https://repository.hneu.edu.ua/handle/123456789/172 Thu, 02 Jul 2026 21:52:26 GMT 2026-07-02T21:52:26Z https://repository.hneu.edu.ua/handle/123456789/41287 Назва: Intelligent Guidance Algorithms for Autonomous Unmanned Interception Systems: Smart Information Processing and Decision-Making Автори: Milevskyi S.; Brynza N.; Serhiienko O.; Mashchenko M.; Chernova N.; Dydiak R. Короткий огляд (реферат): The paper addresses the problem of autonomous guidance for unmanned interceptor systems operating against highly maneuverable small aerial targets. A comparative analytical and numerical study of fundamental guidance laws Proportional Navigation (PN), Augmented Proportional Navigation (APN), Pursuit Navigation, and Linear Quadratic (LQ) optimal control - is conducted, with emphasis on their applicability under real physical constraints of small UAV platforms, including actuator inertia, limited available overload, and measurement noise. To overcome the limitations of classical methods, a combined adaptive guidance algorithm is proposed, integrating APN-based target acceleration compensation, Zero-Effort Miss (ZEM) trajectory prediction, and a nonlinear correction term justified through Lyapunov stability theory. The Lyapunov function approach, employing the squared line-of-sight angular rate, guarantees partial stability with respect to the guidance error variable across a wide range of initial conditions. A Kalman filter is incorporated into the guidance loop to provide reliable real-time estimates of target acceleration and time-to-go under high noise conditions. Three-degree-of-freedom numerical simulations confirm that the proposed algorithm achieves a miss distance of 0.1 m - a reduction of approximately 97 % compared to classical $\text{PN}(16.5$ m) and 97 % compared to APN (3.42 m) - while reducing average interception time by 12 % with only a moderate increase in computational cost. The results validate the effectiveness of combining nonlinear adaptive corrections with stochastic filtering for autonomous terminal guidance applications. Thu, 01 Jan 2026 00:00:00 GMT https://repository.hneu.edu.ua/handle/123456789/41287 2026-01-01T00:00:00Z https://repository.hneu.edu.ua/handle/123456789/41286 Назва: Hardware Security Evaluation of IoT Microcontrollers: Threats and Countermeasures Автори: Pohasii S.; Tkach M.; Holdobin S.; Brynza N.; Stetsenko V.; Tiutiunyk O. Короткий огляд (реферат): This study analyzes the susceptibility of IoT microcontrollers to reverse engineering, defined as unauthorized data access for replication, and proposes countermeasures. STM32F0 demonstrates superior resistance due to dense integration, rapid protection activation, and robust encryption, effectively countering attacks like Cold Boot Stepping, Glitch, Electromagnetic Analysis, and invasive methods. GD32 offers moderate security but is vulnerable to faster attacks. CH32 and MM32F0 show the least resilience due to weak protection. Architectural analysis highlights STM32F0's secure interfaces, while GD32's multi-chip design and CH32/MM32F0's simpler cores increase risks. Security metrics confirm STM32F0's lead, with GD32, CH32, and MM32F0 needing enhancements. Recommendations include hardware shielding, signal filters, and dynamic encryption, implementable in 2-14 days at $0.1-1.0 per device. STM32F0 suits high-security IoT, GD32 mid-level needs, and CH32/MM32F0 low-risk applications. Comprehensive hardware and software modifications are critical to mitigate reverse engineering threats across all microcontrollers. Wed, 01 Jan 2025 00:00:00 GMT https://repository.hneu.edu.ua/handle/123456789/41286 2025-01-01T00:00:00Z https://repository.hneu.edu.ua/handle/123456789/41285 Назва: Analysis of software vulnerability detection methods Автори: Gryzun L.; Havrylova A.; Tkachov A.; Hapon A.; Brynza N. Короткий огляд (реферат): The author proposes a direction of improvement of existing software protection systems, focusing efforts on increasing their ability to detect new types of malware. The most promising direction for the development of technologies for detecting vulnerabilities and vulnerabilities in software is an approach that combines different methods of analysis. This allows achieving higher accuracy of the results, as well as increasing the productivity of the tools used to check the code. A method of combining static analysis of programs and dynamic symbolic execution is proposed to improve the accuracy of vulnerability detection while maintaining high performance of analysis tools. This approach will significantly reduce the risk of errors that can be missed when using one of the analysis methods separately, and also improves the efficiency of the overall software security process. Wed, 01 Jan 2025 00:00:00 GMT https://repository.hneu.edu.ua/handle/123456789/41285 2025-01-01T00:00:00Z https://repository.hneu.edu.ua/handle/123456789/41284 Назва: Management of Smart Technology Systems in Conditions of Increased Requirements for Information Security Автори: Pecherytsia D.; Brynza N.; Tiutiunyk O.; Mashchenko M.; Tonieva K.; Tiutiunyk V. Короткий огляд (реферат): The modern stage of the information society is marked by the rapid adoption of smart technologies, which have become an essential part of many areas of human activity. Their use spans energy systems, transportation, healthcare, industry, agriculture, and urban infrastructure. The idea of "smart"brings together sensor technologies, automated data collection, artificial intelligence algorithms, the Internet of Things, and cyber-physical systems into a single, integrated environment. In this context, the ability to effectively manage these complex systems takes center stage, since without reliable methods of management, monitoring, and protection, the security and dependability of smart technologies remain at risk. Wed, 01 Jan 2025 00:00:00 GMT https://repository.hneu.edu.ua/handle/123456789/41284 2025-01-01T00:00:00Z