Search

This study investigates the processes and means of requirements engineering (RE) of software and complex information systems (SWS). The subject of this study is the instrumental means of SWS RE based on the methods of artificial (computational) intelligence (AI). The goal is to improve the accuracy and efficiency of CCD requirements development processes using AI tools by providing better communications between business teams and technical teams and automating complex requirements collection and documentation processes. The tasks are as follows: to analyze the principles and means of integrating AI tools into requirements engineering processes, the development and verification of which is a critical stage in the development of SWS; to determine the problems of using traditional SWS RE methods and perform their comparative analysis with AI-based methods; and to develop a chatbot architecture for requirements validation and identify characteristics of RE processes that are improved through its use. The results. The results of this study demonstrate that AI tools can significantly improve the accuracy, timeliness, and efficiency of requirements development processes and project team communication. An architecture and software solution for an intelligent chatbot for requirements validation is proposed, and the benefits and limitations of its application are discussed. Directions for further development and research regarding the end-to-end implementation of AI tools in IP processes have been developed.

Предметом дослідження в статті є система комунікаційних мереж БпЛА (літальних мереж, ЛМ), що використовують технологію LiFi для передачі інформації від джерела до приймача в умовах фізичних перешкод і кіберзагроз, а також стратегії розгортання та забезпечення надійності (СРЗН) ЛМ. Мета роботи – розроблення критеріїв і алгоритмів вибору СРЗН ЛМ, що забезпечують необхідний рівень надійності та ефективності за умови заданих обмежень. У статті розв’язуються конкретні завдання: систематизація стратегій розгортання та забезпечення надійності літальної мережі; формулювання принципів і розроблення алгоритму вибору оптимальної стратегії розгортання та забезпечення надійності ЛМ; надання рекомендацій щодо вибору оптимальних стратегій розгортання та забезпечення надійності літальної мережі. Упроваджено такі методи: системний аналіз для вибору оптимальної стратегії розгортання та забезпечення надійності; теорія надійності та ефективності системи. Досягнуті результати: розширено класифікатор стратегій розгортання ЛМ завдяки додатковим ознакам ремонту й обслуговування, а також наявності кібератак; сформульовано критерії вибору стратегій розгортання та забезпечення надійності ЛМ; розроблено алгоритм вибору оптимальної стратегії розгортання та забезпечення надійності ЛМ; проаналізовано та наведено приклад застосування розроблених алгоритмів для ілюстрації покрокової процедури вибору стратегії, що супроводжується розрахунками показників надійності. Висновки: запропоновані множини, критерії та алгоритм вибору стратегій розгортання та забезпечення надійності комунікаційних мереж БпЛА дають змогу обґрунтувати сукупність параметрів і спланувати реалізацію оптимальної (за визначеним критерієм) політики впровадження автоматичної системи підтримки комунікацій на об’єктах критичної інфраструктури в умовах руйнувань та кібервпливів, а також підвищити ефективність (мінімізувати вартість) застосування літальних мереж.

To 90 anniversary selebration of Professor Michael Ystrebenetsky

Traditional availability, reliability, and safety models face the dimension problem due to a huge number of components in modern systems, motivating further research in this field. This paper focuses on multi-fragmental and multiphase models for availability and functional safety assessment of the information and control (I&C) systems with two-cascade redundancy considering design faults manifestation during operation. The methodology of the research is based on Markov and semi-Markov chains with the utilization of multi-phase modeling. Several multi-phase models are developed and investigated considering different conditions of operation and failures caused by version faults. The case study of the research is based on the analysis of safety-critical nuclear power plant I&C systems such as the reactor trip systems developed using the programmable platform RadICS.

The subject of this study is unmanned aerial vehicle (UAV)-based wireless networks in an obstacle-ridden environment. The aim of this study is to develop methods and software to ensure reliable LiFi communication using swarm UAVs in an obstacle-ridden environment. The objectives are as follows: 1) to describe the problem of providing a reliable UAV swarm-based LiFi network, requirements for the composition and use of UAVs, and assumptions; 2) to develop the methodology for solving research tasks; 3) to develop the method and algorithms for solving the problem, considering the requirements, assumptions, and practical limitations; 4) to explore the algorithms by developing software for modeling and searching for rational UAV placement to ensure the required UAV-based LiFi network characteristics; 5) to provide experiments and illustrative examples of the developed tool’s application. The following results were obtained. 1) The requirements for the composition and use of UAVs for creating LiFi networks, as well as assumptions and limitations for the methodology development and research task solving. 2) An obstacle avoidance method using the left and right angles algorithm. 3) A method for obstacle avoidance using the controlled waterfall algorithm. 4) A software tool for modeling and searching for rational UAV placement to ensure the required LiFi network characteristics. The tool allows route construction under obstacles in 2D space and a comparison of the developed algorithms for various variants of obstacle placement. Conclusions. The main contribution of this research is a set of methods, algorithms, and software tools for providing communications between two points using LiFi technologies and a swarm of UAVs supporting these communications as transmitters in conditions of mechanical obstacles.

The object of this study is a programmable logic controller (safety PLC), which is part of an information and control system designed for safe management of important technological processes. The subject of this study is the substantiation of the legality of reusing the results obtained during the development of the safety PLC in accordance with the requirements of functional safety to assess the level of its cyber security. The purpose of this work is to investigate the possibility of "cross" evaluation of the safety characteristics of the safety PLC, namely, the possibility of evaluating the level of cyber security of the programmable logic controller based on known data regarding its level of functional safety, in order to optimize the use of available resources in the project. The study tasks are following: to provide a theoretical basis for the relationship between safety PLC characteristics such as functional safety and cybersecurity. Determine the metrics by which it will be possible to assess the degree of reuse of existing results. Perform an analysis of potential cyberattacks depending on the architecture of the information and control system, which performs security functions, as well as on the possible modes of its use. Determine and evaluate the degree of "cross" influence of critical characteristics of the research object. To perform a calculation analysis of the potential financial and time gain from the reuse of already known results for the minimum configuration of the safety PLC. Conclusions. The study demonstrated the relevance of the question of assessing the cybersecurity of a programmable logic controller based on the use of existing data, regarding its level of functional security (SIL). The proposed approach provides opportunities to significantly optimize the use of resources in safety PLC certification projects. However, the main methodological conclusion is that the well-known principle of Security Informed Safety can be developed and used in practice in the opposite direction, as Security supported/assessed by Safety. That is, to the principle of "assessment of functional safety taking into account/on information (cyber) security" the principle of "assessment of information (cyber) security with the support of/taking into account the results of the assessment of functional security" is added.

The evolution of software quality models (SQMs) reflects the dynamic of the development of information technologies and systems. For new specialised information technologies, it is necessary to develop appropriate software quality models. If there are no standardised models for these technologies then IT companies or individual researchers develop corporate SQMs. Standardised and corporate quality models for the same information technologies often have significant differences that are associated with discrepancies in the number of characteristics and their semantic content. The objective of the paper is to develop a unified approach to the formation of technology-based software quality models (TBSQMs). The research method is based on the principle of evolutionary analysis of SQMs. This principle consists of identifying and comparing key models that record the stages and determine the content of the evolution process, as well as a unified approach to the development of SQMs and metric assessment of their complexity. The paper presents the results of a comparison of corporate and standardised quality models for artificial intelligence and identifies the differences between them. The obtained results have allowed us to establish certain regularities for the current stage of the evolution of software quality models and to develop a classification of such models. A unified approach to developing TBSQMs was developed and an example of its application for blockchain technology-based software quality model was presented. In the future, it is planned to analyse TBSQMs for other information technologies and develop a tool to support the process of forming such models.

Abstract Security Information and Event Management (SIEM) technologies play an important role in the architecture of modern cyber protection tools. One of the main scenarios for the use of SIEM is the detection of attacks on protected information infrastructure. Consorting that ISO 27001, NIST SP 800-61, and NIST SP 800-83 standards objectively do not keep up with the evolution of cyber threats, research aimed at forecasting the development of cyber epidemics is relevant. The article proposes a stochastic concept of describing variable small data on the Shannon entropy basis. The core of the concept is the description of small data by linear differential equations with stochastic characteristic parameters. The practical value of the proposed concept is embodied in the method of forecasting the development of a cyber epidemic at an early stage (in conditions of a lack of empirical information). In the context of the research object, the stochastic characteristic parameters of the model are the generation rate, the death rate, and the independent coefficient of variability of the measurement of the initial parameter of the research object. Analytical expressions for estimating the probability distribution densities of these characteristic parameters are proposed. It is assumed that these stochastic parameters of the model are imposed on the intervals, which allows for manipulation of the nature and type of the corresponding functions of the probability distribution densities. The task of finding optimal functions of the probability distribution densities of the characteristic parameters of the model with maximum entropy is formulated. The proposed method allows for generating sets of trajectories of values of characteristic parameters with optimal functions of the probability distribution densities. The example demonstrates both the flexibility and reliability of the proposed concept and method in comparison with the concepts of forecasting numerical series implemented in the base of Matlab functions.

The subject of this study is a method for analyzing threats and vulnerabilities and selecting countermeasures to ensure cybersecurity in augmented reality (AR) applications. The goal of this study is to increase the completeness of cybersecurity assessment of AR applications by using a formalized procedure for identifying and analyzing the risks of common threats, vulnerabilities, and types of attacks. This study is based on the well-known IMESA method (analysis of types, consequences and criticality of interventions), which structures the procedure for analyzing and minimizing risks by introducing appropriate countermeasures to ensure acceptable cybersecurity risks. Objectives: to substantiate the set of major cybersecurity threats specific to AR applications; to identify and describe download vulnerabilities in AR systems; to provide a detailed classification of various cyberattacks aimed at AR platforms, considering the results of a study of recent incidents; to use the IMECA method to describe and analyze cybersecurity issues in a structured manner and to propose reliable countermeasures. According to the tasks, the following results were obtained: 1) a classification of threats with a detailed description of how each of them can affect AR applications, namely threats such as software interference, unauthorized access, and malicious hardware embedding; 2) a critical analysis of weaknesses in AR systems, in particular, insecure data storage and insufficient authentication before using sensors, which provides an understanding of possible attack vectors; 3) a detailed description of various attack methodologies, including AR phishing, AR malware, and man-in-the-middle attacks, each illustrated with real-world examples or hypothetical scenarios. A systematic approach using the IMECA framework was used to identify, assess, and ensure the cybersecurity of AR applications using a set of proposed countermeasures. Conclusions. AR technology, despite its revolutionary nature and great potential, poses a unique set of cybersecurity challenges. These challenges are related to the immersive nature of the technology, dependence on real-time data, and integration with the physical world. The study emphasizes that an understanding of the threat landscape, combined with an IMESA-structured approach to risk management, is crucial for the secure development of AR applications. Developers, users, and managers responsible for security policies need to be proactive, innovative, and aligned in their approach to cybersecurity in AR systems.

The subject of this study is to ensure the cybersecurity of systems of multifunctional UAV fleets (SMF UAV). The purpose of this study is to identify and analyze the risks associated with the cybersecurity of multi-functional UAV fleets, develop models of threats, vulnerabilities, and attacks, and conduct IMECA analysis of cyber-attacks. Tasks: 1) analyze threats that may affect the security of multifunctional UAV fleets; 2) identify system vulnerabilities and their possible consequences in case of exploitation; 3) develop models of the system infrastructure and threats, vulnerabilities, and attacks, considering the specifics of the functionality and communication between system elements; 4) perform a risk-based analysis, identifying and classifying potential threats and their impact. The following results were obtained. The following results were obtained. 1. Cybersecurity threats to multifunctional UAV fleets are described and classified. 2. Identified and analyzed system vulnerabilities and their potential consequences. 3. Developed models of threats, vulnerabilities, and cyberattacks, considering the specifics of the UAV fleet. 4. Conducted a risk-based analysis, determined the level of threat, and developed recommendations for improving the cybersecurity of the UAV fleet based on the results of the IMECA analysis. Conclusions. The research emphasizes the importance of the developed model and tool for the detection and analysis of cyber threats to the SMF UAV. This allows increasing the cybersecurity and reliability of the system and ensuring timely response to cyber threats. Areas for further research: development of a model and method to consider the specifics of cyber threats and the technological characteristics of the SMF infrastructure; development and implementation of proactive protection tools in the context of combined cyber-attacks; and expansion of the scope of these tools in various industries, including smart cities.

Artificial intelligence technologies are becoming increasingly prevalent in resource-constrained, safety-critical embedded systems. Numerous methods exist to enhance the resilience of AI systems against disruptive influences. However, when resources are limited, ensuring cost-effective resilience becomes crucial. A promising approach for reducing the resource consumption of AI systems during test-time involves applying the concepts and methods of dynamic neural networks. Nevertheless, the resilience of dynamic neural networks against various disturbances remains underexplored. This paper proposes a model architecture and training method that integrate dynamic neural networks with a focus on resilience. Compared to conventional training methods, the proposed approach yields a 24% increase in the resilience of convolutional networks and a 19.7% increase in the resilience of visual transformers under fault injections. Additionally, it results in a 16.9% increase in the resilience of convolutional network ResNet-110 and a 21.6% increase in the resilience of visual transformer DeiT-S under adversarial attacks, while saving more than 30% of computational resources. Meta-training the neural network model improves resilience to task changes by an average of 22%, while achieving the same level of resource savings.

The analysis of utilising unmanned aerial vehicles (UAVs) to form flying networks in obstacle conditions and various algorithms for obstacle avoidance is conducted. A planning scheme for deploying a flying LiFi network based on UAVs in a production facility with obstacles is developed and described. Such networks are necessary to ensure reliable data transmission from sensors or other sources of information located in dangerous or hard-to-reach places to the crisis centre. Based on the planning scheme, the following stages are described: (1) laying the LiFi signal propagation route in conditions of interference, (2) placement of the UAV at the specified points of the laid route for the deployment of the LiFi network, and (3) ensuring the reliability of the deployed LiFi network. Strategies for deploying UAVs from a stationary depot to form a flying LiFi network in a room with obstacles are considered, namely the strategy of the first point for the route, the strategy of radial movement, and the strategy of the middle point for the route. Methods for ensuring the uninterrupted functioning of the flying LiFi network with the required level of reliability within a given time are developed and discussed. To implement the planning stages for deploying the UAV flying LiFi network in a production facility with obstacles, the “Simulation Way” and “Reliability Level” software tools are developed and described. Examples of utilising the proposed software tools are given.

Replacement policies have an important role in the functioning of the cache memory of processor cores. The implementation of a successful policy allows us to increase the performance of the processor core and the computer system as a whole. Replacement policies are most often evaluated by the percentage of cache hits during the cycles of the processor bus when accessing the cache memory. The policies that focus on replacing the Least Recently Used (LRU) or Least Frequently Used (LFU) elements, whether instructions or data, are relevant for use. It should be noted that in the paging cache buffer, the above replacement policies can also be used to replace address information. The pseudo LRU (PLRU) policy introduces replacing based on approximate information about the age of the elements in the cache memory. The hardware implementation of any replacement policy algorithm is the circuit. This hardware part of the processor core has certain characteristics: the latency of the search process for a candidate element for replacement, the gate complexity, and the reliability. The characteristics of the PLRUt and PLRUm replacement policies are synthesized and investigated. Both are the varieties of the PLRU replacement policy, which is close to the LRU policy in terms of the percentage of cache hits. In the current study, the hardware implementation of these policies is evaluated, and the possibility of adaptation to each of the policies in the processor core according to a selected priority characteristic is analyzed. The dependency of the rise in the delay and gate complexity in the case of an increase in the associativity of the cache memory is shown. The advantage of the hardware implementation of the PLRUt algorithm in comparison with the PLRUm algorithm for higher values of associativity is shown.

Предметом дослідження є поглиблений семантичний аналіз даних, оснований на модифікації методологій латентного розподілу Діріхле (LDA) та інтеграції її двоспрямованого кодувального подання з трансформаторів (BERT). Актуальність роботи. Прихований розподіл Діріхле є фундаментальною технікою моделювання тем, яку широко застосовують у різноманітних програмах для аналізу текстів. Хоча його корисність загальновизнана, традиційні моделі LDA часто стикаються з обмеженнями, зокрема жорстким розподілом тем і неадекватним відтворенням нюансів семантики, властивих природній мові. Мета дослідження – покращення адекватності та точності семантичного аналізу завдяки вдосконаленню базового механізму LDA, що інтегрує адаптивні пріоритети Діріхле та використовує глибокі семантичні можливості вбудовувань BERT. Упроваджені методи: відбір текстових наборів даних; попереднє оброблення даних; удосконалення алгоритму LDA; інтеграція з BERT Embeddings; порівняльний аналіз. Завдання дослідження: 1) теоретичне обґрунтування модифікації LDA; 2) реалізація інтеграції з BERT; 3) оцінювання ефективності методу; 4) порівняльний аналіз; 5) розроблення архітектурного рішення. Результати полягають у тому, що насамперед окреслено теоретичні основи як стандартної, так і модифікованої моделей LDA, а також детально викладено їх розширену формулу. За допомогою серії експериментів на текстових наборах даних, що визначаються різними емоційними станами, визначено ключові переваги запропонованого підходу. На підставі порівняльного аналізу за такими показниками, як внутрішньо та міжкластерна відстані та силуетний коефіцієнт, доведено підвищену когерентність, інтерпретованість і адаптивність модифікованої моделі LDA. Запропоновано архітектурне рішення для реалізації методу. Висновки. Емпіричні результати свідчать про значне покращення виявлення тонких складностей і тематичних структур у текстовій інформації, що є кроком в еволюційному розвитку методологій тематичного моделювання. Крім того, результати досліджень не лише створюють можливості застосування LDA для більш складних лінгвістичних сценаріїв, але й окреслюють шляхи їх подальшого вдосконалення для неконтрольованого аналізу текстів.

BackgroundThe healthcare sector demands a higher degree of responsibility, trustworthiness, and accountability when implementing Artificial Intelligence (AI) systems. Machine learning operations (MLOps) for AI-based medical diagnostic systems are primarily focused on aspects such as data quality and confidentiality, bias reduction, model deployment, performance monitoring, and continuous improvement. However, so far, MLOps techniques do not take into account the need to provide resilience to disturbances such as adversarial attacks, including fault injections, and drift, including out-of-distribution. This article is concerned with the MLOps methodology that incorporates the steps necessary to increase the resilience of an AI-based medical diagnostic system against various kinds of disruptive influences.MethodsPost-hoc resilience optimization, post-hoc predictive uncertainty calibration, uncertainty monitoring, and graceful degradation are incorporated as additional stages in MLOps. To optimize the resilience of the AI based medical diagnostic system, additional components in the form of adapters and meta-adapters are utilized. These components are fine-tuned during meta-training based on the results of adaptation to synthetic disturbances. Furthermore, an additional model is introduced for post-hoc calibration of predictive uncertainty. This model is trained using both in-distribution and out-of-distribution data to refine predictive confidence during the inference mode.ResultsThe structure of resilience-aware MLOps for medical diagnostic systems has been proposed. Experimentally confirmed increase of robustness and speed of adaptation for medical image recognition system during several intervals of the system’s life cycle due to the use of resilience optimization and uncertainty calibration stages. The experiments were performed on the DermaMNIST dataset, BloodMNIST and PathMNIST. ResNet-18 as a representative of convolutional networks and MedViT-T as a representative of visual transformers are considered. It is worth noting that transformers exhibited lower resilience than convolutional networks, although this observation may be attributed to potential imperfections in the architecture of adapters and meta-adapters.СonclusionThe main novelty of the suggested resilience-aware MLOps methodology and structure lie in the separating possibilities and activities on creating a basic model for normal operating conditions and ensuring its resilience and trustworthiness. This is significant for the medical applications as the developer of the basic model should devote more time to comprehending medical field and the diagnostic task at hand, rather than specializing in system resilience. Resilience optimization increases robustness to disturbances and speed of adaptation. Calibrated confidences ensure the recognition of a portion of unabsorbed disturbances to mitigate their impact, thereby enhancing trustworthiness.

To 70 anniversary selebration of Professor S. I. Dotsenko

The subject of this study is systems for detection and identification (D&I) of explosive ordnance (EO). The aim of this study is to develop a concept, general structure, and models of a robotic-biological system for D&I of EO (RBS-D&I). The objectives are as follows: 1) to classify mobile systems for D&I of EO and suggest a concept of RBS-D&I; 2) to develop the general structure of RBS-D&I consisting of robotic (flying and ground) and biological subsystems; 3) to develop models of RBS-D&I including automaton, hierarchical, and operational ones; 4) to describe tasks and planned results of the article-related scientific project; and 5) to discuss research results. The following results were obtained. 1) The general structure of the RBS-D&I. The structure comprises the following levels: control and processing centres (mobile ground control and processing centre (MGCPC) and virtual control and processing centre); forces for detection and identification (fleet of unmanned aerial vehicles (FoU), biological detection information subsystem (BDIS), and robotic detection information subsystem (RDIS)); interference; natural covers and a bedding surface; and target objects (all munitions containing explosives, nuclear fission or fusion materials and biological and chemical agents). 2) A concept of RBS-D&I. The concept is based on RBS-D&I description, analysis, development, and operation as an integrated complex cyber-physical and cyber-biological system running in changing physical and information environments. 3) The RBS-D&I automata model. The model describes RBS-D&I operating in two modes. In mode 1, FoU and BDIS operate separately and interact through the MGCPC only. In mode 2, depending on the specifics of the tasks performed, FoU and RDIS can directly interact among themselves or through the MGCPC. 4) hierarchical model. The model has two sets of vertices: EO detection and platforms equipped with the necessary sensors. 5) An operational cycle model. The model describes land release operations via a methodology of functional modeling and graphic description of IDEF0 processes. Conclusions. The proposed concept and RBS-D&I solutions can provide high-performance and guaranteed EO detection in designated areas by the implementation of an intelligent platform and tools for planning the use of multifunctional fleets of UAVs and other RBS-D&I subsystems.

In the rapidly evolving domain of cryptocurrency trading, accurate market data analysis is crucial for informed decision making. Candlestick patterns, a cornerstone of technical analysis, serve as visual representations of market sentiment and potential price movements. However, the sheer volume and complexity of cryptocurrency price time-series data presents a significant challenge to traders and analysts alike. This paper introduces an innovative rule-based methodology for recognizing candlestick patterns in cryptocurrency markets using Python. By focusing on Ethereum, Bitcoin, and Litecoin, this study demonstrates the effectiveness of the proposed methodology in identifying key candlestick patterns associated with significant market movements. The structured approach simplifies the recognition process while enhancing the precision and reliability of market analysis. Through rigorous testing, this study shows that the automated recognition of these patterns provides actionable insights for traders. This paper concludes with a discussion on the implications, limitations, and potential future research directions that contribute to the field of computational finance by offering a novel tool for automated analysis in the highly volatile cryptocurrency market.

The subject of study in this article is modern technologies, tools and methods of building AI systems as a service using FPGA as a platform. The goal is to analyze modern technologies and tools used to develop FPGA-based projects for systems that implement artificial intelligence as a service and to prepare a practical AI service prototype. Task: to analyze the evolution of changes in the products of leading manufacturers of programmable logic devices and experimental and practical examples of the implementation of the paradigm of continuous reprogramming of programmable logic; analyze the dynamics of changes in the development environment of programmable logic systems for AI; analyze the essential elements of building projects for AI systems using programmable logic. According to the tasks, the following results were obtained. The area of application of hardware implementation of artificial intelligence for on-board and embedded systems including airspace industry, smart cars and medical systems is analyzed. The process of programming FPGA accelerators for AI projects is analyzed. The analysis of the capabilities of FPGA with HBM for building projects that require enough of high speed memory is performed. Description languages, frameworks, the hierarchy of tools for building of hardware accelerators for AI projects are analyzed in detail. The stages of prototyping of AI projects using new FPGA development tools and basic DPU blocks are analyzed. The parameters of the DPU blocks were analyzed. Practical steps for building such systems are offered. The practical recommendations for optimizing the neural network for FPGA implementation are given. The stages of neural network optimization are provided. The proposed steps include pruning of branches with low priority and the use of fixed point computations with custom range based on the requirements of an exact neural network. Based on these solutions, a practical case of AI service was prepared, trained and tested. Conclusions. The main contribution of this study is that, based on the proposed ideas and solutions, the next steps to create heterogeneous systems based on the combination of three elements are clear: AI as a service, FPGA accelerators as a technology for improving performance, reliability and security, and cloud or Edge resources to create FPGA infrastructure and AI as service. The development of this methodological and technological basis is the direction of further R&D.

The paper suggests a model-based approach to assessment and choice of parameters of unmanned aerial vehicle (UAV) fleets applied as one of the main services for Smart Cities and recommendations to assure their dependability. The principles of building and modeling a UAV Fleet as a Dependable Service (UAVFaaDS) for Smart Cities are formulated. Dependability issues for UAVFaaDS including a taxonomy of UAVF failures caused by equipment faults and attacks on assets were specified. The main results cover methodology, classification of UAVFaaDS models as models of queuing systems, and a set of queueing theory-based models for assessment of UAVFaaDS performance, and availability allowing for analysis and choice of fleet parameters. The efficiency of UAVFaaDS is assessed by the probability of successful delivery of services. The proposed modeling base and algorithms provide a choice of appropriate models for analysis and synthesis of UAVFaaDS, grounding of parameters of UAV fleets considering operation modes, and maintenance policy. The application of the developed models and algorithms during the synthesis of UAVFaaDS allows choosing the appropriate parameters of the fleet and ensuring the dependability of services, as well as service of orders with a probability of 0.9–0.99 depending on the requirements. Two cases of UAVFaaDS application for delivery of medicines in normal and emergence modes, models’ development, and recommendations for their utilization are discussed.

The subject of the research is the models of artificial intelligence (AI) quality. The current paper develops an AI quality model based on the definition and ordering of its characteristics. Objectives: to develop the principles and justify the sequence of analysis and development of AI quality models as ordered sets of characteristics; to offer models of AI quality for further use, first, the evaluation of individual characteristics and quality in general; to demonstrate the profiling of AI quality models for systems using artificial intelligence. The following results were obtained. The sequence of construction of AI quality models is offered. Based on the analysis of references, a list of AI characteristics was formed and their definitions were harmonized. The general model of AI quality is presented with a description of the step-by-step procedure for the realization of its hierarchical construction. A basic model of AI with abbreviated sets of characteristics is proposed due to its importance. Examples of profiling of quality models for two systems - monitoring of engineering communications and recognition of road signs are given. Conclusions. The study's main result is the development of a quality model for artificial intelligence, which is based on the analysis and harmonization of definitions and dependencies of quality characteristics specific to AI. The selection of characteristics and the construction of the quality model were carried out in such a way to exclude duplication, ensure the completeness of the presentation, as well as to determine the specific features of each characteristic. It is extremely difficult to create a model that would fully meet such requirements, so the presented options should be supplemented and improved considering the rapid development of technologies and applications of AI. The proposed quality models are open and can be supplemented and detailed according to the specific purpose and scope of AI.

From the analysis of the main theoretical provisions of heuristic self-organization systems and logical models, it follows that according to O. G. Ivakhnenko's systems of heuristic self-organization, the first task is to determine the factors content “that determine the essence of different images”. These are the images that characterize the objects of a particular subject area. After determining the composition and content of these images, the next problem is solved, namely, the problem of “generating the new successful heuristic”, which in content is a solution that leads to increased accuracy. Note that we are talking about improving the accuracy of solving the problem of data processing. It follows from the above mentioned that heuristic self-organization systems are data processing systems. This allows the multiplicity of heuristics. Heuristics in content correspond to the logical rules applied in heuristic self-organization systems. The main provisions of the heuristic self-organization system theory were developed by O. G. Ivakhnenko in the eighties of the last century, but they remain unnoticed to this day. At this time, the task is to explain why the neural network makes such a decision and not another. Based on this, the concept of “explainability of artificial intelligence” was introduced for artificial intelligence. It is the content of heuristics that forms the structure of the neural network in the form of logical rules and determines the logic of the decision made. It is established that the derivation rule, which is the basis for constructing artificial neural networks, is an abductive rule, which, unfortunately, does not meet the fourth heuristic and does not meet the definition of intelligence: intelligence is the ability to measure things. Unfortunately, none of the neural networks can measure things. From the analysis of the basic rules content of inference, it follows that the dialectical method of inference is general (generating) for the basic logical methods of inference. The difference lies in the composition and content of the middle member of the triangular relationship, namely, in the form of the element combination of the relationship: the transition from one concept to another. The explainability of artificial intelligence refers to the laws of the structure and activity of artificial neural networks. But modern theories of artificial neural networks ignore the existence of logical rules (heuristics), which were established by O. G. Ivakhnenko. After all, only knowing the rules based on which problems are solved, it is possible to check the correctness of the decision, but not by searching for such rules. The three hypotheses about the explainability of artificial intelligence and the theory of machine identification can be further defined as statements or theorems and strictly proved.

The special class of atomic functions is considered. The atomic function is a solution with compact support of linear differential functional equation with constant coefficients and linear transformations of the argument. The functions considered are used in discrete atomic compression (DAC) of digital images. The algorithm DAC is lossy and provides better compression than JPEG, which is de facto a standard for compression of digital photos, with the same quality of the result. Application of high precision values of atomic functions can improve the efficiency of DAC, as well as provide the development of new technologies for data processing and analysis. This paper aims to develop a low complexity algorithm for computing precise values of the atomic functions considered. Precise values of atomic functions at the point of dense grids are the subject matter of this paper. Formulas of V. O. Rvachev and their generalizations are used. Direct application of them to the computation of atomic functions on dense grids leads to multiple calculations of a great number of similar expressions that should be reduced. In this research, the reduction required is provided. The goal is to develop an algorithm based on V. O. Rvachev’s formulas and their generalizations. The following tasks are solved: to convert these formulas to reduce the number of arithmetic operations and to develop a verification procedure that can be used to check results. In the current research, methods of atomic function theory and dynamic programming algorithms development principles are applied. A numerical scheme for computation of atomic functions at the points of the grid with the step, which is less than each predetermined positive real number, is obtained and a dynamic algorithm based on it is developed. Also, a verification procedure, which is based on the properties of atomic functions, is introduced. The following results are obtained: 1) the algorithm developed provides faster computation than direct application of the corresponding formulas; 2) the algorithm proposed provides precise computation of atomic functions values; 3) procedure of verification has linear complexity in the number of values to be checked. Moreover, the algorithms proposed are implemented using Python programming language and a set of tables of atomic functions values are obtained. Conclusions: results of this research are expected to improve existing data processing technologies based on atomic functions, especially the algorithm DAC, and accelerate the development of new ones.

Предметом дослідження в статті є методи виявлення та розпізнавання вибухонебезпечних предметів. Мета роботи – розроблення основних положень концепції гарантованого виявлення та розпізнавання вибухонебезпечних предметів. У статті розв’язуються такі завдання: аналіз наявних підходів щодо застосування традиційних одиничних та комбінованих, а також нетрадиційних (біологічних) методів виявлення вибухонебезпечних предметів, розроблення класифікаційної таблиці методів виявлення вибухонебезпечних предметів за фізичними принципами, аналіз переваг та недоліків розглянутих методів виявлення вибухонебезпечних предметів, створення порівняльної таблиці методів виявлення вибухонебезпечних предметів, формулювання основних положень концепції гарантованого виявлення та розпізнавання вибухонебезпечних предметів. Застосовуються такі методи: порівняння, абстрагування, аналіз і синтез, наукова індукція. Здобуто такі результати: проаналізовано особливості наявних традиційних і нетрадиційних (біологічних) методів виявлення вибухонебезпечних предметів. Запропоновано класифікацію методів виявлення вибухонебезпечних предметів з урахуванням параметрів, що впливають на ймовірність виявлення та продуктивність. У табличному вигляді запропоновано результати порівняльного аналізу методів виявлення вибухонебезпечних предметів за такими показниками: тип взаємодії з вибухонебезпечними предметами, тип платформи, потенційна продуктивність, підтримка інформаційних технологій, параметри якості, економічні показники. Сформульовано основні положення концепції гарантованого виявлення та розпізнавання вибухонебезпечних предметів. Висновки. Низька продуктивність наявних методів не дає змоги швидко й ефективно розчищати забруднені вибухонебезпечними предметами території, що призводить до значної кількості вражень і загибелі людей унаслідок підриву вибухонебезпечних предметів. Самостійне використання окремих методів виявлення не може суттєво збільшити ймовірність виявлення вибухонебезпечних предметів. Для підвищення продуктивності та безпеки виконання робіт із пошуку та знешкодження вибухонебезпечних предметів доцільно використовувати безпілотні інтелектуальні платформи доправлення інформаційно-вимірювальних засобів.

The entropy-oriented approach called security- or cybersecurity-informed safety (SIS or CSIS, respectively) is discussed and developed in order to analyse and evaluate the safety and dependability of autonomous transport systems (ATSs) such as unmanned aerial vehicles (UAVs), unmanned maritime vehicles (UMVs), and satellites. This approach allows for extending and integrating the known techniques FMECA (Failure Modes, Effects, and Criticality Analysis) and IMECA (Intrusion MECA), as well as developing the new SISMECA (SIS-based Intrusion Modes, Effects, and Criticality Analysis) technique. The ontology model and templates for SISMECA implementation are suggested. The methodology of safety assessment is based on (i) the application and enhancement of SISMECA considering the particularities of various ATSs and roles of actors (regulators, developers, operators, customers); (ii) the development of a set of scenarios describing the operation of ATS in conditions of cyberattacks and physical influences; (iii) AI contribution to system protection for the analysed domains; (iv) scenario-based development and analysis of user stories related to different cyber-attacks, as well as ways to protect ATSs from them via AI means/platforms; (v) profiling of AI platform requirements by use of characteristics based on AI quality model, risk-based assessment of cyberattack criticality, and efficiency of countermeasures which actors can implement. Examples of the application of SISMECA assessment are presented and discussed.

This paper evaluates the status quo of modern society and identifies the neglect of humanism as the root cause of many of today’s global challenges. Note that “smart cities” are not excluded from this indictment. The “Ptolemaic Universe” offers a means to restore our symbiosis with the environment. The ReSeT model is proposed as a tool to analyze the Ptolemaic Universe. Using ReSeT: Homo Sapiens becomes dependent on AI resulting in Homo Digitalis, with further evolution in concert with AI resulting in Homo Digitus. All of these stages are then analyzed in the context of global trusted dependability (GTD). The wellness domain, provides the design specification framework for Homo Digitus’ human-centered and resilient “smart city” ecosystem. This ultimately leads to a better world of increased wellness for Homo Hominus, with better smart cities emphasizing education and science, promoting wisdom and common sense, and rejecting violence. In summary, humanity has generated diverse social structures with erratic outcomes. On the other hand, technology provides a successful foundation for modern society especially in the Pandemic Era. However, technology’s contributions are generally not publicly acknowledged. The paper concludes with several initiatives designed to establish a trusted and resilient society.

Typical structures of monitoring systems (MSs) that are used in urban complex objects (UCOs) (such as large industrial facilities, power facilities, and others) during the post-accident period are combined with the technologies of flying sensor networks (FSNets) and flying edge networks (FENets) (FSNets and FENets); cloud/fog computing and artificial intelligence are also developed. An FSNets and FENets-based MS, composed of one of the Advanced Air Mobility (AAM) systems classes, which comprise main and virtual crisis centers, fleets of flying sensors, edge nodes, and a ground control station, is presented and discussed. Reliability and survivability models of the MS for the UCOs, considering various operation conditions and options of redundancy, are developed and explored. A tool to support the research on MS reliability, survivability, and the choice of parameters is developed and described. Crucially, this paper enhances the technique for assessing systems using the multi-parametrical deterioration of characteristics as a class of multi-state systems. Problems that may arise when using FSNets/FENet-based AAM systems are discussed. The main research results comprise a structural basis, a set of models, and a tool for calculating the reliability and survivability of FSNets/FENet-based AAM systems, with various options for distributing the processing and control resources between components, their failure rates, and degradation scenarios.

Operating reliability assessment of instrumentation and control systems (I&Cs) is always one of the most important activities, especially for critical domains such as nuclear power plants (NPPs). It is an important source of I&C reliability information preferable to lab testing data because it provides information on I&C reliability under real use conditions. That is the reason that now it is a common practice for companies to have an established process of collecting operating reliability data on a large variety of used components on regular basis, maintaining a database with failure information, total operation time, typical failure modes, etc. The intensive use of complicated components like field-programmable gate arrays (FPGAs) in I&C which appear in upgrades and newly-built nuclear power plants makes the task to develop and validate advanced operating reliability assessment methods that consider specific technology features very topical. Increased integration densities make the reliability of integrated circuits the most crucial point in modern NPP I&Cs. Moreover, FPGAs differ in some significant ways from other integrated circuits: they are shipped as blanks and are very dependent on the design configured into them. Furthermore, FPGA design could be changed during planned NPP outage for different reasons. Considering all possible failure modes of FPGA-based NPP instrumentation and control systems at the design stage is a quite challenging task. Therefore, operating reliability assessment is one of the most preferable ways to perform a comprehensive analysis of FPGA-based NPP I&Cs. Based on information in the literature and own experience, operational vs analytical reliability could be pretty far apart. For that reason, analytical reliability assessment using reliability block diagrams (RBD), failure modes, effects and diagnostics analysis (FMEDA), fault tree analysis (FTA), fault insertion testing (FIT), and other techniques and their combinations are important to meet requirements for such systems. The paper summarizes our experience in operating and analytical reliability assessment of FPGA based NPP I&Cs.

Artificial intelligence systems are increasingly being used in industrial applications, security and military contexts, disaster response complexes, policing and justice practices, finance, and healthcare systems. However, disruptions to these systems can have negative impacts on health, mortality, human rights, and asset values. The protection of such systems from various types of destructive influences is thus a relevant area of research. The vast majority of previously published works are aimed at reducing vulnerability to certain types of disturbances or implementing certain resilience properties. At the same time, the authors either do not consider the concept of resilience as such, or their understanding varies greatly. The aim of this study is to present a systematic approach to analyzing the resilience of artificial intelligence systems, along with an analysis of relevant scientific publications. Our methodology involves the formation of a set of resilience factors, organizing and defining taxonomic and ontological relationships for resilience factors of artificial intelligence systems, and analyzing relevant resilience solutions and challenges. This study analyzes the sources of threats and methods to ensure each resilience properties for artificial intelligence systems. As a result, the potential to create a resilient artificial intelligence system by configuring the architecture and learning scenarios is confirmed. The results can serve as a roadmap for establishing technical requirements for forthcoming artificial intelligence systems, as well as a framework for assessing the resilience of already developed artificial intelligence systems.

The article analytically summarizes the idea of applying Shannon’s principle of entropy maximization to sets that represent the results of observations of the “input” and “output” entities of the stochastic model for evaluating variable small data. To formalize this idea, a sequential transition from the likelihood function to the likelihood functional and the Shannon entropy functional is analytically described. Shannon’s entropy characterizes the uncertainty caused not only by the probabilistic nature of the parameters of the stochastic data evaluation model but also by interferences that distort the results of the measurements of the values of these parameters. Accordingly, based on the Shannon entropy, it is possible to determine the best estimates of the values of these parameters for maximally uncertain (per entropy unit) distortions that cause measurement variability. This postulate is organically transferred to the statement that the estimates of the density of the probability distribution of the parameters of the stochastic model of small data obtained as a result of Shannon entropy maximization will also take into account the fact of the variability of the process of their measurements. In the article, this principle is developed into the information technology of the parametric and non-parametric evaluation on the basis of Shannon entropy of small data measured under the influence of interferences. The article analytically formalizes three key elements: -instances of the class of parameterized stochastic models for evaluating variable small data; -methods of estimating the probability density function of their parameters, represented by normalized or interval probabilities; -approaches to generating an ensemble of random vectors of initial parameters.

Modern trainable image recognition models are vulnerable to different types of perturbations; hence, the development of resilient intelligent algorithms for safety-critical applications remains a relevant concern to reduce the impact of perturbation on model performance. This paper proposes a model and training method for a resilient image classifier capable of efficiently functioning despite various faults, adversarial attacks, and concept drifts. The proposed model has a multi-section structure with a hierarchy of optimized class prototypes and hyperspherical class boundaries, which provides adaptive computation, perturbation absorption, and graceful degradation. The proposed training method entails the application of a complex loss function assembled from its constituent parts in a particular way depending on the result of perturbation detection and the presence of new labeled and unlabeled data. The training method implements principles of self-knowledge distillation, the compactness maximization of class distribution and the interclass gap, the compression of feature representations, and consistency regularization. Consistency regularization makes it possible to utilize both labeled and unlabeled data to obtain a robust model and implement continuous adaptation. Experiments are performed on the publicly available CIFAR-10 and CIFAR-100 datasets using model backbones based on modules ResBlocks from the ResNet50 architecture and Swin transformer blocks. It is experimentally proven that the proposed prototype-based classifier head is characterized by a higher level of robustness and adaptability in comparison with the dense layer-based classifier head. It is also shown that multi-section structure and self-knowledge distillation feature conserve resources when processing simple samples under normal conditions and increase computational costs to improve the reliability of decisions when exposed to perturbations.

This paper suggests a strategy (C5) for assessing cloud and IoT system (CIS) dependability, availability, and cybersecurity based on the continuous collection, comparison, choice, and combination of Markov and semi-Markov models (MMs and SMMs). It proposes the systematic building of an adequate and accurate model to evaluate CISs considering (1) continuous evolution of the model(s) together with systems induced by changes in the CIS or physical and cyber environment parameters; (2) the necessity of collecting data on faults, failures, vulnerabilities, cyber-attacks, privacy violations, and patches to obtain actual data for assessment; (3) renewing the model set based on analysis of CIS operation; (4) the possibility of choice and utilizing “off-the-shelf” models with understandable techniques for their development to assure improved accuracy of assessment; (5) renewing the models during application of CIS by time, component or mixed combining, taking into consideration different operation and maintenance events. The results obtained were algorithms for data collection and analysis, choice, and combining appropriate MM and SMMs and their different types, such as multi-fragmental and multiphase models, considering changing failure rates, cyber-attack parameters, periodical maintenance, etc. To provide and verify the approach, several private and public clouds and IoT systems were researched and discussed in the context of C5 and proposed algorithms.

This paper suggests a methodology (conception and principles) for building two-mode monitoring systems (SMs) for industrial facilities and their adjacent territories based on the application of unmanned aerial vehicle (UAV), Internet of Things (IoT), and digital twin (DT) technologies, and a set of SM reliability models considering the parameters of the channels and components. The concept of building a reliable and resilient SM is proposed. For this purpose, the von Neumann paradigm for the synthesis of reliable systems from unreliable components is developed. For complex SMs of industrial facilities, the concept covers the application of various types of redundancy (structural, version, time, and space) for basic components—sensors, means of communication, processing, and presentation—in the form of DTs for decision support systems. The research results include: the methodology for the building and general structures of UAV-, IoT-, and DT-based SMs in industrial facilities as multi-level systems; reliability models for SMs considering the applied technologies and operation modes (normal and emergency); and industrial cases of SMs for manufacture and nuclear power plants. The results obtained are the basis for further development of the theory and for practical applications of SMs in industrial facilities within the framework of the implementation and improvement of Industry 4.0 principles.

Cybersecurity is a common Internet of Things security challenge. The lack of security in IoT devices has led to a great number of devices being compromised, with threats from both inside and outside the IoT infrastructure. Attacks on the IoT infrastructure result in device hacking, data theft, financial loss, instability, or even physical damage to devices. This requires the development of new approaches to ensure high-security levels in IoT infrastructure. To solve this problem, we propose a new approach for IoT cyberattack detection based on machine learning algorithms. The core of the method involves network traffic analyses that IoT devices generate during communication. The proposed approach deals with the set of network traffic features that may indicate the presence of cyberattacks in the IoT infrastructure and compromised IoT devices. Based on the obtained features for each IoT device, the feature vectors are formed. To conclude the possible attack presence, machine learning algorithms were employed. We assessed the complexity and time of machine learning algorithm implementation considering multi-vector cyberattacks on IoT infrastructure. Experiments were conducted to approve the method’s efficiency. The results demonstrated that the network traffic feature-based approach allows the detection of multi-vector cyberattacks with high efficiency.

Safety assessment of modern critical instrumentation and control systems is a complicated process considerably dependent on expert techniques, single/multiple faults consideration scope, other assumptions, invoked limitations, and support tools used during the assessment process. Ignoring these assumptions, as well as the significance of expert and tool influence, could lead to such effects as functional safety underestimation or overestimation in such a manner that functional safety assessment correctness and accuracy are affected. This paper introduces XMECA (x modes, effects, and criticality analysis, where x could be from different known techniques and domains—failures in functional safety, vulnerabilities and intrusions regarding cybersecurity, etc.) as a key technique of safety assessment. To verify the results obtained as XMECA deliverables, expert and uncertainty modes, effects, and criticality analysis (EUMECA) is performed, in particular focusing on decisions and judgments made by experts. Scenarios for processing verbal and quantitative information of XMECA tables from experts are offered. A case study of a possible functional safety assessment approach that considers the above-mentioned techniques and a supporting tool is provided. To assess the trustworthiness of safety analysis and estimation using XMECA, a set of the metrics is suggested. Features of adapting the suggested method for security assessment considering intrusions, vulnerabilities, and effects analysis (IMECA technique) are discussed.

The factors complicating the specification of requirements for artificial intelligence systems (AIS) and their verification for the AIS creation and modernization are analyzed. The harmonization of definitions and building of a hierarchy of AIS characteristics for regulation of the development of techniques and tools for standardization, as well as evaluation and provision of requirements during the creation and implementation of AIS, is extremely important. The study aims to develop and demonstrate the use of quality models for artificial intelligence (AI), AI platform (AIP), and AIS based on the definition and ordering of characteristics. The principles of AI quality model development and its sequence are substantiated. Approaches to formulating definitions of AIS characteristics, methods of representation of dependencies, and hierarchies of characteristics are given. The definitions and harmonization options of hierarchical relations between 46 characteristics of AI and AIP are suggested. The quality models of AI, AIP, and AIS presented in analytical, tabular, and graph forms, are described. The so-called basic models with reduced sets of the most important characteristics are presented. Examples of AIS quality models for UAV video navigation systems and decision support systems for diagnosing diseases are described.

Digital images are used in various technological, financial, economic, and social processes. Huge datasets of high-resolution images require protected storage and low resource-intensive processing, especially when applying edge computing (EC) for designing Internet of Things (IoT) systems for industrial domains such as autonomous transport systems. For this reason, the problem of the development of image representation, which provides compression and protection features in combination with the ability to perform low complexity analysis, is relevant for EC-based systems. Security and privacy issues are important for image processing considering IoT and cloud architectures as well. To solve this problem, we propose to apply discrete atomic transform (DAT) that is based on a special class of atomic functions generalizing the well-known up-function of V.A. Rvachev. A lossless image compression algorithm based on DAT is developed, and its performance is studied for different structures of DAT. This algorithm, which combines low computational complexity, efficient lossless compression, and reliable protection features with convenient image representation, is the main contribution of the paper. It is shown that a sufficient reduction of memory expenses can be obtained. Additionally, a dependence of compression efficiency measured by compression ratio (CR) on the structure of DAT applied is investigated. It is established that the variation of DAT structure produces a minor variation of CR. A possibility to apply this feature to data protection and security assurance is grounded and discussed. In addition, a structure or file for storing the compressed and protected data is proposed, and its properties are considered. Multi-level structure for the application of atomic functions in image processing and protection for EC in IoT systems is suggested and analyzed.

Предметом досліджень статті є резервовані структури складних систем (РСС), зокрема, керуючих систем безпеки АЕС, енергомереж, аерокосмічних систем, інших комплексів критичного застосування (ККЗ). Метою є розроблення і дослідження моделей надійності (безвідмовності) таких РСС з урахуванням засобів оброблення інформації та комунікацій ККЗ. Завдання: 1) сформувати множину РСС, які базуються на мажоритарному резервуванні за принципом «2-з-3» і дублюванні за принципом «1-з-2» засобів оброблення інформації та комунікацій при послідовно-паралельних і місткових з’єднаннях елементів; 2) розробити структурні схеми надійності (ССН), математичні (аналітичні) моделі безвідмовності РСС з каскадним резервуванням «2-з-3» і «1-з-2»; 3) дослідити ці моделі і визначити залежності ймовірностей безвідмовної роботи різних РСС від часу, інтенсивностей відмов засобів оброблення інформації та комунікацій, мажоритарних елементів, елементів «1-з-2»; 4) сформулювати рекомендації щодо вибору типів РСС залежно від значень вхідних параметрів та вимог до систем. Для цього використовувались методи теорії множин, теорії ймовірностей, комбінаторного аналізу. Отримано такі результати. Запропоновано множину РСС з різними варіантами резервування і з’єднання ярусів «2-з-3», розроблено і досліджено відповідні ССН і математичні моделі безвідмовності та сформульовано рекомендації щодо їх використання залежно від вхідних параметрів. Висновки. Наукова новизна отриманих моделей полягає в наступному: вони надають аналітичну оцінку для РСС з різними варіантами каскадів «2-з-3» і «1-з-2 з урахуванням засобів оброблення інформації та комунікацій ККЗ.

This paper describes a proposed method and technology of safety assessment of projects based on field programmable gate arrays (FPGA). Safety assessment is based on special invariants, e.g., properties which remain unchanged when a specified transformation is applied. A classification and examples of FPGA project invariants are provided. In the paper, two types of invariants are described. The first type of invariants used for such assessment are those which are versatile since they reflect the unchanged properties of FPGA projects, hardware description languages, etc. These invariants can be replenished as experience gained in project implementation accumulates. The second type of invariants is formed based on an analysis of the specifics of a particular FPGA project and reflects the features of the tasks to be solved, the algorithms that are implemented, the hardware FPGA chips used, and the computer-aided design tools, etc. The paper contains a description of the overall conception and particular stages of FPGA projects invariant-based safety assessment. As examples for solving some tasks (using of invariants and defect injections), the paper contains several algorithms written in the VHSIC hardware description language (VHDL). The paper summarizes the results obtained during several years of practical and theoretical research. It can be of practical use for engineers and researchers in the field of quality, reliability, and security of embedded systems, software and information management systems for critical and business applications.

Об’єкт. Процес виявлення бот-мереж у корпоративних мережах на основі аналізу мережевого трафіка та поведінки програмного забезпечеення комп’ютерних системах. Предмет. Методи виявлення бот-мереж в комп’ютерних системах. Мета. Підвищення достовірності виявлення бот-мереж шляхом розроблення методів їх виявлення бот-мереж в корпоративних мережах. Результати. Запропоновано новий підхід до виявлення бот-мереж в корпоративних мережах на основі аналізу поведінки ботів. Виявлення бот-мереж здійснюється шляхом застосування розроблених двох методів: за допомогою аналізу на мережному рівні та на хостовому рівні. Перший метод дозволяє аналізувати поведінку програмного забезпечення на хості, що може вказувати на можливу присутність бота безпосередньо на хості і виявлення шкідливого програмного забезпечення, тоді як другий метод включає в себе моніторинг і аналіз DNS-трафіка, що також дозволяє зробити висновок про інфікування мережних хостів ботами бот-мережі. На основі запропонованих методів було розроблено ефективний інструмент виявлення бот-мереж - BotGRABBER. Він здатний виявляти боти, які використовують такі методи ухилення від виявлення як періодична зміна IP-відображення (cycling of IP mapping), «потік доменів» (domain flux), «швидкозмінні» мережі (fast flux) та DNS-тунелювання (DNS tunneling). Висновки. Використання розробленої системи дозволяє виявляти інфіковані ботами бот-мереж хости і локалізувати шкідливі програми з високою ефективністю – до 96%, а також демонструє низькі помилкові спрацьовування – на рівні 3-5%. Особливість запропонованого підходу полягає в тому, що виявлення бот-мереж є «невидимим» для власників бот-мереж.

The subject matter of the article is opportunity use of a method of Pascal for finding of remainders of division of large numbers when using CRC. The goal is to analyze a possibility of use of a method of Pascal for definition of remainders of division in the CRC systems. Tasks: to analyze a possibility of use of CRC for noise-immune coding in different channels, to show a possibility of application of a method of Pascal for an image of noise-immune coding with use of checksums. The used methods are analytical method, methods of modal arithmetic’s. The received such results. The technique of calculation of Checksum of the message which are used for the control of integrity and restoration distorted the message is developed. Conclusions. Use of a method of Pascal for definition of remainders of division allows accelerating considerably process of search of various options the message, which will be restored. Realization of the offered method allows creating parallel structures, which give the chance to solve a problem of recovery of the message on the scale of real time.

Предметом вивчення в статті є процеси оцінювання кібербезпеки інформаційно-керуючих систем (ІКС). Метою є розробка техніки аналізу розрив процесу проведенні аналізу кібербезпеки. Завдання: розробити метод аналізу розривів у процесі оцінювання нефункціональних вимог до функціональної та кібербезпеки ІКС, заснований на класифікації вимог з урахуванням можливості їх декомпозиції, який включає в себе побудову поліпшеного кейса запевнення інформаційної безпеки і визначення контрзаходів щодо усунення виявлених розривів. Висновки. Наукова новизна отриманих результатів полягає в наступному: отримав подальшого розвитку метод забезпечення інформаційної безпеки цифрових компонентів ІКС шляхом проведення аналізу невідповідностей вимог з використанням процедур опису вразливостей і оцінки критичності наслідків вторгнень, а також визначення множини контрзаходів за критерієм «безпека-вартість», що дозволяє зменшити ризики до прийнятного рівня.

The paper discusses throughput unfairness inherent in the very nature of mixed data rate Wi-Fi networks employing random media access control technique CSMA/CA. This unfairness exhibits itself through the fact that slow clients consume more airtime to transfer a given amount of data, leaving less airtime for other clients. This decreases the overall network throughput and significantly degrades performance of high data rate clients. In the paper we propose mathematical models considering airtime unfairness and estimating wireless networks throughput depending on number of network connections and their data rates. These models show that all wireless clients have an equal throughput independently of data rates used by them. We verify our theoretical findings by running natural experiment and show that client's throughput approximates to the data rate of the slowest client.

The work highlights the most important principles of software reliability management (SRM). The SRM concept construes a basis for developing a method of requirements correctness improvement. The method assumes that complicated requirements contain more actual and potential design faults/defects. The method applies a newer metric to evaluate the requirements complexity and double sorting technique evaluating the priority and complexity of a particular requirement. The method enables to improve requirements correctness due to identification of a higher number of defects with restricted resources. Practical application of the proposed method in the course of demands review assured a sensible technical and economic effect.