Viacheslav Davydov

The object of this study is the task scheduling process in heterogeneous distributed information systems. The scientific task addressed relates to the low efficiency of resource management, especially under conditions of dynamic workload and significant uncertainty, which are typical for distributed information systems. An intelligent task scheduling method has been devised for heterogeneous distributed information systems, which effectively combines DAG (Directed Acyclic Graph) and GERT (Graphical Evaluation and Review Technique) models with advanced artificial intelligence algorithms. The proposed method employs a Graph Attention Network (GAT) to account for probabilistic dependences between tasks and Proximal Policy Optimization (PPO) for dynamic control of task distribution within the system. Furthermore, a Bayesian method is used to optimize the assignment of tasks to computing nodes. The use of the proposed method reduced the average task execution time from 51.5 to 35.2 seconds, and the standard deviation of the load between nodes from 0.47 to 0.22. These results are explained by the flexibility of the models to unforeseen changes and the ability to self-learn based on accumulated data. A feature of the method is the combination of classical graph models with probabilistic estimation and adaptive AI mechanisms, which made it possible not only to take into account the dynamics of the environment but also to ensure accurate response to changes in resource availability. By using GERT graphs, the algorithm forms alternative planning paths in case of failures or unforeseen delays, and machine learning components provide self-correction of decisions. The method is oriented towards application in cloud and IoT infrastructures, in which scalability, planning accuracy, and resilience to changes are critical

The article presents a mathematical model of radar systems operating under jamming conditions. The relevance of the study is highlighted by two main factors: the need to enhance the accuracy of radar systems and the requirement to incorporate new jamming factors into models, which raises challenges in maintaining the reliability of mathematical formalizations. Various modeling approaches, including Markov models, deep learning, and probabilistic filters, are analyzed. These methods enable radar systems to adapt to changing conditions and improve target detection, which are crucial for their effective operation under uncertainty. The article emphasizes the importance of integrating different techniques to develop algorithms capable of functioning in complex jamming environments. A key aspect of the model is its consideration of time delays and probabilistic characteristics, which help to formalize and optimize the system’s performance in various jamming scenarios. The model is based on the Graphical Evaluation and Review Technique (GERT) network, offering in-depth analysis and adaptation of radar systems to changing conditions. This approach is a critical component for enhancing the resilience of radar systems in complex and uncertain operational environments.

The subject of this study is the process of anomaly detection in high-load complex computer systems (HLCCSs). The task addressed in the paper is the lack of real-time anomaly detection models in HLCCS with a specified accuracy. A set of mathematical models for real-time anomaly detection has been built and investigated. This set includes a mathematical model for detecting anomalous connections between components of computer system (DACCCSs) and a mathematical model for assessing current state of computer system (CSACS). The results of models tests showed the following efficiency metrics. For a DACCCS model: accuracy – 84 %, positive predictive value – 87 %, recall – 74 %, and weighted average accuracy (WAA) – 78 %. For a CSACS model: accuracy – 91 %, positive predictive value – 82 %, recall – 68 %, and WAA – 67 %. The positive results of the study can be attributed to the following factors. A DACCCS model uses projection matrices and orthogonal vector functions to analyze anomalies. This enables the creation of spatial decompositions that reveal complex interrelationships between system components using only eigenvalues and eigenvectors. A CSACS model applies the singular value decomposition method, which implies solving a system of scalar equations to determine the current state of the system. This approach minimizes computational costs compared to methods requiring the solution of complex matrix equations. Thus, the model could be applied for real-time data analysis and anomaly detection under conditions of limited resources and high system load. The practical application scope includes HLCCS, such as banking transaction servers and cloud platforms, in which it is essential to enable stable operation under high request amount and to minimize the risk of data loss or service failure

A set of training methods for artificial neural networks of intelligent decision-making support systems has been developed. A distinctive feature of the proposed methods is that not only the synaptic weights of the artificial neural network, but also the type and parameters of the membership function are trained. If it is impossible to ensure the specified quality of functioning of artificial neural networks due to learning the parameters of the artificial neural network, the learning of the architecture of artificial neural networks takes place. The choice of the architecture, type and parameters of the membership function takes place taking into account the computing resources of the tool and taking into account the type and amount of information entering the input of the artificial neural network. Due to the use of the proposed methods, there is no accumulation of learning errors of artificial neural networks as a result of processing information received at the input of artificial neural networks. Also, a distinctive feature of the developed methods is that no preliminary calculation data is required for data calculation. The development of the proposed methods is due to the need to train artificial neural networks for intelligent decision-making support systems, with the aim of processing a larger amount of information, with the unambiguity of the decisions being made. According to the results of the research, it was established that the specified training methods provide an average of 10‒18% higher training efficiency of artificial neural networks and do not accumulate errors during training. These methods will make it possible to conduct training of artificial neural networks, to determine effective measures to increase the efficiency of the functioning of artificial neural networks. The use of the specified methods will allow to increase the efficiency of the functioning of artificial neural networks due to the learning of the parameters and architecture of artificial neural networks, to reduce the use of computing resources of decision-making support systems. The developed methods will make it possible to develop measures aimed at increasing the effectiveness of learning artificial neural networks; increase the efficiency of information processing in artificial neural networks.

This study solves the task to redistribute the load on a geographically distributed foggy environment in order to achieve a load balance of virtual clusters. The necessity and possibility of developing a universal and at the same time scientifically based approach to load balancing has been determined. Object of study: the process of redistribution of load in a foggy environment between virtual, geographically distributed clusters. A load balancing method makes it possible to reduce delays and decrease the time for completing tasks on foggy nodes, which brings task processing closer to real time. To solve the task, a mathematical model of the functioning of a separate cluster in a foggy environment has been built. As a result of modeling, the problem of finding the optimal distribution of tasks across the nodes of the virtual cluster was obtained. The limitations of the problem take into account the characteristics of the physical nodes of support for the virtual cluster. The process of distributing the additional load was also simulated through the graph representation of tasks entering virtual clusters. The task to devise a method for load transfer between virtual clusters within a foggy environment is solved using the proposed iterative algorithm for finding a suitable cluster and placing the load. The simulation results showed that the balance of the foggy environment when using the proposed method increases significantly provided the network load is small. The scope of application of the results includes geographically distributed foggy systems, in particular the foggy layer of the industrial Internet of Things. A necessary practical condition for using the proposed results is the non-exceeding the specified limit of the total load on the foggy medium, usually 70 %

This paper has determined the relevance of the issue related to improving the accuracy of the results of mathematical modeling of the software security testing process. The fuzzy GERT-modeling methods have been analyzed. The necessity and possibility of improving the accuracy of the results of mathematical formalization of the process of studying software vulnerabilities under the conditions of fuzziness of input and intermediate data have been determined. To this end, based on the mathematical apparatus of fuzzy network modeling, a fuzzy GERT model has been built for investigating software vulnerabilities. A distinctive feature of this model is to take into consideration the probabilistic characteristics of transitions from state to state along with time characteristics. As part of the simulation, the following stages of the study were performed. To schematically describe the procedures for studying software vulnerabilities, a structural model of this process has been constructed. A "reference GERT model" has been developed for investigating software vulnerabilities. The process was described in the form of a standard GERT network. The algorithm of equivalent transformations of the GERT network has been improved, which differs from known ones by considering the capabilities of the extended range of typical structures of parallel branches between neighboring nodes. Analytical expressions are presented to calculate the average time spent in the branches and the probability of successful completion of studies in each node. The calculation of these probabilistic-temporal characteristics has been carried out in accordance with data on the simplified equivalent fuzzy GERT network for the process of investigating software vulnerabilities. Comparative studies were conducted to confirm the accuracy and reliability of the results obtained. The results of the experiment showed that in comparison with the reference model, the fuzziness of the input characteristic of the time of conducting studies of software vulnerabilities was reduced, which made it possible to improve the accuracy of the simulation results.

This paper reports an analysis of the software (SW) safety testing techniques, as well as the models and methods for identifying vulnerabilities. An issue has been revealed related to the reasoned selection of modeling approaches at different stages of the software safety testing process and the identification of its vulnerabilities, which reduces the accuracy of the modeling results obtained. Two steps in the process of identifying software vulnerabilities have been identified. A mathematical model has been built for the process of preparing security testing, which differs from the known ones by a theoretically sound choice of the moment-generating functions when describing transitions from state to state. In addition, the mathematical model takes into consideration the capabilities and risks of the source code verification phase for cryptographic and other ways to protect data. These features generally improve the accuracy of modeling results and reduce input uncertainty in the second phase of software safety testing. An advanced security compliance algorithm has been developed, with a distinctive feature of the selection of laws and distribution parameters that describe individual state-to-state transitions for individual branches of Graphical Evaluation and Review Technique networks (GERT-networks). A GERT-network has been developed to prepare for security testing. A GERT-network for the process of checking the source code for cryptographic and other data protection methods has been developed. A graphic-analytical GERT model for the first phase of software safety testing has been developed. The expressions reported in this paper could be used to devise preliminary recommendations and possible ways to improve the effectiveness of software safety testing algorithms

У статті представлені результати аналізу основних методів виявлення вразливостей програмного забезпечення. Представлені результати досліджень ряду авторів, синтезуючих та регламентуючих знань про системи виявлення вразливостей програмного забезпечення. Розглянуті методи аналізу програмного забезпечення, що використовуються при проведенні сертифікаційних випробувань. Показано, що використання існуючих методів та методик аналізу безпеки програмного забезпечення не забезпечує точність результатів на умовах нечисельних вхідних даних. Цей недолік ускладнює жорсткі вимоги до оперативності реалізації тестових сценаріїв. Отже, необхідне розроблення системи виявлення вразливих користувачів, основною задачею якої буде мінімізація кількості протирічної інформації, що використовується експертом при прийнятті рішень. Найкраще перспективне створення підвищення ефективності існуючих систем виявлення вразливостей полягає у зменшенні навантажень експертів за рахунок випробувань методів виявлення вразливостей, що впроваджені до систем підтримки прийняття рішень. Це дозволить істотно знизити затримки часу при прийнятті рішень щодо безпеки програмного забезпечення, а також, як наслідок, зробить процедуру тестування безпеки програмного забезпечення більш доступною широкому колу розробників. Підвищення доступності процедур тестування безпеки програмного забезпечення повинно підвищувати інтерес як розробників, так і користувачів інформаційних систем до проблем безпеки, що, в свою чергу, дасть новий імпульс розвитку методів та систем виявлення вразливостей і перевірки відповідності програмним вимогам безпеки.

Nowadays, number of cyber-attacks aimed at software increases. Thus, improving the quality of software security services becomes acute. Confidentiality is one of the basic security services. It is provided, including through the use of obfuscation mechanism. Code quality measurement Methods has been investigated. Particular attention is paid to methods aimed at decompiled code, where standard metrics do not take in account. The method of decompiled code static analysis has been improved. This method takes into account the features of the compile procedure for languages with intermediate code.

Introduction to automatic safety on-board UAV system. Reviewed methods which helps objectively evaluate the stability level of the software and hardware complex of the control and positioning onboard system. Proposed use of GERT networks for comparative analysis and research of individual stages mission tasks.