The implementation of job package execution processes in Flow Shop systems is affected by instrument failures and downtime during recovery. Maintaining a given level of reliability of the systems is ensured by periodic maintenance of their devices related to troubleshooting, which are the causes of failures. During the maintenance time intervals, the devices are unavailable for the implementation of their assigned functions. Device maintenance planning allows you to determine the time intervals between their implementations. However, the position of the instrument maintenance implementations may not be fixed, but may fall within time intervals of a given duration (time "windows"). In this case, the position of instrument maintenance implementations is optimized taking into account the nature of the processes of completing task packages in the systems. At the same time, tasks performed in systems that are included in packages may be part of orders for which specific deadlines have been set. As a result, the task of optimizing the composition of task packages, including packages in the availability intervals of devices of non-fixed duration, and the order of execution of packages in these intervals when tasks enter orders, with specific deadlines for them, is urgent. With small problem sizes, their solutions can be obtained by using mixed integer linear programming.

The purpose of the work is to form a new mathematical model of integer programming for optimizing solutions of this type. Methods of constructing mathematical programming models were used to achieve this goal. At the first stage, the formation of a nonlinear mathematical model was implemented. Expressions are obtained that are used to construct constraints corresponding to the distribution of all tasks into packages. In order to reduce the time spent on obtaining solutions, the model was linearized. To verify it, an application has been developed in the IBM ILOG CPLEX environment. In the course of the research, results were obtained that showed the effectiveness of the model for solving the tasks of planning the execution of task packages in Flow Shop systems with varying service frequency and the condition that tasks are included in orders with specific deadlines for their execution. The results are of practical importance in solving small-dimensional problems of constructing scenarios for completing task packages in production systems.

In the context of the rapid growth of scale and criticality of IoT infrastructures, the task of ensuring the fault tolerance of hybrid systems combining fog and cloud computing, capable of maintaining a given level of service in case of failures of nodes and communication channels, becomes especially urgent.

The purpose of the study is to develop and evaluate a mathematical model of fault tolerance for a distributed IoT environment, as well as to develop approaches to dynamic load balancing, taking into account reliability indicators, network delays and node load intensity.

The paper uses methods of continuous Markov chain theory, queuing theory, as well as an experiment on foggy nodes and a cloud server with varying network parameters and failure scenarios. In the course of the study, solutions were obtained for building an integral metric combining reliability, latency and load, as well as algorithmic rules for redistributing tasks between fog and cloud nodes, ensuring the stable functioning of the IoT network with partial infrastructure failures.

Experimental results have shown that using the proposed model for dynamic load balancing reduces the average system downtime by 32 % and reduces delays for critical tasks by 4–6 times compared with purely cloud architectures.

The scientific novelty of the work lies in the development of a mathematical model of fault tolerance of a hybrid IoT system based on continuous Markov circuits, which integrates node reliability, network delays and load factors into a single integrated stability metric, as well as a dynamic load balancing method in a cloud architecture using an integrated metric as a criterion. redistribute tasks between nodes to minimize downtime and delays in case of partial infrastructure failures.

The theoretical significance of the research lies in the development of a mathematical modeling apparatus for fault tolerance of distributed IoT systems and in the proposal of a formalized criterion for assessing their stability in dynamic operating conditions.

The practical significance lies in the possibility of using the developed model and integrated metrics when designing and configuring hybrid architectures of the Internet of Things, selecting redundancy parameters and load redistribution rules, as well as developing recommendations for optimizing data flows to improve the stability and performance of real telecommunications and industrial IoT platforms.

Actuality. The growth of real-time traffic places increased demands on the quality of service in networks. Traditional load balancing methods, focused only on network node loading, do not take packet loss, delay, and jitter into account. There is a need for advanced balancing algorithms for software-configurable networks capable of dynamically taking into account both the traffic load on network nodes and its quality characteristics.

Purpose: development, theoretical justification, and experimental testing of network traffic balancing techniques based on a combined quality parameter for software-configurable networks, ensuring compliance with client traffic service quality requirements.

Methods: theocritical analysis of SLA communication operators to identify key parameters of the quality of service to all networks, mathematical modeling and formalization of the normalization procedure of various quality parameters, traffic balancing method based on the modified Enhanced Weighted Round-Robin algorithm, with the use of developed software for simulating traffic balancing in SDN networks. The solution. Development

analysis of dynamic balancing techniques, with integrated calculation of the combined service quality parameter in networks. A mechanism for normalization of quality parameters is proposed.

Experimentally proven effectiveness: the algorithm ensured uniform distribution of the load and improved the combined indicator of the quality of service in networks by 46% compared to the method of load balancing only.

Novelty. For the first time, an original adaptive model of mathematical normalization of various Quality of Service parameters was developed. Specialized software was created for simulation modeling and visualization of the balancing method taking into account Quality of Service.

Practical significance. Development of a ready-made tool for modeling and testing infrastructure stability. Demonstration of practical implementation of intellectual balancing for implementation in SDN controllers.

This paper investigates the trade-off between energy efficiency and spectral efficiency in multi-cell massive MIMO systems. The relevance of the study is driven by the need to simultaneously increase throughput and reduce the energy consumption of base stations in next-generation wireless networks, given the growing number of antennas and served users.

The purpose of the study is to determine how the number of base-station antennas, the number of served users, linear signal processing schemes, and hardware implementation parameters affect the energy efficiency of multi-cell massive MIMO systems, and to identify configurations that provide the best trade-off between energy and spectral efficiency.

Methods. A mathematical model of a massive MIMO system is developed, taking into account the number of base station antennas M, the number of user equipment K, and various linear signal processing schemes, including MR, ZF, RZF, S-MMSE, and M-MMSE. The model incorporates power consumption parameters reflecting hardware implementation characteristics, represented by two different sets of component specifications. The evaluation of energy and spectral efficiency is carried out using simulation-based analysis for various system configurations.

Results. The results show that an optimal antenna-to-user ratio of M/K ≈ 3–4 achieves maximum energy efficiency without a significant reduction in spectral efficiency. It is demonstrated that the M-MMSE and S-MMSE algorithms provide the highest energy efficiency performance with moderate computational complexity, particularly when improved hardware components are employed. The obtained results confirm the existence of a pronounced energy efficiency optimum as the number of base station antennas increases.

The novelty of this work lies in the comprehensive analysis of the energy–spectral efficiency trade-off in multi-cell massive MIMO systems while jointly accounting for linear signal processing schemes and hardware implementation parameters, which enables the formulation of practical recommendations for base station configuration under technological constraints.

Practical significance. The findings of this study can be applied to the design and optimization of energy-efficient multi-cell massive MIMO systems for next-generation wireless communication networks, taking into account hardware implementation constraints and quality-of-service requirements.

Digital-to-analog converters are widely and effectively used in radio electronic equipment for various purposes when it is necessary to convert a digital control code into an analog parameter ‒ current or voltage. They are also used in digital-to-analog frequency synthesizers to obtain the desired envelope shape of the synthesized signal, and are widely used in video and audio interfaces, digital oscilloscopes, and precision signal sources. Currently, the main problems in the construction of precision and (or) high-speed digital-to-analog converters are technological limitations of production, namely, the final accuracy of the implementation of analog elements, primarily analog voltage (current) switches and R-2R matrix resistors. Therefore, the structural method of overcoming existing technological problems is relevant.

The purpose of this work is to theoretically substantiate a new approach to the ideology of digital-to-analog conversion and to build structures of digital-to-analog converters with increased accuracy and (or) high performance.

The solution to the problem is to increase the number of reference signals at the inputs of partial digital-to-analog converters, regardless of their internal structure, but while unconditionally ensuring a strict fractional-multiple
(vernier) ratio of the values of the reference signals. At the same time, the vernier reference signals must be coupled at only two points – at the beginning and at the end of the scale, and at direct current. The accuracy of the vernier scale coupling (the accuracy of the reference signal coupling) must correspond to the final accuracy of the digital-to-analog conversion.

The novelty and originality of the proposed method lies in the significant expansion of the range of conversion of the digital control code into an analog parameter, i. e. in obtaining a new quality of digital-to-analog conversion.

The possibility of practical implementation of the new digital-to-analog converter structure is confirmed by theoretical calculations and circuit modeling using the Microcap12 package, which demonstrated the correctness of the proposed method. The proposed solution makes it possible to circumvent the technological limitations of production on the potentially achievable conversion accuracy in the manufacture of digital-to-analog converters chips and provides qualitatively new possibilities for digital-to-analog conversion technology.

The relevance of this work is driven by the widespread adoption of IEEE 802.11 networks. This article examines the key innovations associated with the transition from the seventh-generation IEEE 802.11be Wi-Fi standard, known as Wi-Fi 7, to the eighth-generation IEEE 802.11bn standard, known as Wi-Fi 8. Specifically, attention is paid to the  throughput improvements, the implementation of more advanced modulation methods, and support for multi-channel transmissions, which significantly improves the speed and stability of connections. The eighth-generation standard is expected to introduce fundamentally new approaches to organizing client-access point interactions. Unlike 802.11be, 802.11bn is expected to enable simultaneous operation of a single client with multiple access points and vice versa. This will fundamentally change most of existing radio environment management mechanisms, but at the same time, it will provide previously unreachable mobility within a distributed wireless infrastructure. Current challenges facing the industry are also considered, including compatibility with previous generations of standards and the complexities that arise during wireless local area networks design.

Purpose: A systematic review of the key features of the latest addition to the IEEE 802.11be standard, known as Wi-Fi 7, and the next generation of IEEE 802.11bn, known as Wi-Fi 8, currently under development,

Methods: Analysis of professional and scientific literature, standards texts, and industry practices.

Results. In addressing the stated objective, this paper presents an overview of the key improvements expected in the IEEE 802.11bn standard. Particular attention is paid to changing approaches to WLAN design in the context of new technologies such as Distributed Multi-Link Operation and Multi-AP Coordination.

Theoretical significance lies in the analysis of the key aspects of the innovations in IEEE 802.11be and IEEE 802.11bn. Research problems requiring solutions that require the revision and sophistication of existing models and methods are identified. These include: dynamic resource allocation in multi-band networks, optimization of spectrum resource use, development of radio resource management algorithms, and development of handover algorithms.

Practical significance: The obtained results can be used when formulating design problems for seventh- and eighth-generation Wi-Fi networks.

Relevance. In modern Industrial Ethernet networks, anomaly and cyberattack detection requires taking the protocol semantics of network traffic into account. The relevance of this study is determined by the growing number of attacks on industrial control systems, the increasing integration of production and corporate networks, and the widespread use of industrial protocols, some of which were not originally designed to meet modern cybersecurity requirements. Traditional signature-based detection tools are limited in their ability to identify previously unknown and stealthy attacks, while approaches based solely on statistical flow characteristics often lose important information about communication logic, message roles, and application-layer features of industrial protocols. An additional challenge is the shortage of labeled data typical of real industrial environments, which complicates the training of robust attack detection models.

Objective. To develop and evaluate a method for representing industrial traffic as tokenized sequences suitable for transformer models in small Industrial Ethernet networks.

Methods. Flow- and session-level packet aggregation, semantic tokenization of Modbus/TCP and OPC UA protocol fields, embeddings with positional encoding, self-supervised pre-training, and subsequent fine-tuning of the model for session classification were used. The approach was evaluated on the Malware in Smart Factory dataset.

Results. A protocol-aware representation of network sessions that preserves communication context and the specific features of industrial protocols was developed. On the Malware in Smart Factory dataset, the transformer model achieved an F1-score of 99.3 % in attack detection and outperformed LSTM and CNN models; pre-training further improved classification performance.

Theoretical and practical significance. The proposed approach provides a unified input format for analyzing Modbus/TCP and OPC UA traffic and can be used in anomaly and intrusion detection systems for small industrial networks.

Precision time protocol is widely common in different areas that include critically significant industries such as power generation, manufacturing, economics and communication where high accuracy is the necessary provision for its working. Time offset can causes delays in data receiving that potentially leads to faults. Precision time protocol is a quite attractive for malicious user to interfere in equipment functioning because of its accessibility. Therefore, preventable detecting of such actions is one of the actual goals for defending from faults.

The objective is to research possible attack strategies on the elements of the synchronization system operating according to the best time transmitter clock algorithm, and to form simulation models of the functioning of the elements of the synchronization system under the influence of attacks based on the formed attack strategies.

Methods include collection, systematization and analysis of scientific and technical information, simulation using an agent-based approach.

Results. A complex of simulation models of the functioning of synchronization system elements operating according to the best time transmitter clock algorithm under the influence of information attacks are developed.

The theoretical significance is that a complex of developed models makes it possible to monitor the port status of precision time protocol’s devices to determine the convergence time of the best time transmitter clock algorithm, and generally, to monitor the functioning of synchronization system elements based on the attributes of device ports, characteristics of communication channels, and various attack strategies.

The practical significance is that the obtained results can be used for real communication networks to identify anomaly synchronization system devices in the presence of information about the characteristics of communication nodes. When we have the statistics on the estimation of the convergence time of the best time transmitter clock algorithm and information on the status of device ports, it is possible to draw a conclusion about the objects for attacking and to take steps for eliminating destructive influences.

Statement of the problem. The advancement of modern technologies has enabled adversaries to employ intelligent and sophisticated tools during the execution of multi-stage attacks to conceal their activities within the network infrastructure. Countering such attacks constitutes one of the primary objectives of information security monitoring for data transmission networks. Given this persistent challenge, there is a continuous demand for the development of novel countermeasures or the optimization of existing anomaly detection systems. These systems must not only facilitate more efficient acquisition of relevant information but also leverage this information to enhance the prediction of potential cyberattacks.

Purpose: to determine the relationship between the probabilistic and temporal characteristics of the network security monitoring process.

Results. А network security monitoring tool structure is proposed that utilizes streaming two-layer recurrent neural networks with controlled synapses to classify and predict multi-stage attacks. The network security monitoring process for a data transmission network was modeled to determine the impact of various factors. Software was developed to calculate the probabilistic and temporal characteristics of the network security monitoring process for a data transmission network under attacker attack.

Theoretical significance. The model and software used allow for the formulation of requirements for various subprocesses of network security monitoring for a data transmission network.

Practical Significance: The proposed model can serve as a foundational framework for the development of systems designed to prevent multi-stage attacks.

Relevance. Representative network-traffic datasets are required for research and testing of attack detection tools; however, their collection and annotation are labor-intensive, and data sharing is constrained by confidentiality requirements and the risk of leakage. Synthetic data can increase sample sizes and enable modeling of rare and zero-day scenarios while preserving the statistical properties of network traffic.

Objective. To improve the quality and reproducibility of generating synthetic tabular features of network traffic, using Android applications as a case study, by applying the Tabular Denoising Diffusion model (TabDDPM) and performing comprehensive validation of the generated data using a consistent set of metrics.

Methods. We employ the TabDDPM diffusion-based generative model, which is applicable to arbitrary tabular datasets. Generation performance is assessed via statistical analysis methods, including comparisons of feature distributions and inter-feature dependencies, evaluation of utility in a downstream task, and estimation of the discrepancy between synthetic and real data.

Results. A comprehensive quality assessment of TabDDPM is conducted for generating tabular features of network traffic associated with attacks or unwanted applications. The results demonstrate the feasibility of producing controlled synthetic datasets that preserve characteristic traffic patterns and enable scaling of training samples without directly copying the original records.

Novelty. We propose a unified post-generation validation protocol for synthetic traffic that integrates realism, utility, and indistinguishability metrics, thereby reducing the risk of misleading conclusions arising from fragmented evaluation. In addition, an integral quality indicator is introduced to quantify generation performance by aggregating partial metrics.

The theoretical significance lies in advancing a methodological framework for verifying tabular diffusion models in cybersecurity applications.

The practical significance is the ability to use the resulting synthetic datasets to model cyberattacks and zero-day scenarios, perform stress testing, and train and / or evaluate intrusion detection systems.