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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2019 - 201912020 - 20211

Налаштування

Автор: search.filters.author.Bulba, S. S.ORCID: search.filters.orcid.https://orcid.org/0000-0003-0358-7516

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  • Ескіз
    Документ
    Transaction Planning Methods in Hyperconverged Architecture Systems
    (Ceur-ws, 2019) Bulba, S. S.; Kuchuk, N. G.; Semenova, A.; Zhengbing, Hu
    The analysis of the features of the functioning of systems with hyperconverged architecture is carried out. Transaction efficiency in such systems is reduced compared to decentralized systems. The purpose of the research: to develop a method for planning transactions in systems with a hypeconvergent architecture, which will take into account the specifics of the functioning of such systems. The development of the method takes into account the centralized management of the transaction package and the distribution of various resources. Existing methods for determining the sequence of transactions in systems with hyperconverged architecture are considered. The methods that were considered were based on the greedy, clustering, and ant algorithms. For each method, its features and functioning scheme are determined. Analysis of existing methods showed the advantages of the greedy algorithm with a small system load. It is also proved that with the growth of information volumes and the number of simultaneously executed transactions, each of the methods considered is less effective than with decentralized management. Therefore, a method for planning the execution of transactions through the sharing of these optimization algorithms is proposed. This allowed to reduce the execution time of the optimal transaction plan in comparison with existing methods. The experiments showed that the effectiveness of the proposed method increases with increasing amount of information. Which is processed by a transaction package of a hyperconverged system.
  • Ескіз
    Документ
    Automated penetration testing method using deep machine learning technology
    (Національний технічний університет "Харківський політехнічний інститут", 2021) Semenov, S. S.; Weilin, Cao; Liqiang, Zhang; Bulba, S. S.
    The article developed a method for automated penetration testing using deep machine learning technology. The main purpose of the development is to improve the security of computer systems. To achieve this goal, the analysis of existing penetration testing methods was carried out and their main disadvantages were identified. They are mainly related to the subjectivity of assessments in the case of manual testing. In cases of automated testing, most authors confirm the fact that there is no unified effective solution for the procedures used. This contradiction is resolved using intelligent methods of analysis. It is proposed that the developed method be based on deep reinforcement learning technology. To achieve the main goal, a study was carried out of the Shadov system's ability to collect factual data for designing attack trees, as well as the Mulval platform for generating attack trees. A method for forming a matrix of cyber intrusions using the Mulval tool has been developed. The Deep Q - Lerning Network method has been improved for analyzing the cyber intrusion matrix and finding the optimal attack trajectory. In the study, according to the deep reinforcement learning method, the reward scores assigned to each node, according to the CVSS rating, were used. This made it possible to shrink the attack trees and identify an attack with a greater likelihood of occurring. A comparative study of the automated penetration testing method was carried out. The practical possibility of using the developed method to improve the security of a computer system has been revealed.