Scientific Notes of the State University of Information and Communication Technology

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admin admin — 2025-06-21

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FUNCTIONALSTATISTICAL MODELS OF CONTROL AND MANAGEMENT OBJECTS

Галаган Н. В. (Halahan N.V.) — 2025-06-21

In a modern infocommunication network, the object
of control and management can be any information network equipment, as well as the entire network as a
whole. In this case, the network is considered as a complex system that is subject to management. The
complexity of the control and management process is largely determined by the complexity of the objects. To
describe the functioning of the object, it would be advisable to build its mathematical model. The state of an
object is most fully characterized by a mathematical functional-statistical model – a system of equations or
operators that describe the dependence of the initial parameters of an object, system or unit on external or
internal influences during operation. Based on the analysis of this model, it is possible to formulate the main
tasks solved by the automatic control and management system, as well as synthesize the optimal network
management system, determine the degree of automation and its effectiveness.
When constructing a mathematical functional-statistical model, it is necessary to take into account that
a network as a control object can consist of systems of various classes and types. Such systems can be
autonomous and non-autonomous, stationary and non-stationary, closed and open. Therefore, to construct a
mathematical functional-statistical model, it is necessary to use a sufficiently generalized mathematical
apparatus, which, with appropriate changes, can be extended to individual cases.
Keywords: network management, control and management system, functional-statistical model, control
and management object, synthesis, optimal system, delay, availability factor, Monte Carlo method

References
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CYBERSECURITY METHODS FOR THE INTERNET OF THINGS NETWORK IN THE FIELD OF LABORATORY RESEARCH DATA TRANSMISSION

Іванченко Є. В. (Ivanchenko Ye.V.) — 2025-06-21

The paper analyzed methods of protecting sensor networks of the Internet of Things in the
field of laboratory tests. The role of methods of protecting sensor networks in the context of existing threats
to the sensor network was studied, taking into account different types of attacks with the probable use of
different types of sensor connections and actuators. The considered network-level protection methods were
grouped, which can be used to solve laboratory test tasks by using sensor networks based on IoT technology,
into five categories: network segmentation, intrusion identification, secure routing, protection of actuators,
protection of the MQTT protocol. A system of methods for protecting IoT networks in the field of
laboratory testing has been formed, which takes into account both general methods for protecting wireless
networks and a group of special methods for protecting IoT sensor networks, as well as the influence of
methods for protecting the perception, support and application levels and the network level in the general
security model. The interrelationships of individual methods within the framework of interaction with
different groups to solve the problem of protecting wireless sensor IoT networks when used in laboratory
tests have been studied. A number of advantages and disadvantages that the groups of considered methods
provide for laboratory test tasks have been formed and the path for further development of these methods
has been outlined in order to increase the level of efficiency and quality of laboratory tests and the accuracy
of laboratory measurements. The practical significance of the work lies in the possibility of using a system
of interconnected methods to achieve the maximum level of protection when using sensor networks in the
laboratory for the further implementation of existing methods in real conditions and the development of
new methods to increase the effectiveness of protection.
Keywords: sensor network, IoT, laboratory testing, automated laboratories, network layer security,
wireless sensor networks

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5G NETWORK USER EQUIPMENT POSITIONING WITH THE USE OF TIMING ADVANCE

Горохов О. С. (Horokhov O.S.) — 2025-06-21

This article is devoted to the study of positioning methods for user equipment
for navigation, industry and public safety. In scenarios where the use of GPS is complicated by different
factors, positioning can be provided using the standard functionality of the cellular network. This paper
discusses positioning methods in 5G networks, in particular, those based on E-CID, field strength,
difference in time of arrival, and LPHAP, and proposes a method based on timing advance. The proposed
method is aimed at simplifying the positioning procedure, ensuring high accuracy of determining the
location of subscriber equipment and reducing power consumption during positioning. The method of
reducing the error by averaging a number of TA values is investigated. Methods for reducing UE power
consumption in the location process for critical scenarios are investigated. Possible problems that lead to
discrepancies in the time synchronization required to achieve the highest possible positioning accuracy
when using the presented method are considered.
Keywords: 5G, positioning, timing advance, navigation

References
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OPTIMIZATION OF FORECASTING THROUGH INCORPORATION OF CAUSAL INFLUENCE

Беспала О. М. (Bespala O.M.) — 2025-06-21

The article explores approaches to improving forecasting accuracy by incorporating causal
influence between variables with time delays. The study uses Granger causality to detect temporal lags and
adjust input variables accordingly. A method is proposed to enhance LSTM-model performance by
integrating lag analysis into the training pipeline. Experimental evaluation shows that accounting for
different lag times across variables significantly reduces forecasting error. The results confirm that causallag modeling improves robustness and reflects real-world dynamics in time series forecasting. The authors
emphasize the importance of considering lagged interrelationships to form a more informative input space
for the neural network. Experimental results indicate an improvement in forecasting accuracy when causally
significant relationships are identified in advance.
Keywords: forecasting, time series, Granger causality, lag effect, prediction model, neural network

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THE IMPACT OF PROFESSIONAL CERTIFICATION OF SOFTWARE TESTERS ON THE QUALITY OF INFORMATION SYSTEMS IN THE TELECOMMUNICATIONS SECTOR

Юрчик Д. Ю. (Yurchyk D.Yu.) — 2025-06-21

The article investigates the relationship between
the level of professional training of software testers and the quality of information systems in the
telecommunications sector. The structure and requirements of international certification programs, such as
ISTQB, are analyzed, with special attention to their impact on the efficiency of software testing processes.
A comparative study of companies that have implemented personnel certification has revealed a decrease
in the number of critical system failures and an increase in the reliability of telecommunications services.
The results obtained indicate that standardized approaches to qualifying testers have a positive impact on
system performance, minimize downtime, and increase overall user satisfaction. Practical
recommendations for improving the reliability of telecommunication platforms by integrating certification
systems into human resource management and quality assurance strategies are proposed. These
recommendations can be useful for decision makers seeking to optimize software quality through targeted
investments in training and professional development of testers.
Keywords: tester certification, software quality, information systems, telecommunications, testing,
reliability, professional training

References
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ADAPTIVE CHANNEL SWITCHING ALGORITHM FOR MULTI-CHANNEL WI-FI 7 DEVICES IN HETEROGENEOUS NETWORKS

Табор Д. І. (Tabor D.I.) — 2025-06-21

Wi-Fi 7 Standard, aimed at enhancing throughput and reducing latency, faces
compatibility challenges in heterogeneous networks where devices of different generations (Wi-Fi 4/5/6)
operate simultaneously. Existing channel-switching algorithms are limited to one-dimensional parameter
analysis, ignoring load dynamics, the impact of legacy devices, and the need for real-time adaptation. This
results in inefficient spectrum utilization, increased latency, and diminished benefits of Multi-Link
Operation (MLO) technology. The Adaptive Channel Switching Algorithm (ACSA) proposes an innovative
approach based on multi-criteria analysis of channel states: load, interference levels, and transmission
success rates. Unlike traditional methods, ACSA dynamically adapts to network changes, accounts for the
influence of legacy devices, and operates in a decentralized manner. This enables efficient traffic balancing,
minimizes collisions, and optimizes the use of Wi-Fi 7’s wideband channels.
Keywords: Wi-Fi 7, MLO, channel change, ACSA, Q-Learning, DFS

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Thessaloniki, Greece. IEEE. URL: https://doi.org/10.1109/CSCN57023.2022.10050957.
6. Reinforcement Learning for Optimizing Wi-Fi Access Channel Selection / H. Nguyen et al.
Advances in Computational Collective Intelligence. Cham, 2021. P. 334–347. URL:
https://doi.org/10.1007/978-3-030-88113-9_27.

KAPPA ARCHITECTURE OPTIMIZATION DURING BUILDING SCALABLE SYSTEMS

Мельник Ю. В. (Melnyk Yu.V.) — 2025-06-21

. The article discusses the design and construction of
scalable systems using efficient Lambda and Kappa architectures. Lambda architecture has many
advantages, but the main disadvantage of this approach to designing scalable systems is its complexity due
to duplication of data processing logic. The main target of the research is to describe and develop an
alternative optimized model of the Kappa architecture, which consumes fewer resources but is excellent for
real-time event processing, which will provide an effective tool for building scalable systems and simplify
the choice. The result of the work is a comprehensive approach to building scalable systems, in which each
component of the system will be optimized through event flow analysis, which will allow for significant
improvements in real-time data processing systems.
Keywords: Kappa architecture, Lambda architecture, BigData, scalable systems, model optimization

References
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розподілених систем обробки та зберігання даних. Системи управління навігації та зв’язку,
Збірник наукових праць 2024, т. 3(77), С.84-88 URL: https://doi.org/: 10.26906/SUNZ.2024.3.08
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за критерієм повноти їхньої топологічної спостережуваності. Вісник Вінницького
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онтологічних моделей. Математичне моделювання в економіці, № 2 (15), 2019.
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APPLICATION OF ARTIFICIAL INTELLIGENCE FOR AUTOMATED ASSESSMENT OF SITUATIONAL TASKS IN CYBERSECURITY TRAINING

Легомінова С. В. (Lehominova S.V.) — 2025-06-21

The
article considers the problem of objective assessment of students' success in performing situational tasks in
higher education institutions, in particular in conditions of distance and blended learning. Analysis of
scientific publications indicates the influence of the contrast effect during assessment and the risks of
subjectivity inherent in traditional assessment methods. An automated assessment tool is proposed, based
on semantic comparison of students' answers with a reference answer, using natural language processing
(NLP) technologies and a mathematically based sentence transformer model. Based on standard Python
libraries, an improved model for automatic assessment of text situational tasks has been developed, which
performs a comprehensive analysis of the semantic and lexical coherence and completeness of students'
answers in comparison with the reference answer. Modeling and testing demonstrated a high Pearson
correlation coefficient (>0.95) between the scores generated by the model and expert assessments, which
confirms the accuracy and reliability of the results. A key advantage of the model is its ability to detect
internal plagiarism in student responses, thus supporting academic integrity. The model also significantly
reduces the time required for grading compared to traditional approaches and allows for visualization of
potential similarities.
Keywords: information technology, artificial intelligence, situational learning, automatic assessment,
cybersecurity

References
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Ievgeniia Kuzminykh et al. International Journal of Modern Education and Computer Science. 2021.
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generative artificial intelligence – an integrative literature review. Digital Education Review. 2024.
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2019-05.
4. P.A.L. Nadeesha, T.A. Weerasinghe, W.R.N.S Abeyweera. Automatic scoring of knowledge
gained and shared through discussion forums: based on the community of inquiry model. Information
Technologies and Learning Tools. 2025. Vol. 105, no. 1. P. 85–102. URL:
https://doi.org/10.33407/itlt.v105i1.5912.
5. Application of artificial intelligence for improving situational training of cybersecurity
specialists / Y. V. Shchavinsky et al. Information Technologies and Learning Tools. 2023. Vol. 97,
no. 5. P. 215–226. URL: https://doi.org/10.33407/itlt.v97i5.5424.
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https://doi.org/10.1007/s40593-014-0026-8.
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2020. Vol. 6, no. 3. P. 275–290. URL: https://doi.org/10.1016/j.eng.2019.12.014.
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SIMULTANEOUS LOCALIZATION AND MAPPING METHOD OF DYNAMIC ENVIRONMENT FOR UNMANNED SYSTEMS

Мороз М. В. (Moroz M.V.) — 2025-06-21

This paper is devoted to the study of simultaneous localization and mapping (SLAM)
methods for unmanned systems operating in complex and dynamic environments. The paper considers the
challenges associated with the need to adapt to environmental changes. The use of the Kalman filter and its
extended versions is proposed, in particular with optimization using nonlinear programming methods. The
obtained scientific results include the optimization of the Kalman filter taking into account nonlinearities
using nonlinear programming methods, as well as the integration of classical computer vision algorithms
(ORB) with convolutional neural networks. To achieve these results, the tasks of analyzing existing SLAM
methods, developing new adaptive localization algorithms, and their experimental verification on existing
datasets were solved. The results obtained can be recommended for use in projects on autonomous
navigation of unmanned ground and air platforms, especially in conditions of limited environmental
predictability, such as rescue operations, military missions, automated manufacturing and transportation
systems.
Keywords: SLAM, Kalman filter, dynamic environment, localization, unmanned systems,
convolutional neural networks

References
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construction robotics applications. Automation in Construction. 2024. Vol. 162. P. 105344. URL:
https://doi.org/10.1016/j.autcon.2024.105344.
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https://arxiv.org/abs/2005.11052.
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Technology. 2020. URL: https://doi.org/10.1016/j.dt.2020.09.012.
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arXiv. URL: https://doi.org/10.48550/arXiv.2211.01941.
7. Kim, A., Osep, A., & Leal-Taixe, L. (2021). EagerMOT: 3D Multi-Object Tracking via
Sensor Fusion.
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https://doi.org/10.1109/tro.2015.2463671.
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the Positioning Method of Power Inspection Robot in Complex Environment / C. Wang et al.
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SLAM. Applied and Computational Engineering. 2023. Vol. 4, no. 1. P. 569–573. URL:
https://doi.org/10.54254/2755-2721/4/2023325.

FLASH CALLS IN MODERN TELECOMMUNICATION NETWORKS: THREATS, CHALLENGES, AND EFFECTIVE COUNTERMEASURES

Ветошко І. П. (Vetoshko I.P.) — 2025-06-21

This paper addresses the phenomenon of Flash Calls — ultra-short incoming
calls used for user authentication in modern telecommunication networks — and their impact on network
security and operator revenues. Unlike traditional SMS-based methods, Flash Calls allow cost-effective,
high-speed verification, but simultaneously bypass billing systems and facilitate fraudulent practices such
as CLI spoofing and international revenue sharing fraud (IRSF). The paper explores the technical
characteristics of Flash Calls, highlights their detection complexity, and analyses vulnerabilities in current
anti-fraud systems. A multi-layered countermeasure architecture is proposed, integrating real-time
analytics, machine learning algorithms, and STIR/SHAKEN protocols to identify and block fraudulent
activity. The effectiveness of the approach is supported by traffic analysis and experimental blocking results
on global platforms such as Meta. The proposed system ensures reliable identification of malicious patterns
while minimizing false positives and preserving service quality.
Keywords: Flash Calls, CLI spoofing, fraud detection, Voice firewall, Wangiri, machine learning,
STIR/SHAKEN, telecom security, IMS network

References
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https://www.infobip.com/blog/what-is-a-flash-call.
2. Taylor L. CFCA 2021 Global Fraud Loss Survey. New York, NY, USA : CSFA, 2021. 67 p,
URL: https://cfca.org/document/2021-fraud-loss-survey/.
3. GSMA. Flash Call Traffic Analysis Report 2024. GSMA Intelligence.
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5. Vetoshko I.P., Kravchuk S.O. Opportunities to Improve the Quality of Voice Services in 5G
Networks // 2023 IEEE International Conference on Information and Telecommunication
Technologies and Radio Electronics (UkrMiCo), ISBN: 979-8-3503-4848-4, 13-18 November 2023,
Kyiv, Ukraine. https://doi.org/10.1109/UkrMiCo61577.2023.10380376.
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7. Vetoshko I.P., Kravchuk S.O. Possibilities of improving the voice services quality in 5G
networks // Information and Telecommunication Sciences. – 2023. – Vol.14, No 2. – P. 9-16,
https://doi.org/10.20535/2411-2976.22023.9-16
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(SHAKEN): Governance Model and Certificate Management. Effective from 2021-10-05. Official
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Sophia Antipolis Cedex, 2024. 371 p.
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METHODOLOGY FOR FORMING INTEGRAL INDICATORS OF NETWORK CONNECTION QUALITY IN PACKET-SWITCHED NETWORKS

Брезіцький С. М. (Brezitskyi S.M.) — 2025-06-21

The article addresses quality assurance in packetswitched telecommunication systems. Growing network traffic and higher QoS demands challenge
operators with the complexity of comprehensively evaluating network connections across multiple
parameters (delay, packet loss, error rate, jitter).
The research analyzes existing telecommunications studies and ITU-T recommendations Y.1540,
Y.1564, and Y.1731, which define IP network quality indicators. Current evaluation methods, while
effective, often lack assessment integrity, necessitating integral quality indicators.
The study aims to create integral indicator models accounting for synergistic relationships between
network performance parameters. It develops indicators including throughput ratio (TR), priority queue
quality factor (PQF), non-priority queue quality factor (NQF), and network connection quality factor
(NCQF). Additionally, it proposes a "fault tree analysis" model to analyze performance deterioration
causes.
These findings have practical applications in real-time monitoring, fault diagnosis, architecture
optimization, and user need adaptation. The methodology offers flexibility and versatility for different
network types and services, improving assessment accuracy and operational efficiency. This creates a
foundation for more reliable, adaptive telecommunication systems meeting modern requirements.
Keywords: telecommunication networks, quality of network connections, integral indicators, network
performance, delay, jitter, error rate, QoS, interconnection model, throughput, network monitoring, network
optimisation, packet switching

References
1. Recommendation ITU-T Y.1540 (12/2019). Internet protocol data communication service –
IP packet transfer and availability performance parameters. URL: https://www.itu.int/rec/T-RECY.1540-201912-I/en.
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URL: https://www.itu.int/rec/T-REC-Y.1564-201602-I/en.
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Pearson Education, Limited, 2003. 408 p.
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RESEARCH INTO THE PROBLEMS OF FUNCTIONING OF INTELLIGENT NETWORKS USING THE INTERNET OF THINGS

Галаган Н. В. (Halahan N.V.) — 2025-06-21

The article is devoted to the
consideration of the Internet of Things technology, where it is necessary to first of all evaluate the basic
principles, key tasks, as well as the most modern approaches and solutions.
The article proves that the Internet of Things is associated with a physical action or event. It forms a
response to a real-world factor. At the same time, a single sensor can generate a huge amount of data, for
example, an acoustic sensor for preventive equipment inspection. In other cases, a single bit of data is
enough to convey important information about the state of the system. Sensor systems have evolved and,
in accordance with Moore's law, have shrunk to sub-nanometer sizes and become significantly cheaper.
This is what predicts that many devices will be connected to the Internet of Things, and this is why these
predictions will come true.
Therefore, when considering the Internet of Things, it is necessary to consider microelectromechanical
systems, sensors and other types of low-cost edge devices and their electrophysical properties. This also
applies to the power systems needed to power the edge devices.
Keywords: sensors, differentiated privacy, Internet of Things, information system, privacy protection

References
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Українського науково-дослідного інституту зв'язку. - 2018. - № 3. - С. 5-11.
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by cognitive radio and machine learning,” China Commun., vol. 15, no. 12, pp. 16-48, Dec. 2018.

REVIEW OF THE RESEARCH ON NOTE RECOGNITION FROM AUDIO DATA USING NEURAL NETWORKS

Бай Я. В. (Bai Y.V.) — 2025-06-21

This article explores modern neural network approaches to automatic music note recognition
from audio recordings. The study examines various methods, including Fast Fourier Transform (FFT),
Convolutional Neural Networks (CNN), and Residual Shuffle-Exchange Networks (RSE). Each approach
is analyzed in terms of accuracy, adaptability, and performance in real-time scenarios. The paper highlights
the strengths of deep learning techniques in handling challenges such as background noise, polyphonic
textures, and variability in note articulation across different instruments. Experimental results show that
CNNs and RSE-based models significantly outperform traditional signal processing methods, achieving
precision rates exceeding 80%. Special attention is given to data preprocessing, feature extraction, and the
design of deep architectures suited for musical tasks. The research also emphasizes the importance of
diverse datasets, including both real and synthetic recordings, for improving generalization. The findings
indicate the strong potential of neural networks in applications such as music transcription, live
performance analysis, and music education, offering real-time and highly accurate note recognition
systems.
Keywords: Note recognition, audio analysis, neural networks, convolutional neural network, RSE
network, music transcription, deep learning, sound processing, machine learning, artificial intelligence

References
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DEEP LEARNING IN SPEECH SYNTHESIS SYSTEMS

Іщеряков С. М. (Ishcheriakov S.M.) — 2025-07-26

Deep learning systems
allow you to automate complex tasks that previously required human intelligence, and do so with high
accuracy. Deep learning uses artificial neural networks with many layers – each layer processes information
in an increasingly complex and abstract way. This allows the system to learn high-level features such as
emotions, intonations, expressiveness, etc. The introduction of these features makes synthesized speech
more natural, which contributes to its better perception by humans. Unlike traditional speech synthesis
methods, such as formant synthesis, concatenative synthesis or HMM-based approaches (Hidden Markov
Models), deep learning provides much higher flexibility and sound quality. In traditional systems, speech
was built from pre-recorded fragments or generated according to predefined rules, which limited the
naturalness, intonation richness and emotional coloring of the voice. Thus, deep learning overcomes key
limitations of traditional approaches and opens up new opportunities in the field of voice technologies –
from text-to-speech to full-fledged emotional communication between humans and machines. The article
considers the main areas of application of deep learning for speech synthesis, analyzes existing approaches
to building synthesis systems, and analyzes their weaknesses and strengths.
Keywords: deep learning, neural network, synthesized speech

References
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14. Luong T., Pham H., Manning C. D. Effective Approaches to Attention-based Neural
Machine Translation. Proceedings of the 2015 Conference on Empirical Methods in Natural
Language Processing, Lisbon, Portugal. Stroudsburg, PA, USA, 2015.
URL: https://doi.org/10.18653/v1/d15-1166.

METHODOLOGY FOR ENSURING ADAPTATION AND SELF-ORGANIZATION OF A SENSOR NETWORK UNDER CONDITIONS OF INTERFERENCE AND CYBER INFLUENCE

Охрамович М. М. (Okhramovych M.M.) — 2025-07-26

The article examines the functioning process of a sensor network (SN) in which an adaptation
and self-organization methodology is applied under conditions of interference and cyber influence. This
methodology is based on the use of test diagnostics of the SN, self-organization, and restoration of stable
operation.
Sensor networks consist of a large number of multifunctional devices that serve as highly efficient
solutions in information collection systems. Ensuring information security in SNs is quite challenging, as
it requires key exchange and session maintenance, which affects the number of transactions between
network nodes. The reliability of transmitted information is achieved by receiving confirmation after data
transmission from properly functioning network sensors. The minimal size of the devices makes sensor
technology highly flexible and practical.
Keywords: test diagnostics, network self-organization, interference environment, cyber influence,
sensor network, sensor

References
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Вісник Національного технічного університету “ХПІ”: зб. наук. праць. Тематичний випуск:
Нові рішення в сучасних технологіях. 2012. № 26. С. 55–61.
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сенсорних мереж: пояснювальна записка до атестаційної роботи здобувача вищої освіти на
другому (магістерському) рівні, спеціальність 172 Телекомунікації та радіотехніка
/М. М. Петрушко; М-во освіти і науки України, Харків. нац. ун-т радіоелектроніки. – Харків,
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METHODS OF IMPLEMENTING RETRIEVAL-AUGMENTED GENERATION IN COMBINATION WITH MODERN LARGE LANGUAGE MODELS

Олійник Б. О. (Oliinyk B.O.) — 2025-07-27

The article presents a comprehensive study of stateof-the-art Retrieval-Augmented Generation (RAG) methods integrated with large language models such as
GPT-4 and open GPT-4-equivalent models (GPT-4o). We analyze experimental results from 2023-2025
(including open sources like Arxiv, HuggingFace, PapersWithCode) that demonstrate the advantages of
new approaches: RAG 2.0 with joint end-to-end training of components, adaptive dynamic retrieval,
generative prompts for retrievers, self-refining pipelines with feedback, and modular architectures for APIbased models. We explain how these innovations overcome the shortcomings of traditional RAG by
reducing hallucination rates and improving answer faithfulness. The mechanisms of the models are
described, along with system workflow diagrams and a comparative table of metrics (Hallucination Rate,
Answer Faithfulness, Retrieval Precision). We substantiate the potential of advanced RAG in information
systems, chatbots, and analytics platforms. A clear vision for further development is formulated –
integrating RAG with continuously updated knowledge bases and real-time search APIs to ensure up-todate responses.
Keywords: large language models, retrieval-augmented generation, RAG 2.0, dynamic retriever, selfreflection, hallucinations, factual accuracy

References
1. Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks / Lewis M. et al.
NeurIPS, 2020. URL: https://medium.com/@adnanmasood/contextually-enriched-knowledgeenhanced-and-externally-grounded-retrieval-models-for-fun.
2. RAG 101: Demystifying Retrieval-Augmented Generation Pipelines / Wolff H. NVIDIA
Technical Blog, 2023. URL: https://developer.nvidia.com/blog/rag-101-demystifying-retrievalaugmented-generation-pipelines.
3. Self-RAG: Self-Reflective Retrieval-Augmented Generation / Asai A. et al.
arXiv:2310.11511, 2023. https://doi.org/10.48550/arXiv.2310.11511.
4. Contextual AI. Introducing RAG 2.0 / Kiela D. Medium blog post, 2024. URL:
https://medium.com/@jackdrummond_16745/introducing-rag-2-0-revolutionising-ai-and-llmdevelopment-71a1f85cc0dd.
5. Active Retrieval-Augmented Generation / Mallen E. et al. arXiv: 2409.11136, 2023. URL:
https://doi.org/10.48550/arXiv.2409.11136.
6. Contextually Enriched, Knowledge-Enhanced, and Externally Grounded Retrieval Models /
Masood A. Medium, 2025.
7. Promptriever: Promptable Retrieval Models / Weller O. et al. EMNLP, 2024 (preprint). URL:
https://medium.com/@techsachin/promptriever-first-zero-shot-promptable-instruction-trainedretriever-model-72e9f2eecbb2.
8. Speculative RAG: Enhancing RAG through Drafting / Wang Z. et al. arXiv:2407.08223, 2024.
URL: https://doi.org/10.48550/arXiv. 2407.08223.

ANALYSIS OF BIOMEDICAL 3D DATA SEGMENTATION METHODS USING DEEP LEARNING

Лащевська Н. О. (Lashchevska N.O.) — 2025-07-27

This article provides a comprehensive review of modern approaches to the segmentation of
biomedical 3D scans using deep learning techniques. A comparative analysis of neural network
architectures is conducted, particularly convolutional neural networks (CNNs), Transformers, and the latest
Mamba-type models, which offer varying levels of computational complexity, accuracy, and generalization
capability. The advantages of hybrid architectures, multimodal approaches, and pretraining strategies –
including self-supervised learning and generative adversarial networks (GANs) – are described as methods
to improve performance under conditions of limited labeled data. The article highlights current
segmentation challenges related to the high complexity of biomedical images, variability in scanning
protocols, and resource constraints. Practical recommendations are offered for selecting the appropriate
architecture based on task requirements, dataset characteristics, and available computational resources.
Standard evaluation metrics (e.g., Dice coefficient, Hausdorff distance) and the use of publicly available
medical datasets (BraTS, LiTS, ACDC) for model benchmarking are also discussed.
Keywords: medical image segmentation, 3D biomedical data, deep learning, convolutional neural
networks, transformers, Mamba architecture, medical imaging, multimodal data, data augmentation, neural
models, computational efficiency, self-supervised learning, BraTS, LiTS, ACDC

References
1. Ronneberger, O., Fischer, P., & Brox, T. U-Net: Convolutional networks for biomedical image
segmentation. MICCAI, 234–241.
2. Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., Xing, L. TransUNet: Transformers Make
Strong Encoders for Medical Image Segmentation. arXiv preprint arXiv:2102.04306.
3. Hatamizadeh, A., Nath, V., Liu, Y., Yin, Z., Kapp, D., & Tagare, H. Swin-Unet: Unet-like Pure
Transformer for Medical Image Segmentation. arXiv preprint arXiv:2205.05003.
4. Zhang, Y., Yang, J., & Wang, Q. GAN-based data augmentation for liver tumor segmentation
in CT scans. Medical Image Analysis, 78, 102401.
5. Zhou, Z., Seyedhosseini, M., & Tasdizen, T. Self-supervised learning for 3D medical image
segmentation. EEE Transactions on Medical Imaging, 42(3), 901–912.
6. Kim, H., Lee, J., & Park, J. Efficient3DNet: Lightweight 3D convolutional networks for fast
medical image segmentation. Computerized Medical Imaging and Graphics, 100, 102198.
7. S. Niyas, S.J. Pawan, M. Anand Kumar, Jeny Rajan. Medical image segmentation with 3D
convolutional neural networks: A survey. Neurocomputing, Volume 493,7 July 2022, Pages 397-413
https://doi.org/10.1016/j.neucom.2022.04.065.
8. Ali Hatamizadeh, Yucheng Tang, Vishwesh Nath, Dong Yang, Andriy Myronenko, Bennett
Landman, Holger R. Roth, Daguang Xu; Proceedings of the IEEE/CVF Winter Conference on
Applications of Computer Vision (WACV), 2022, pp. 574-584
9. Zhaohu Xing, Tian Ye, Yijun Yang, Guang Liu, Lei Zhu. SegMamba: Long-range Sequential
Modeling Mamba For 3D Medical Image Segmentation. (2024)
https://doi.org/10.48550/arXiv.2401.13560.
10. Md. Eshmam Rayed, S.M. Sajibul Islam, Sadia Islam Niha, Jamin Rahman Jim, Md Mohsin
Kabir, M.F. Mridha. Deep learning for medical image segmentation: State-of-the-art advancements
and challenges. ELSEVIER Volume 47, 2024,101504https://doi.org/10.1016/j.imu.2024.101504.

SCALABILITY PROBLEMS OF SOFTWARE-DEFINED NETWORKS

Гніденко М. М. (Hnidenko M.M.) — 2025-07-27

Softwaredefined networking (SDN) has enormous potential to transform network infrastructure, but there are
significant challenges on the road to widespread adoption. And scalability is a key challenge. Given the
rapid growth in data volumes, the number of connected devices, and bandwidth requirements, the ability of
SDN to scale effectively is critical. If an SDN controller cannot cope with a large number of devices and
data flows, this can lead to performance degradation, latency, and even network failures. Research
initiatives aimed at addressing scalability issues in SDN are extremely relevant. These could include
developing more efficient flow control algorithms. This would allow the controller to handle a larger
number of requests without compromising performance. Addressing the scalability issue, along with other
issues such as security, legacy compatibility, and management complexity, is key to realizing the full
potential of software-defined networks and providing operators with the efficient, flexible, and scalable
infrastructure they need. SDN scalability studies show that these problems are often not caused by SDN
and are not fundamentally unique to it, and most of these problems can be solved without losing the benefits
of SDN. If a network has tens of thousands of switching elements and can grow rapidly, the sheer number
of control events generated in any network of this scale is enough to overwhelm any centralized controller.
One way to solve this problem is to proactively enforce rules on the switches, effectively eliminating most
control requests before they reach the control plane. Nodes in SDN networks can be geographically
distributed, and the large diameter of these networks exacerbates the scalability problems of controllers.
Physical partitioning of the network can be used to divide it into separate regions; each partition can be
managed by an independent controller, and these controllers can exchange only the necessary state change
events, effectively hiding most events from external controllers.
Keywords: Software-defined network (SDN), SDN controller, scalability, performance, control
plane, data plane, ASIC, Open vSwitch

References
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comprehensive survey on software-defined networking for smart communities. Int J Commun Syst.
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A TOPOLOGICAL APPROACH TO RASTER GRAPHICS VECTORIZATION BASED ON SIMPLICIAL COMPLEXES

Сітко Д. О. (Sitko D.O.) — 2025-07-27

This paper elaborates a topological approach to raster graphics vectorization using
methods of Topological Data Analysis (TDA): simplicial complexes and persistent homology. The primary
focus is on the development of an algorithm for the construction of simplicial complexes, which is an
essential tool for identifying the structural and spatial characteristics of an image. The properties of
simplicial complexes in the context of pixel-based data analysis are investigated, and an algorithm for
generating simplicial structures from raster images is proposed. The developed approach enables effective
analysis of topological relationships between pixels and facilitates the extraction of significant components
for subsequent transformation into a vector representation. A simplicial homology search algorithm is also
described. The proposed method enhances image segmentation accuracy, which is critical for high-quality
vectorization, particularly in cases of complex geometry, structural heterogeneity, and image noise. The
results demonstrate the potential of topological methods for improving image preprocessing and
segmentation, offering new perspectives in vectorization tasks. The research lays a foundation for further
algorithmic development and practical implementation in computer graphics and image processing
applications.
Keywords: image vectorization, simplicial complexes, topological data analysis, image
segmentation, raster graphics, TDA, topological methods, Vietoris-Rips complex, persistent homology

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23. Otter, N., Porter, M. A., Tillmann, U., Grindrod, P., & Harrington, H. A. (2017). A roadmap
for the computation of persistent homology. EPJ Data Science, 6, 17. URL:
https://doi.org/10.1140/epjds/s13688-017-0109-5(SpringerOpen).

USING CLOUD TECHNOLOGIES IN THE DEVELOPMENT OF A TELEGRAM CHATBOT IN PYTHON USING SQLITE DATABASE

Шикула О. М. (Shykula O.M.) — 2025-07-27

Using cloud technologies
in the development of a Telegram chatbot in Python using the SQLite database. This article analyzes
chatbots and reveals the role that bots play in the modern world. The project was implemented using the
Python programming language and the PyCharm integrated development environment. In the process of
developing the Telegram chatbot, preference was given to using the SQLite database. To ensure
communication with the Telegram messenger, the Aiogram, Sqlite packages and the communication
interface with the Telegram Bot API application were used. The bot was officially registered in Telegram
with a unique search identifier @TgSQL_bot. Testing was carried out both during the development process
and after completion, including manual testing of the functioning and response speed of the program to
verify the correctness of the bot's operation. This chatbot is an effective tool for controlling financial
expenses through the Telegram messenger, it provides users with convenient access to the necessary
information and the ability to interact with it in real time.
Keywords: Telegram chatbot, Python programming language, SQLite database

References
1. Що таке чат-бот: секрети використання та основні переваги для бізнесу. HelpCrunch.
URL: https://helpcrunch.com/blog/uk/shcho-take-chat-bot/.
2. Чат-бот. Переваги, засоби використання та як створити бота. Gerabot. URL:
https://gerabot.com/article/detalno_pro_chatboti.
3. Месенджери довіри. Reputation Construction. URL:
https://reputation.construction/mediatrust2023
4. Для 50,6% читачів основним месенджером є Telegram. Результати опитування AIN.UA.
URL: https://ain.ua/2023/03/09/telegram-osnovnyj-mesendzher-opytuvannya/.
5. Lutz M. Learning Python: Powerful Object-Oriented Programming. – O’Reilly, 2025. – 1501
p.
6. Hillard D. Practices of the Python Pro. Manning, 2019. 248 p.
7. Scavetta R. J., Angelov A. Python and R for the Modern Data Scientist: The Best of Both
Worlds. O’Reilly, 2021. 198 p.
8. Ernesti J., Kaiser P. Python 3: The Comprehensive Guide to Hands-On Python Programming.
Rheinwerk Computing, 2022. 1078 p.
9. Gorelick M., Ozsvald I. High Performance Python: Practical Performant Programming for
Humans. O’Reilly, 2020. 469 p.
10. Beaulieu A. Learning SQL: Generate, Manipulate, and Retrieve Data. O’Reilly, 2020. 380 p.
11. Кращі IDE для Python в 2023 році. Блог Mate academy. URL:
https://mate.academy/blog/python/ide-for-python-2023/.
12. Smetana M. Y. How Python brings efficiency to chatbots: enhancing user experience with
magic filters in Aiogram. Connectivity. 2024. Vol. 168, no. 2. URL: https://doi.org/10.31673/2412-
9070.2024.025559.
13. Що таке API: навіщо використовується програмістами та базові основи роботи з ним.
Академія ITSTEP. URL: https://cloud.itstep.org/blog_3/what-is-an-api-why-is-it-used-byprogrammers-and-the-basics-of-working-with-it.
14. Лавренчук C., Чабан А. Дослідження зміни погодних умов за допомогою Telegram Bot
API. COMPUTER-INTEGRATED TECHNOLOGIES: EDUCATION, SCIENCE, PRODUCTION.
2020. № 41. С. 46–50. URL: https://doi.org/10.36910/6775-2524-0560-2020-41-08.
15. SQLite / K. P. Gaffney et al. Proceedings of the VLDB Endowment. 2022. Vol. 15, no. 12.
P. 3535–3547. URL: https://doi.org/10.14778/3554821.3554842.
16. de Quattro A. Guide to SQLite: Practical Guide. Independently Published, 2024. 118 p.
17. BotFather. Можливості, команди та функціонал. Gerabot. URL:
https://gerabot.com/article/botfather_mozhlivosti_ta_funkcional.
18. Chatbot Analysis / Mr. Bhor Shubham et al. International Journal of Advanced Research in
Science, Communication and Technology. 2022. P. 405–408. URL: https://doi.org/10.48175/ijarsct3547.
19. Using Python on PythonAnywhere. Python for Everybody. URL:
https://www.py4e.com/software-pyaw.php.
20. Python in the Cloud: Let’s Explore PythonAnywhere and Other Alternatives. Codemotion.
URL: https://www.codemotion.com/magazine/languages/python-in-the-cloud-lets-pythonanywhereand-other-alternatives/.

DEVELOPMENT OF THE WEB APPLICATION "TASKMASTER" USING REACT, NODE.JS, MONGODB IN A CLOUD ENVIRONMENT

Вишнівський В. В. (Vyshnivskyi V.V.) — 2025-07-27

The article analyzed the market of web applications for task management, described existing
analogues, their advantages and disadvantages. The client and server parts of the application were
implemented. The client part includes components for entering, displaying and managing tasks, while the
server part provides data storage and processing in the database.
Using modern technologies React, Node.js, MongoDB and other modern tools, a task management
application was created, which ensures high reliability and efficiency of the application. The component
approach makes it easy to add new functions and change existing ones without much effort. Thanks to the
use of virtual DOM in React and optimized rendering, the application responds quickly to user actions.
Using Redux allows you to centrally manage the state of the application, which ensures its stable operation
even with a large number of tasks and simplifies its maintenance. Thanks to MongoDB Atlas, data is stored
in the cloud, which ensures its availability and security.
Keywords: web application, task management, React, Node.js, MongoDB

References
1. Schwarzmüller M. React Key Concepts: An in-depth guide to React's core features. Packt
Publishing, 2025. 544 p.
2. Mammino L. Node.js Design Patterns. JS, 2020. 660 p.
3. Aleksendric M., Borucki A., Domingues L. Mastering MongoDB 7.0: Achieve data excellence
by unlocking the full potential of MongoDB. Packt Publishing, 2024. 434 p.
4. Rappin N. Modern CSS with Tailwind. Flexible Styling Without the Fuss. Pragmatic
Bookshelf, 2022. 104 p.
5. Bhat K. Ultimate Tailwind CSS Handbook: Build sleek and modern websites with immersive.
Orange Education Pvt Ltd, 2023. 294 p.
6. React Router / React Router Documentation. URL: https://reactrouter.com/en/main.
7. Brown E. Web Development with Node and Express: Leveraging the JavaScript Stack.
O'Reilly, 2019. 340 p.
8. Garreau M., Faurot W. Redux in Action. Manning, 2018. 312 p.
9. Redux Toolkit / Redux Toolkit Documentation. URL: https://redux-toolkit.js.org/.
10. Building Applications with React and Redux / Pluralsight. URL:
https://www.pluralsight.com/courses/react-redux-react-router-es6.
11. Learning React: Functional Web Development with React and 18. Redux / O'Reilly Media.
URL: https://www.oreilly.com/library/view/learning-react-2nd/9781492051718/.
12. Full-Stack Web Development with React / Coursera. URL:
https://www.coursera.org/learn/full-stack-react.
13. React Hooks / React Documentation. URL: https://legacy.reactjs.org/docs/hooks-intro.html
14. HTTP Methods / Avior API Documentation. URL:
https://www.contrive.mobi/aviorapi/HTTPMETHODS.html.
15 Pro MERN Stack: Full Stack Web App Development with Mongo, Express, React, and Node.
/ Apress. URL: https://www.apress.com/gp/book/9781484243906.
16. Mastering Node.js / Packt Publishing. URL: https://www.packtpub.com/product/masteringnode-js/9781785888960.
17. Advanced Node.js Development / Udemy. URL: https://www.udemy.com/course/advancednodejs-development/.
18. Express in Action: Writing, Building, and Testing Node.js Applications / Manning
Publications. URL: https://www.manning.com/books/express-in-action.
19. MongoDB: The Definitive Guide / O'Reilly Media. URL:
https://www.oreilly.com/library/view/mongodb-the-definitive/9781491954454/.

USING CLOUD TECHNOLOGIES IN THE DEVELOPMENT OF AN INSTANT USER SUPPORT SYSTEM BASED ON PHP AND AJAX

Шикула О. М. (Shykula O.M.) — 2025-07-27

The article reviews and
analyzes existing instant support systems, analyzes user needs and requirements for instant support. As a
result of the analysis, key functions and capabilities that should be available in the system were identified.
An instant support system based on the PHP programming language and the use of Ajax technology
to ensure speed and interactivity was designed and developed. The use of these technologies allowed for
high-speed processing of requests and providing answers without the need to reload the page, which
significantly increases the convenience of using the system.
The developed instant support system has great potential for use in various industries where it is
necessary to provide fast and high-quality user service. The possibilities for further development and
improvement of the system, in particular through integration with advanced artificial intelligence
algorithms and the development of an adaptive interface, open up new prospects for improving the quality
of service and meeting user needs.
Keywords: instant support system, requirements analysis, system development, PHP, JavaScript,
Ajax, SQLite DBMS

References
1. Stuttard D., Pinto M. The Web Application Hacker's Handbook: Finding and Exploiting
Security Flaws. NY: Wiley, 2020. 912 c.
2. Ballad T., Confer W. Securing PHP Web Applications. MA: Syngress, 2008. 304 c.
3. Snyder C., Southwell M., Owad T. PHP Security. CA: O'ReillyMedia, 2005. 428 c.
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5. Schlossnagle G. Advanced PHP Programming. NJ: Pearson Education, 2004. 224 c.
6. Bergmann S., Priebsch S. Foundations of PHP for Web Developers. NY: Apress, 2015. 372
c.
7. Zandstra M. PHP Objects, Patterns, and Practice. NY: Apress, 2013. 536 c.
8. Nixon R. Learning PHP, MySQL & JavaScript. CA: O'Reilly Media, 2018. 832 c.
9. McDonald M., McGovern J., et al. Web Security for Developers: Real Threats, Practical
Defense. CA: O'Reilly Media, 2022. 542 c.
10. Wenz C. PHP Phrasebook. IN: Addison-Wesley, 2005. 480 c.
11. Nixon R. Modern PHP: New Features and Good Practices. CA: O'ReillyMedia, 2015. 268 c.
12. Welling L., Thomson L. PHP and MySQL Web Development. CA: Addison-Wesley, 2017.
672 c.
13. Duckett J. PHP & MySQL: Novice to Ninja. UK: SitePoint, 2017. 690 c.
14. Shiflett C. Essential PHP Security. CA: O'Reilly Media, 2005. 100 c.
15. Sklar D., Trachtenberg A. PHP Cookbook. CA: O'Reilly Media, 2014. 820 c.
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METHOD FOR LOCALIZING NODES IN WIRELESS SENSOR NETWORKS BASED ON THE RECONSTRUCTION OF SPARSE DISTANCE MATRICES

Кириченко Р. М. (Kyrychenko R.M.) — 2025-07-27

The article considers modern methods for localizing nodes
in wireless sensor networks with an emphasis on increasing the positioning accuracy by combining classical
multidimensional scaling (MDS) with greedy heuristic algorithms and methods for restoring the sparse
matrix of RSSI signals. A comprehensive approach to detecting anomalies and errors in measurements
using spectral analysis and machine learning algorithms is proposed. Experimental modeling in MATLAB
was carried out, which confirmed the effectiveness of the developed methods in various network operation
scenarios. The results obtained have practical significance for increasing the reliability, security, and
performance of wireless sensor networks, which is important for the development of modern Internet of
Things (IoT) systems and industrial applications.
Keywords: wireless sensor networks, localization, multidimensional scaling (MDS), greedy
heuristics, RSSI, matrix reconstruction, anomaly detection, spectral analysis, machine learning, MATLAB,
Internet of Things (IoT), network security, positioning, measurement errors

References
1. Shakunt, P.S. Diagnosis of Faults in Wireless Sensor Networks Through Machine Learning
Approach. Human-Centric Smart Computing. ICHCSC 2023. Smart Innovation, Systems and
Technologies. 2023. 376. URL: https://doi.org/10.1007/978-981-99-7711-6_17.
2. Ridha M. A., Nickray M. Fault Detection in Wireless Sensor Networks Using Horse Herd
Algorithm and Convolutional Neural Network with Attention Layer. Journal of Electrical Systems.
2024. Vol. 20, no. 11. P. 3291–3309. URL: https://doi.org/10.52783/jes.8086.
3. Feghhi M. M., Alsharfa R. M., Majeed M. H. Efficient Fault Detection in WSN Based on
PCA-Optimized Deep Neural Network Slicing Trained with GOA. International Journal of
Intelligent Engineering and Systems. 2025. Vol. 18, no. 5.
URL: https://doi.org/10.48550/arXiv.2505.07030.
4. Padhi R., Muduli D., Sharma S. Automated Fault Diagnosis System in Wireless Sensor
Network: A Fault Node Recovery Algorithm Approach. Recent Advances in Signals and Systems.
VSPICE 2023. Lecture Notes in Electrical Engineering. 2024. No. 1227.
URL: https://doi.org/10.1007/978-981-97-4657-6_25.
5. Mederos-Madrazo B., Diaz-Roman J., Enriquez-Aguilera F. Dealing with Outliers in Wireless
Sensor Networks Localization: An Iterative and Selection-Minimization Strategy. Int J Netw Distrib
Comput. 2024. P. 41–52. URL: https://doi.org/10.1007/s44227-024-00024-1.

АNALYSIS OF WAYS TO USE ARTIFICIAL INTELLIGENCE TO IMPROVE MONITORING OF SECURE ITINFRASTRUCTURE

Катков Ю. І. (Katkov Yu.I.) — 2025-07-27

The article is dedicated to the analysis of various ways of applying artificial intelligence
for monitoring the protected IT infrastructure. The modern world is undergoing rapid technological
transformation, where information technologies play a key role in the functioning of almost all aspects of
our lives. Information technology infrastructure becomes not only critically important for supporting
businesses and organizations but also determines the convenience and efficiency of using and developing
digital services in the modern world. Ensuring the continuity and optimal functioning of IT infrastructure
is of paramount importance for businesses, organizations, and society as a whole. In this regard, the
application of artificial intelligence for monitoring and supporting IT infrastructure proves to be a powerful
tool for predicting, detecting, and resolving issues before they strengthen and affect efficiency. Today’s IT
infrastructure is becoming increasingly complex and voluminous, requiring constant monitoring to timely
respond to possible failures or malfunctions that may lead to disruptions in work, financial losses, or even
threats to data security. In this context, the application of artificial intelligence becomes crucial as it can
provide fast, accurate, and forecasted solutions based on the analysis of a vast amount of data and previous
patterns of failures.
Keywords: vulnerabilities, IT infrastructure monitoring, cybersecurity, artificial intelligence

References
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https://www.cisco.com/c/en/us/products/collateral/security/stealthwatch/ secure-network-analyticsaag.html
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Cybersecurity Platform. URL: https://www.darktrace.com/products/detect
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86. P. 1106–1117. URL: https://doi.org/10.1016/j.future.2017.05.046
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Eduvest – Journal of Universal Studies. 2023. Vol. 3, no. 1. P. 265–277. URL:
https://doi.org/10.36418/eduvest.v3i1.732
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застосування штучного інтелекту для моніторингу ІТ-інфраструктури. Актуальні проблеми
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інформації про надійність / Ю. Березовська та ін. Зв’язок. 2020. № 6(148). С. 42–46.
23. Березовська Ю. Інформаційні системи безперервного використання з часовим
резервуванням. Сучасні досягнення компанії Hewlett Packard Enterprise в галузі IT та нові
можливості їх вивчення і застосування : тези доп. Міжнар. науково-практ. конф., м. Київ, 16
груд. 2020 р. Київ, 2020. С. 6–8.

FEATURES OF ADMINISTERING A CORPORATE NETWORK BASED ON WINDOWS SERVER 2022

Катков Ю. І. (Katkov Yu.I.) — 2025-07-27

The article is dedicated to the issue of determining
the peculiarities of administering a corporate network based on Windows Server 2022. There is a problem
of ensuring the security of the OS Windows Server 2022 using special vulnerability protection mechanisms
during the administration of a corporate network. Solving this problem becomes extremely important for
businesses and organizations. The IT infrastructure built on the basis of the OS Windows Server 2022 is
becoming increasingly complex and voluminous today, requiring constant administration to timely respond
to possible failures or malfunctions that may lead to interruptions in work, financial losses, or even threats
to data security. Therefore, the application of special vulnerability protection mechanisms for the OS
Windows Server 2022 proves to be a powerful tool for anticipating, detecting, and addressing potential
issues before they affect its efficiency. The article discusses key aspects and capabilities of vulnerability
protection mechanisms for software tools: Common Vulnerability Scoring System; Common
Vulnerabilities and Exposures; National Vulnerability Database. It reveals the main aspects and advantages
of their application.
Keywords: vulnerabilities, cybersecurity, Common Vulnerability Scoring System; Common
Vulnerabilities and Exposures; National Vulnerability Database

References
1. Microsoft Windows Server 2022 Security Vulnerabilities in 2025. stack.watch – Weekly
Security Vulnerability Emails. URL: https://stack.watch/product/microsoft/windows-server-2022/
2. 10 New Things in Windows Server 2022 to Know. Geekflare. URL:
https://geekflare.com/new-features-in-windows-server-2022/
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ситуаціям : монографія. Житомир : Рута, 2006. 386 с.
4. What's new in Windows Server 2022. Microsoft Learn: Build skills that open doors in your
career. URL: https://learn.microsoft.com/en-us/windows-server/get-started/whats-new-in-windowsserver-2022
5. Windows Server 2022 Security Hardening best practices. Virtualization Howto. URL:
https://www.virtualizationhowto.com/2021/12/windows-server-2022-security-hardening-bestpractices/
6. NATIONAL VULNERABILITY DATABASE. NVD – Home. URL: https://nvd.nist.gov
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терористичних загроз. «Актуальні проблеми кібербезпеки» : матеріали Всеукр. науково-практ.
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11. Функціональна стійкість інформаційних мереж при наявності обмеженої апріорної
інформації про надійність / Ю. Березовська та ін. Зв’язок. 2020. № 6(148). С. 42–46.
12. Березовська Ю. Інформаційні системи безперервного використання з часовим
резервуванням. Сучасні досягнення компанії Hewlett Packard Enterprise в галузі IT та нові
можливості їх вивчення і застосування : тези доп. Міжнар. науково-практ. конф., м. Київ, 16
груд. 2020 р. Київ, 2020. С. 6–8.