The growing complexity and frequency of cyberattacks require a transformation in cybersecurity education. Traditional approaches, which focus primarily on theoretical knowledge, certification, and compliance, often fail to provide learners with sufficient hands-on experience. To address this gap, cyber ranges have emerged as an effective tool for practical, scenario-based training in a safe and controlled environment. This paper presents the implementation of a modern cyber range system designed specifically for educational purposes. The platform is container-based and integrates seamlessly into the learning process via the LTI standard, enabling single sign-on and data exchange with the Moodle LMS. Each student receives an isolated environment on demand, with auto-generated flags embedded for individual task verification. The system automatically deploys and destroys containers based on session activity, ensuring efficient use of server resources and maintaining a secure, ephemeral environment for practice. The solution also includes network segmentation and privilege restrictions for each container, enhancing security while simulating real-world conditions. The proposed architecture supports scalability and rapid deployment, making it suitable for use in classrooms, exams, and self-paced learning. In this article, we review existing approaches to building cyber ranges, discuss the design and implementation of our system, and highlight the technical and pedagogical benefits. Future developments includes support for team-based Red/Blue training, instructor analytics modules, and orchestration integration. The presented platform bridges the gap between theoretical training and real-world cybersecurity skills development, helping to prepare students for modern digital threats.

This paper investigates the impact of knowledge extraction methods on the effectiveness of RAG (Retrieval-Augmented Generation) systems that utilize knowledge graphs. It highlights that the quality of the knowledge graph, which is formed using various knowledge extraction methods, is crucial for overcoming limitations of large language models (LLMs), such as “hallucinations”. The paper analyzes the architectures of LightRAG and GraphRAG, emphasizing that the selection of an optimal knowledge extraction strategy depends on specific tasks and the subject area. LLMs have advanced significantly, but they have limitations, including generating factually incorrect information (“hallucinations”) and possessing “outdated knowledge”. RAG systems were proposed to address these issues by combining LLMs with external knowledge bases. This approach reduces hallucinations, ensures factual accuracy, solves the problem of outdated knowledge, and increases transparency. Knowledge graphs are powerful tools for structuring information, consisting of entities (nodes) and relations (edges). They enhance RAG systems by enabling more precise and contextually grounded retrieval compared to keyword-based searches. The quality of a knowledge graph depends on the knowledge extraction methods used, which include named entity recognition (NER), relation extraction (RE), entity linking (EL), and event extraction. Different methods, such as rule-based, classical machine learning, and deep learning approaches, have varying trade-offs in terms of accuracy, completeness, and scalability. Entity linking and knowledge graph completion are also crucial for accuracy and richness. LightRAG and GraphRAG are two main graph-based RAG systems. LightRAG uses the knowledge graph as a quick reference, requiring high precision in knowledge extraction to avoid noise degradation. GraphRAG uses the knowledge graph as a domain model, where completeness of extracted knowledge is more critical, though systematic errors are still harmful. Both systems rely on LLMs for knowledge extraction, which makes them dependent on the LLM’s quality and the size of document fragments processed. The theoretical analysis confirms that the effectiveness of RAG systems is critically dependent on knowledge extraction methods. The quality, completeness, and accuracy of the knowledge graph directly influence the RAG system’s ability to provide relevant, accurate, and truthful answers. Different RAG system architectures like LightRAG and GraphRAG have distinct requirements for knowledge graph characteristics, prioritizing either accuracy or completeness in knowledge extraction.

The Internet of Things (IoT) has begun transforming our digital space, in which everyday objects are interconnected and capable of communicating with each other. This transformation not only simplifies our lives but also creates unprecedented opportunities and enhances efficiency in areas such as smart homes, healthcare, industrial manufacturing, and urban management. However, as with any technological breakthrough, IoT requires a careful and well-planned implementation. The large-scale and unregulated deployment of IoT devices raises significant security concerns that must be mitigated.At the core of IoT devices lies the ability to autonomously collect, process, and transmit information independently of human intervention. While this autonomy is the primary innovation, it also introduces numerous security vulnerabilities. For instance, IoT devices often operate on private and public networks, increasinf the attack surface for malicious actors capable of compromising data confidentiality and integrity. However, security is not the sole concern. The reliability and availability of the critical functions these devices provide are equally crucial. From thermostats and smartwatches to autonomous vehicles and urban infrastructure, compromised IoT devices can cause anything from minor inconveniences to catastrophic failures affecting millions.IoT devices typically have limited computational power and memory. These constraints often render the implementation of traditional security measures, such as complex encryption algorithms, impractical. Given these limitations and the explosive growth in the number of IoT devices, the potential for unauthorized interference with systems also increases. According to Cybersecurity Ventures, the number of IoT devices worldwide is expected to reach 25.1 billion by 2025. Each device represents a potential entry point for attackers. This widespread proliferation creates a vast, often poorly secured, network of interconnected devices susceptible to exploitation by malicious actors for data theft, DDoS attacks, and other cyber threats.The infrastructure surrounding IoT devices can be divided into four levels: perception (node capture, node impersonation, replay attack, timing attack, sleep deprivation attack), network (eavesdropping, DoS/DDoS attacks, Man-in-the-Middle attacks), data processing (resource exhaustion, exploits), and application (exploits). Therefore, key security challenges in IoT include unauthorized access to devices and data, communication interception, data manipulation, and depletion of system and device resources.The purpose of this article is to describe a comprehensive proposed solution for securing IoT systems. There is an urgent need for scalable and reliable solutions. Our approach considers the unique characteristics of IoT ecosystems, particularly their diversity, limited computational resources, and the physical nature of the devices.

This paper examines the potential of integrating Large Language Models (LLMs) with knowledge bases to imrpove the accuracy and reliability of their responses. The advantages of such a combination are evaluated, particularly in reducing the risk of hallucinations – the phenomenon where models generate erroneous or fabricated information. Various methodologies for combining LLMs with knowledge bases are analyzed, along with their respective advantages and limitations. The prospects and challenges of implementing this technology in diverse fields—such as information retrieval, decision support, and automated content creation—are discussed. The paper presents an overview of the current state of research in this domain and delineates directions for future investigation.The integration of LLMs with knowledge bases represents a significant advancement in artificial intelligence, aiming one of the key concerns regarding LLMs—their tendency to generate inaccurate or fabricated information, commonly referred to as hallucinations. This approach leverages the vast language understanding and generation capabilities of LLMs while grounding their outputs in structured and verified information from knowledge bases. The synergy between these two technologies has the potential to significantly enhance the reliability and factual accuracy of AI-generated responses across a wide range of applications. The methodologies for combining LLMs with knowledge bases differ in their implementation and effectiveness. Some approaches involve pre-training LLMs on curated knowledge bases, while others reference knowledge bases externally during the inference process. Each method presents its own set of advantages and challenges, such as balancing computational efficiency against accuracy and maintaining the fluency of LLM outputs while adhering strictly to factual information. The application of this integrated technology extends beyond mere information retrieval, showing promise in complex decision support systems, automated content creation for specialized domains, and contributing to the advancement of explainable AI by providing traceable sources for generated information. As research in this area progresses, it is expected to open new avenues for developing more trustworthy and capable AI systems across various industries and academic disciplines.


 
 
 With the development of the Big Data sphere, as well as those fields of study that we can relate to artificial intelligence, the need for fast and efficient computing has become one of the most important tasks nowadays. That is why in the recent decade, graphics processing unit computations have been actively developing to provide an ability for scientists and developers to use thousands of cores GPUs have in order to perform intensive computations. The goal of this research is to implement orthogonal decomposition of a matrix by applying a series of Householder transformations in Java language using JCuda library to conduct a research on its benefits. Several related papers were examined. Malaschonok and Savchenko in their work have introduced an improved version of QR algorithm for this purpose [4] and achieved better results, however Householder algorithm is more promising for GPUs according to another team of researchers – Lahabar and Narayanan [6]. However, they were using Float numbers, while we are using Double, and apart from that we are working on a new BigDecimal type for CUDA. Apart from that, there is still no solution for handling huge matrices where errors in calculations might occur.
 The algorithm of orthogonal matrix decomposition, which is the first part of SVD algorithm, is researched and implemented in this work. The implementation of matrix bidiagonalization and calculation of orthogonal factors by the Hausholder method in the jCUDA environment on a graphics processor is presented, and the algorithm for the central processor for comparisons is also implemented. Research of the received results where we experimentally measured acceleration of calculations with the use of the graphic processor in comparison with the implementation on the central processor are carried out. We show a speedup up to 53 times compared to CPU implementation on a big matrix size, specifically 2048, and even better results when using more advanced GPUs. At the same time, we still experience bigger errors in calculations while using graphic processing units due to synchronization problems. We compared execution on different platforms (Windows 10 and Arch Linux) and discovered that they are almost the same, taking the computation speed into account. The results have shown that on GPU we can achieve better performance, however there are more implementation difficulties with this approach.
 
 

Communication networks are complex information systems influenced by a vast amount of factors. It is critically important to forecast the paths that data take to verify the network, check its security and plan its updates. Model allows exploring processes that take place in the network without affecting performance and availability of a real network itself. With modelling it becomes possible to investigate the results of infrastructural changes introduced to the network before actually implementing them. It is important to be able to formally convert real network description into the model definition which preserves all data that is significant for network operation and skip data which is not. Outlining the rules for such conversion and using a limited set of basic functional components provide the ground for automatic model creation for the network of different levels of complexity.Proposed approach to modelling of communication networks is based on decomposition of the overall function of every particular real network component into a set of functions that belong to some predefined basic set. Functions of the basic set include L3 routing, L2 switching, packet filtering, NAT, etc. Model of a real network component is defined as a group of functional nodes each of which implements some function from the basic set.Configuration and current state of network components that influence its operation are also decomposed into elements each of which relates to some particular functional node. Configuration of network components is modelled as a set of configuration storage elements and current state is modelled as a set of current state storage elements.Links that connect real network components and links that connect functional nodes in the model are presented as singledirection channels that implement propagation of L2 frames thus simplifying the model due to excluding physical layer (L1) from the scope.Using the proposed approach to modelling may allow to formalize conversion of a real network descrip- tion to a model thus making automated modelling possible. By using a sufficient basic set of functional nodes it is possible to model the network containing components of any complexity level.

The paper investigates a possibility of developing a non-virtual hierarchy for a special case of class signature, which may possess different interpretations. The approach is similar to double dispatching in the C ++ programming language. As an alternative to polymorphism, a non-polymorphic hierarchy has been suggested based on generic programming templates. This hierarchy is based on inverse parametrization for templates enabling constructing a general scheme for the design pattern. The pattern defined a class architecture suitable for static implementation of double dispatched multimethod for a special case of signature- defined interfaces.In fact, any abstract base class (interface) with purely virtual operations must acquire a polymorphic implementation. Besides, the polymorphism itself, the dependence of a virtual function on two objects – “this” and another parameter – requires the use of double dispatch, turning a class member function into a double dispatched multimethod.A preliminary consideration deals with issues of double dispatching in the C++ programming language. Inheritance with polymorphic class member functions is used. This requires special efforts of adding to both bases and derived classes a couple of virtual functions to support dispatching. In any case, this approach, besides using virtual functions, has a disadvantage of violating one of the SOLID principles, namely the principle of dependency inversion: base classes should not depend on derivatives, which negatively affects the quality of the software.Polymorphism is usually understood as the dynamic tuning of a program to the data type of the object that the program will encounter during its execution. That is, by its nature, polymorphism is a purely dynamic characteristic. However, in C++ literature and in practice, you can come across the term “static polymorphism”.At the same time, research of possibilities of generalized programming (templates) allows transferring some dynamic problems to the static level. In particular, a variant of static polymorphism application without virtual functions can be considered.A variant of non-virtual double scheduling has been proposed, generalized in the form of a created design pattern “Signature multimethod”. The use of the newly created pattern is illustrated with an example of implementing classes of complex numbers. The absence of violations of SOLID principles is shown, and the possibility of supplementing the hierarchy with new derived classes without the need to interfere with the structure of the base class is demonstrated.The approach suggested in this work has been used in courses in object-oriented programming at the Faculty of Informatics of Kyiv-Mohyla Academy.

Static code analysis is a very important stage in the development and implementation of software, and it needs to be used to obtain a better code. The most complicated in static analysis is the analysis of source code, and further analysis. Analysis of the source code can be done multiple times and expand the set of necessary grammar. The main goal was and remains to provide a solution to reduce the time needed for a global reassessment of static analysis at code level after a change.The main problem when using static code analysis is to build an abstract semantic graph, because each software solution is provided with a separate data warehouse. The proposed solution proposes to use as a database repository using graphs. Thus, the storage mechanisms of the created abstract semantic graph have been simplified, which in turn, in addition to increasing the clarity of the information we store, provides convenient ways to further work with the stored information.The developed solution can transform a rather large source repository into a graphic representation and maintain it later. It is found that the approach is suitable for carrying out code compliance checks and for performing static analysis tests on a graphic representation. This approach also uses advanced file-level detailing, accelerating static analysis. Based on my measurements, the frameworks are fast enough to help their users quickly change the repository of codes.This article confirms the thesis about the possibility of storing an abstract semantic graph in a graph database, and after refinement, if it contains sufficient transformations and requests for language processing, can become a complete transport for communication between various static analysis tools that usually perform one of two the functions of verification either for quality or for vulnerability, thereby making a unified creation of an abstract semantic graph.As an improvement it is necessary to consider the possibility of incremental analysis – the analysis of changes in the code, in order to minimize resource costs for a rather resource intensive operation of the structure of the abstract semantic graph.Manuscript received 09.06.2020

This paper reviews sales forecasting methods and its ability to be used in modern enterprise resource planning systems. Having Dynamics 365 Business Central as an example was reviewed for the practical application of such methods, including those using machine learning techniques. During the described work existing solution based in time series algorithms was investigated and analyzed in detail, suggestions were made regarding the clustering techniques usage.Enterprise resource planning is defined as an ability to deliver the business software that unifies processes and data models within various areas such as finance, human resource management, supply chain management, manufacturing, distribution, etc. This paper highlights the problems that can be solved using this type of software, namely: automate the processes that were not formalized before; unify data captured by different departments for its better analysis; collect, group, and aggregate information for analytical and reporting purposes. Pros and cons for building its own solutions compared to implementation of solutions from reliable vendors are presented. Top vendors on the market are identified.Benefits of accurate sales forecasting and its importance to the business are identified: preventing missed sales, decreasing storage costs, improving marketing activities, decreasing write-offs, and improving cash flow. From another side, the common sales forecasting techniques are listed and described: clustering, descriptive, factor, time series, and regression analysis methods. Machine learning is considered as a prospective option for efficient automation of the analytical processes.Next, the specific implementation of enterprise resource planning system – Dynamics 365 Business Central – is reviewed in scope of the sales forecasting problem. It is identified that the ability of the standard solution is limited with Sales Budgeting functionality that can support manual sales forecasting but lacks automation for the future sales figures. However, its system can be extended using Sales and Inventory Forecast which in its turn can utilize the benefits of artificial intelligence techniques by means of Microsoft Azure Machine Learning service. Existing experiment that was reviewed in scope of this paper, had been based on Microsoft Time Series algorithm and combined the best results from the following techniques: autoregressive integrated moving average (ARIMA), exponential smoothing (ETS), and seasonal and trend decomposition using LOESS (STL).The following potential areas for improvement were identified in the existing approach: for large item portfolio the seasonality may significantly differ from one group of items to other; the same approach is applied to items on different life cycle stage; there could be a huge difference in behavior of slow and fast moving items; non-moving items may impact the overall picture.As a potential improvement, the usage of the clustering analysis methods is proposed. The author suggests splitting items into clusters using k-means machine learning method before performing time series analysis and assessing the impact of this approach on a large verified data set.Manuscript received 10.06.2020

ПЕРЕДМОВАУ цьому випуску журналу «Наукові записки НаУКМА.Комп'ютерні науки» представлено творчі здобутки вчених, магістрів факультету інформатики університету, а також фахівців з інших наукових центрів України, що співпрацюють із НаУКМА в галузі інформатики, кібернетики, програмування.Основним напрямом досліджень авторів статей збірника є теоретичні проблеми кібернетики і програмування та практичні застосування програмних технологій в галузі сучасної інформатики і прикладної математики.До підготовки й випуску збірника докладено чимало зусиль.Ми вдячні керівництву НаУКМА, без фінансової та організаційної допомоги якого це