Факультет комп’ютерних та інформаційних технологій

Постійне посилання на фондhttps://repository.lntu.edu.ua/handle/123456789/49

Переглянути

Фільтри

2019 - 201912020 - 20246

Налаштування

Автор: search.filters.author.Bortnyk, Kateryna

Результати пошуку

Зараз показуємо 1 - 7 з 7
  • Ескіз
    Item type:Наукова стаття,
    Wireless access point with multilayer data protection algorithm
    (2021) Kostiuchko, Serhii; Kyryliuk, Liudmyla; Chernyashchuk, Natalia; Bortnyk, Kateryna; Hrunjuk, Serhii
    The article proposes a multi-level algorithm for protecting information transmission by means of a wireless router and Raspberry Pi. The proposed algorithm is implemented on the basis of separate encryption tools operating in parallel with secure communication channels. By directing traffic to anonymous and protected servers (Tor) and by encrypting information and using a DNS proxy in parallel, the information is available only to legitimate users
  • Ескіз
    Item type:Наукова стаття,
    Imitation of CNS-Control of Human Lower Limb: Joints Simulation
    (2019-10) Bortnyk, Kateryna; Lavrenchuk, Svitlana; Lishchyna, Nataliia
    In this paper, the imitation of central nervous system control of human lower limb is done and the controller simulation for ankle joints is presented. The CNS control is applied to multi-joints coordination relating to the problem of human postural balance. This control strategy was developed with aid of state-space model of the lower extremity of human in order to reconstruct natural human motion and to stabilize the human posture. Based on real model experiments, which simulates the human motion process in real time, we present the methodology of actual target values obtaining which correspond to position of human upright standing.
  • Ескіз
    Item type:Наукова стаття,
    Effective Content Moderation Using Modern AI Tools
    (2024) Bortnyk, Kateryna; Bahniuk, Nataliia; Kondius, Inna; Melnyk, Kateryna; Melnychuk, Yuliia; Kondius, Kostiantyn
    In the paper we develop a web application that integrates AI capabilities for content moderation, while providing an effective and balanced user protection system based on general comparative analysis of two leading tools for content moderation: Microsoft Content Moderator and Google Perspective API. Moderation mechanisms using AI algorithms have been developed, allowing not only to filter unwanted content, but also to adapt to changing conditions and user needs. We present the comparison results of two language models of Google Perspective API and Microsoft Content Moderator and appropriate tools taking into account the specific helpful, honest, harmless criteriums that is aligned with human values and include emotional tonality supported by developers.
  • Ескіз
    Item type:Наукова стаття,
    The system of dynamic optimization pricing by machine learning
    (Greece, 2024) Konotopchyk, Artem; Melnyk, Kateryna; Lavrenchuk, Svitlana; Khrystynets, Nataliia; Melnyk, Pavlo; Melnyk, Pavlo; Bortnyk, Kateryna
    Based on the real retail data collected over eight months, a number of models are developed to determine the most efficient machine learning algorithm. It is found that the KNeighbors Regressor model demonstrates the best performance for a small number of transactions, achieving a low MSE of 0.00091 and a high R2 of 0.72 in the validation set. For a large number of transactions, Random Forest Regressor and Decision Tree Regressor show the best ability to capture complex relationships and handle nonlinearties in the data, thanks to the ensemble learning technique. Whereas the Linear Regressor and Support Vector Regressor models demonstrated large deviations from the real price on the test data. The results of the study demonstrate the relevance of rtificial intelligence algorithms in dynamic pricing trategies, showing the ability to quickly process huge amounts of data, taking into account numerous factors and changes in market demand.
  • Ескіз
    Item type:Наукова стаття,
    Socket performance influence on data processing intensity in a virtual machine cluster with heterogeneous conditions
    (Lutsk: LNTU, 2024-06-21) Melnyk, Vasyl; Bahniuk, Nataliia; Roiko, Oleksandr; Bortnyk, Kateryna; Kizym, Svitlana
    The paper presents the dependences of data processing in heterogeneous conditions taking into account the impact of socket performance. As high-performance, sockets are proposed on the virtual interface architecture to use a component basis in order to support the application performance for intensive data processing. High-performance applications, using the traditional Linux-based TCP/IP interface, require guarantees of performance and its scalability to adapt for heterogeneous networks. In case of using highly effective protocols for their operation, some additional methods should be used at the user level, including for sockets on the virtual interface architecture. The limitation on the highperformance socket layers is tyed with the application developing completion to full performance salvation during the TCP/IP data transfer process. There also revealed that small-block load balancing has been found to increase heterogeneous network adaptation.
  • Ескіз
    Item type:Наукова стаття,
    Threats detection and analysis based on SYSMON tool
    (2023) Bahniuk, Nataliia; Linchuk, Oleksandr; Bortnyk, Kateryna; Kondius, Inna; Melnyk, Kateryna; Kondiu, Kostiantyn
    In this work, an nalysis for the study of threats in a real environment with the possibility of conducting a fullfledged analysis of threats, as well as their simulationhas been developed for research purposes. Designed laboratory was built for the threats research, specification of deploying and configuring Sysmon, imitation of an attack in laboratory conditions and its investigation by implicit signs, the processesing of threat analysis using the Sysmon tool. We present a system based on the analysis of continuous input chan-nels of Sysmon logs. The system is based on the Cyber Threat Analysis Ontology and analyzes SYSMON logs to classify software according to different threat levels and enhance cyber defense capabilities with situational awareness, prediction and auto-mated actions. The developed laboratory improves the effectiveness of threat analysis using the Sysmon tool, makes study of threats, deploying and configuring Sysmon, imitation of an attack in laboratory conditions and its investigation by implicit signs. It can be applied for the study of threats in a real environment with the possibility of conducting a full-fledged analysis of threats, as well as their simulation for research purposes.
  • Ескіз
    Item type:Наукова стаття,
    Development of network traffic monitoring system elements using Deep Learning
    (Piscataway, NJ: IEEE, 2024) Melnyk, Vasyl; Bahniuk, Nataliia; Bortnyk, Kateryna; Kondius, Inna; Zubovetska, Nataliia; Kondius, Kostiantyn
    In this article we develop a network traffic monitoring system elements using deep learning that demonstrates the high effectiveness of deep learning in detecting and analyzing network traffic, which contributes to ensuring the security and stability of corporate networks. In this context we develop algorithms for detecting and countering potential threats in network traffic, providing deeper analysis and effective response to cyber threats