CSIT - 2025 рік

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https://elar.khmnu.edu.ua/handle/123456789/18099

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Перегляд CSIT - 2025 рік за Автор "Drozd, Andriy"

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  • Ескіз
    Документ
    Decision-making method in interdependent computing systems
    (Хмельницький національний університет, 2025) Kryzhanyvskyi, Dmytro; Drozd, Andriy; Besedovskyi, Oleksii
    The relevance of this paper lies in the fact that modern interdependent computing systems are being actively implemented in critical areas ranging from smart energy grids and transportation systems to autonomous robotic platforms and distributed cloud services. These systems are characterized by a complex structure, a large number of interacting agents, and high requirements for real-time decision-making. Despite significant scientific and technological progress, a number of challenges remain unresolved to ensure the sustainability, adaptability, and coherence of all system components. One of the key challenges is the need to ensure rational decision-making in a decentralized environment where each agent has limited information about the state of the system as a whole and operates under conditions of uncertainty and potential distrust of other agents. Classical centralized methods are often ineffective or inapplicable in such cases due to excessive complexity or delays in data exchange. The issue of developing methods that ensure not only the correctness of decisions but also compliance with time constraints is particularly relevant. In interdependent computing environments, where the decision of one agent affects the outcome of the work of others, any delay or error in the strategy can lead to degradation of the performance of the entire system. In such environments, it is crucial to use adaptive, game-based, and reputation-based approaches that allow for dynamic consistency and stability of the system. In this paper, we develop a decision-making method for interdependent computing systems that combines Bayesian reputation updating, log-linear strategy learning, and reinforcement learning mechanisms. The peculiarity of the proposed method is its ability to adapt to changes in the environment and effectively detect unscrupulous agents by dynamically adjusting reputations. The algorithmic implementation of the model allows achieving the Bayesian-Nash equilibrium, which indicates the stability of the system even in complex interaction scenarios. The results of experimental modeling have demonstrated that the proposed method strikes a balance between adaptability, reliability, and efficiency of interactions. The system demonstrates the ability to self-organize, stabilizes in fewer iterations compared to classical approaches, and effectively prevents the influence of sabotaging behavior of individual agents. The prospect of further research is to adapt the model to different types of computing environments, including MEC infrastructures, edge systems, and IoT platforms. Special attention is planned to be paid to the development of new objective functions that would take into account not only the stability and speed of convergence, but also energy consumption, network bandwidth, and quality of service (QoS).
  • Ескіз
    Документ
    Monitoring system for critical infrastructure objects based on digital twins
    (Хмельницький національний університет, 2025) Andrieiev, Dmytro; Lyhun, Oleksii; Drozd, Andriy; Ponochovna, Olena
    Critical infrastructures are fundamental to the seamless operation of modern societies, encompassing sectors such as energy, healthcare, transportation, and communications. Ensuring their reliability, performance, continuous operation, safety, maintenance, and protection is a national priority for countries worldwide. The digital twins play a crucial role in critical infrastructure, as they enhance security, resilience, reliability, maintenance, continuity, and operational efficiency across all sectors. Among the benefits offered by digital twins are intelligent and autonomous decision-making, process optimization, improved traceability, interactive visualization, and real-time monitoring, analysis, and prediction. Furthermore, the study revealed that digital twins have the capability to bridge the gap between physical and virtual environments, can be used in combination with other technologies, and can be integrated into various contexts and industries. The use of digital twins was explored as the foundation for developing a modern monitoring system for critical infrastructure facilities enables multi-level assessment of asset conditions in real time, ensuring precise threat detection, anomaly identification, and timely decision-making. Integration with artificial intelligence and big data technologies allows not only the collection and analysis of large volumes of information but also the creation of adaptive behavioral models for systems in emergency situations. Special attention was given to the method of optimizing critical IT infrastructure using digital twins, which combines virtual modeling, predictive algorithms, and automated management. The proposed approach enhances the reliability of digital systems, minimizes downtime, optimizes maintenance costs, and strengthens cybersecurity. This system is especially relevant in the context of growing risks and increasing demands for the stability of strategically important infrastructure assets. The application of digital twins for monitoring and optimizing critical infrastructure demonstrates considerable potential for improving its resilience, safety, and operational efficiency. The approaches discussed in the study confirm the relevance of implementing digital models as tools for timely risk identification, failure prediction, and informed decision-making. By integrating such technologies, organizations can reduce operational costs, minimize downtime, and improve the overall stability of infrastructure operations. Therefore, digital twins represent a vital step toward the digital transformation and modernization of mission-critical systems across various sectors.
  • Ескіз
    Документ
    System for cybersecurity evaluation of corporate networks
    (Хмельницький національний університет, 2025) Ramskyi, Ihor; Drozd, Andriy; Lyhun, Oleksii; Ponochovna, Olena
    In the context of rapidly increasing cyber threats and the growing complexity of corporate IT infrastructure, ensuring a reliable and proactive approach to cybersecurity is becoming critically important for organizations of all sizes. Traditional cybersecurity assessment methods often fail to keep up with the dynamic nature of emerging threats – necessitating the development of more adaptive and intelligent evaluation systems. This article presents a comprehensive modular system for assessing the cybersecurity level of corporate networks – offering a holistic view of the security landscape by integrating both technical and organizational indicators. The proposed system utilizes self-organizing analytical methods to dynamically process large volumes of data related to vulnerabilities, configuration states, and network behavior patterns. Through intelligent algorithms and adaptive learning, the system is capable of autonomously detecting anomalies, evaluating potential attack vectors, and correlating threats with the network’s weak points. Additionally, the inclusion of organizational factors – such as policy compliance, user behavior, and access structures – enables a more contextual and in-depth risk assessment. A key advantage of the system is its ability to perform real-time monitoring and dynamic risk evaluation – empowering decision-makers to take informed actions in response to incidents. The system's architecture supports scalability and compatibility with existing security tools and network management platforms. To validate its effectiveness, the system was implemented and tested in a simulated corporate environment reflecting modern structural and operational challenges. The experimental results confirmed its capability to identify vulnerabilities, prioritize responses, and enhance overall cyber resilience. This research contributes to the advancement of next-generation cybersecurity assessment tools – ensuring the continuous improvement of corporate defense mechanisms in an ever-changing cyber landscape.