Computer Systems and Information Technologies=Комп'ютерні системи та інформаційні технології

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https://elar.khmnu.edu.ua/handle/123456789/9492

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Перегляд Computer Systems and Information Technologies=Комп'ютерні системи та інформаційні технології за Автор "Atamaniuk, Olha"

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  • Ескіз
    Документ
    Analysis of information technologies and methods for automatic updating of threat detection models in computer systems
    (Хмельницький національний університет, 2026) Isaiev, Tymur; Atamaniuk, Olha
    The development of intelligent adaptive information technologies for automatic updating of threat detection models in computer systems is one of the most important directions in modern research on information technologies. Computer systems today operate in environments that are constantly changing, influenced by new software, evolving hardware, and diverse data processing methods. Traditional static approaches, which rely on fixed rules or predefined models, often become outdated quickly and fail to provide the necessary adaptability. Existing approaches to detection in computer systems have been studied extensively, and while they provide valuable insights, they also demonstrate clear limitations. Signature-based methods depend heavily on known patterns and therefore struggle to identify new or unexpected phenomena. Heuristic analysis allows for broader generalization but is frequently associated with high rates of false positives, which reduces its practical usefulness. Behavioral monitoring can capture dynamic changes in system activity, yet it requires significant computational resources and may slow down performance. Machine learning models offer adaptability and the ability to learn from data, but they demand large amounts of training information and careful tuning to avoid errors. Hybrid approaches attempt to combine the strengths of multiple techniques, but they often face difficulties in seamless integration and optimization within existing infrastructures. Because of these limitations, researchers are increasingly focused on developing frameworks that incorporate automatic updating mechanisms. Such frameworks are designed to be self-adaptive, meaning they can evolve continuously in response to new conditions without requiring manual intervention. Real-time adaptation is a central feature of these systems, enabling them to improve accuracy, reduce false positives, and optimize the use of computational resources. By integrating intelligent updating mechanisms, information infrastructures can achieve higher levels of stability and efficiency. This not only enhances the overall performance of computer systems but also ensures that they remain relevant and effective in environments where change is constant. The ability to evolve automatically, without relying on outdated static methods, positions these technologies as a cornerstone of future developments in information systems. The continuous evolution of computational environments demands solutions that are flexible, intelligent, and capable of real-time adaptation. By embracing adaptive frameworks, researchers and developers can create systems that are not only more accurate and efficient but also more resilient and scalable. This marks a decisive step toward the next generation of computer systems, where adaptability and automation are essential for long-term reliability and success.
  • Ескіз
    Документ
    Method for synthesis of a scalable architecture of a distributed computer systems, resistant to social engineering attacks
    (Хмельницький національний університет, 2025) Bokhonko, Oleksandr; Atamaniuk, Olha
    Social engineering continues to be one of the most dangerous classes of threats for modern distributed IT systems, where event processing, resource access, and protection mechanisms are performed on a large number of heterogeneous nodes. The growth of the scale of architectures, the emergence of multi-channel interaction scenarios, remote users, and a high level of dynamism create challenges for the synthesis of systems that are able to maintain resistance to social engineering attacks. The study proposes methods and tools for the synthesis of distributed systems focused on ensuring structural, behavioral, and functional resistance to such attacks. The basis of the approach is the use of a population multi-agent mean-field model, which allows considering a large number of nodes as a coordinated system of local detectors interacting through an aggregated state space. This makes it possible to describe the impact of attacks not on individual components, but on the entire distributed system as a whole, and to evaluate its response through integrated risk and resilience indicators. The study forms a generalized model of a distributed system, defines the roles of different types of nodes, protections and interaction channels, and also describes the methodology for architecture synthesis, which includes the classification of local actions, coordination mechanisms and evaluation criteria. Special attention is paid to the integration of protective measures - deception components, multifactor authentication, filtering and segmentation mechanisms - into the structure of a distributed system. Methods for optimizing the distribution of these measures at different levels of the architecture are proposed in accordance with the dynamics of the mean field and target requirements for stability. An iterative approach to architecture synthesis is developed, which combines the adaptation of local node strategies with the tuning of global system parameters. The results demonstrate that the use of the mean field concept allows to ensure scalability of solutions, consistency of node behavior, and also to increase the ability of a distributed system to counteract social engineering attacks in conditions of uncertainty and high variability of scenarios. The methodology can be used for the design, improvement and engineering synthesis of real distributed IT architectures operating in critical environments.