Cyberpolygon is a platform designed to train students in the field of cybersecurity in order to obtain professional skills. In this regard, Astana IT University created a project aiming development of a cyberpolygon with various vectors for the exploitation of cyberattacks. This project allows more than ten students to work on the platform at the same time and gain Red/Blue teaming skills. This article provides a methodology of the development of the Cyberpolygon platform. Further, through the Cyberpolygon, experimental training was conducted among the students of the second year of study at Astana IT University, which allows solving real problems through the cyberpolygon. The performance testing and study of the platform was carried out by the methods of comparative analysis.

Handover management is a fundamental process in modern mobile networks, ensuring service continuity under user mobility. However, the relationship between network conditions and handover behavior remains insufficiently understood under real-world measurement conditions. This study presents a data-driven analysis of handover behavior based on drive-test measurements collected in an urban environment. A formal definition of handover events is proposed and implemented for automatic detection using changes in the serving cell identifier. The dataset is further analyzed to assess the influence of radio signal indicators, QoS metrics, and mobility-related variables on handover occurrence. Logistic Regression is used as an interpretable baseline, while Random Forest is applied to capture nonlinear feature interactions. The results show that individual QoS indicators demonstrate limited direct explanatory capability when considered independently. Random Forest achieved higher predictive performance than Logistic Regression, with AUC = 0.902 compared to 0.787, indicating the importance of nonlinear relationships in handover behavior. Degradation events are additionally identified using a threshold-based proxy, showing that latency is a more sensitive indicator of degraded conditions than throughput. Overall, the findings suggest that handover behavior depends on multiple interacting network conditions rather than a single dominant predictor, highlighting the importance of QoS-aware and data-driven mobility analysis in 5G networks and beyond.

The rapid development of smart cities requires reliable and resilient connectivity across heterogeneous communication environments, while no single access technology can simultaneously satisfy requirements for coverage, mobility, latency, capacity, and service continuity. This review investigates the integration of satellite, cellular, and Wi-Fi networks and examines the role of artificial intelligence (AI) in enabling coordinated multi-access connectivity. A PRISMA-guided systematic literature search and study-selection procedure was applied to peer-reviewed studies published between 2016 and 2026, resulting in 36 peer-reviewed publications included in the qualitative synthesis. The selected studies were analyzed across eight thematic areas, including satellite–terrestrial integration, cellular–Wi-Fi convergence, vertical handover, AI/DRL-based mobility management, integrated terrestrial–non-terrestrial networks, resilience, smart city communication infrastructure, and multi-connectivity. The analysis shows that satellite–cellular integration, terrestrial Multi-RAT coordination, AI-based handover, and multi-connectivity have been extensively investigated as individual research directions; however, their convergence within a unified satellite–cellular–Wi-Fi framework remains limited. Existing solutions are also predominantly reactive and rarely combine predictive network management, service-aware decision-making, and resilience objectives. The review therefore identifies predictive AI-driven multi-access orchestration integrating satellite, cellular, and Wi-Fi connectivity as a promising direction for resilient smart city communication infrastructures.

This article is devoted to the study of methods for analyzing the cryptographic stability of encryption algorithms. The rapid development of encryption systems, as well as the concomitant development of their hacking, leads to the emergence of new systems with high cryptographic strength. During the work, popular encryption methods were studied according to various parameters (performance, memory size, resistance to damage, etc.), based on the results of the study, the DES and AES algorithms were selected, and its algorithm was modeled using the Cryptool 2 software product, as well as attack by various methods. Based on the results of the study, software for an encryption algorithm was developed using the object-oriented programming language C#.

Quantitative assessment of information security in 5G telecommunications infrastructure is difficult because individual security metrics are heterogeneous and rarely interpreted together. Conventional multi-criteria decision-making (MCDM) applications rank alternatives by compensatory aggregation, so a violated metric can be offset by others. This study proposes a standard-traceable method in which MCDM only weights the metrics, while measured values are evaluated against tolerable values by a non-compensatory exceedance indicator and a three-level status rule. Twenty-four metrics were derived from ISO/IEC 27011:2024 controls through the ISO/IEC 27004 measurement chain and grouped into three domains. The analytic hierarchy process (AHP) weighted three quality criteria of a metric (significance 62.6%, correctness 28.9%, measurability 8.5%), and the weighted aggregated sum product assessment (WASPAS) method used them to derive the metric weights, cross-checked by the fuzzy technique for order of preference by similarity to ideal solution (Fuzzy TOPSIS). Validation combined synthetic deterioration scenarios, Monte Carlo analysis and comparison with alternative MCDM methods. In three 12-month scenarios, the exceedance indicator flagged each violation in the month it occurred, whereas compensatory aggregation lagged by three to six months or did not react. The domain status remained stable in 85.3–100% of Monte Carlo draws under perturbed tolerable values and measurements. Compared with conventional MCDM-based aggregation, the advantage lies in traceable metric selection, normalization against tolerable values and the non-compensatory status rule.