type of indicator’s functions and on the rule of their usage in the calculated formula. Introduced criterion allowed to estimate indices of the functioning of computer systems presented indistinctly. The quality of classification was evaluated based on ROC analysis. It was found that the method based on Bayes' Fuzzy expert system is qualitative, and its classification speed is almost independent of quantity indicators. Comparative evaluation of Bayes' Fuzzy classifier with Fuzzy clustering classifier and Fuzzy discriminant classifier are performed. In order to regulate the level of false-positive and false-negative classification, recommendations have been developed to manage the level of sensitivity and specificity of a Fuzzy expert system based on the Bayes classifier.
Development of Computer State Identification Method Based on Boosting Ensemble
(2021) Chelak, Viktor; Gavrylenko, Svitlana
This work is about developing a modification of boosting method by using a special preprocessing procedure to improve the accuracy of computer system state identification. The aim of the research is to develop method for detection computer threats, malware, etc. Experimental research 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. Prospects for further research may be to develop an ensemble of fuzzy decision trees based on the proposed method, optimizing their software implementation.
Method of computer system state identification based on boosting ensemble with special preprocessing procedure
(Національний технічний університет "Харківський політехнічний інститут", 2022) Chelak, Viktor; Gavrylenko, Svitlana
The 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.
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, Velizar
The 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 %.
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, Viktor
The 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.
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, Serhii
This 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.
Computer system anomalous state detection method based on fuzzy logic
(ФОП Петров В. В., 2019) Chelak, Viktor; Gavrylenko, Svitlana; Chelak, E.
Development of a heuristic scanner for an antivirus program on the basis of the mamdani fuzzy logic method
(Technical University of Sofia, 2018) Gavrylenko, Svitlana; Chelak, Viktor; Gornostal, Aleksey
The article considers the means of antivirus protection of information, their advantages and disadvantages. An analysis of modern decision-making systems is carried out. The system of fuzzy logic is chosen. A module based on the Mamdani fuzzy logic method was developed, and the developed system was tested. The obtained results of the research showed the possibility of using the developed module in heuristic analyzers of intrusion detection systems.
Assessment of the state of the computer system based on the hurst exponent
(Technical University of Sofia, 2017) Semenov, Sergei; Gavrylenko, Svitlana; Chelak, Viktor
The method of identifying abnormal behavior of computer systems based on the Hurst exponent is examined in this report. Results of the research suggest the possibility of using the Hurst exponent for identifying the anomalous behavior of computer systems in the overall system to detect malicious software.