№ 3 (73)

Keywords: act of unlawful interference, automated corporate governance system, data bank of threats to the security of automated systems by technological processes, artificial intelligence, cascade threat model, critical information infrastructure, protection measures, fuzzy logic.
Purpose of the study: to advance the methodology of cascade security threats modeling of intelligent transport systems (ITS) of critical information infrastructure (CII); development standardized scenarios of cascade security threats, and to integrate the cascade threat model with a hybrid adaptive security management system (HASMS) for automated development of effective solutions and intelligent risk management algorithms. This integration enables end­to­end security control: from real (or potential) threat actors through to the implementation of protective measures in response to actual (or potentially feasible) acts of unlawful interference (AUI) affecting protected ITS objects – ensuring a predefined level of protection and compliance with regulatory security requirements.
Methods of the study: systemic analysis of ITS security threats; CII security assurance methods; application of hybrid intelligence for risk assessment and security management of CII ITS objects; synthesis and analysis of cascade threats realization scenarios for CII ITS; development of protective measures against cascade threats and evaluation of their compliance with regulatory standards.
Results: a methodology constructing of cascade security threats scenarios of CII ITS has been developed, accounting for the nonlinear propagation of threats across an object’s architecture. The methodology is integrated into the HASMS to enable automated discovery of effective solution and intelligent risk management algorithms along the full chain – from real (or potential) adversaries to the deployment of protective responses against actual (or potential) AUI, thereby ensuring the required protection level and regulatory compliance. For intelligent water transport systems (IWTS) 44 standardized cascade threat scenarios have been formulated and illustrated using object­-level subsystems for global navigation satellite systems (GNSS) and automated ships traffic management systems. Protection measures aligned with FSTEC of Russia regulatory documents have been adapted to IWTS­-specific characteristics, ensuring the prescribed security level amid an expanding threat landscape and maintaining compliance with applicable regulatory requirements.
Scientific novelty: the authors propose a paradigm shift from linear to nonlinear (cascade) threat models for CII ITS, enabling hybrid adaptive security management based on the integration of hybrid intelligence technologies and digital twins of ITS objects. This approach supports the automated search for and implementation of maximally effective security management scenarios grounded in comprehensive risk assessments.
Practical relevance: the developed standardized cascade threat scenarios for IWTS – demonstrated through GNSS and automated ships traffic management subsystems – facilitate automation of effective solution discovery and intelligent risk management algorithms across the entire security response chain from real (or potential) threat actors to protective measure implementation against actual (or potential) AUI, ensuring both the required protection level and regulatory compliance. The findings are applicable to ITS across all transport modes. They have been implemented in the Strategic Technological Project «Intelligent Transport Systems and Autonomous Vehicles» under the Priority 2030 Academic Leadership Program; contributed to the Technical Assignment prepared by the Russian delegation for the 110th session of the International Mari time Organization’s Maritime Safety Committee (agenda item: «Revision of the Guidelines on Maritime Cyber Risk Management (MSC­FAL.1/Circ.3/Rev.2) and Identification of Further Actions to Enhance Maritime Cybersecurity»); and applied in the research project «Centralized Trusted Intelligent Management Systems for Off­-Street Urban Transport Based on Domestic Solutions» under State Assignment No. 103­00001­25­02 dated 20.03.2025.

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Research purpose: is to propose a formalized approach to the certification procedure of text models of artificial intelligence,
allowing to quantify the safety of such models and their applicability in specific subject areas.
Research methods: description of the certification procedure in the language of mathematical statistics and discrete mathematics.
Research results: three approaches to assessing the quality of text models of artificial intelligence are presented. The first approach is based on calculating the confidence interval, in which with a given probability there is a share of correct answers.
The second approach is based on comparing the model's answers with the answers from a verified set of questions and answers. The third approach is based on a combination of the first two approaches.
Novelty of research: consists in the development of a mathematical models of the certification procedure, the security of textual AI models by the criterion of error admissibility.
Keywords: mathematical model of certification, safety, applicability, subject area, verified set of questions and answers

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Keywords: infrastructural conflict, cybersecurity monitoring, information security management, proactive defense, intelligent analysis system, cognitive modeling.
The purpose of the research to reveal the content of the main terms related to the concept of «infrastructure conflict», its nature and typology, to determine the main indicators and characteristics.
Research method(s): system analysis, predictive modeling, conflictological and riskological analysis.
Result(s) of the study: a new approach to diagnosing and monitoring the destructive impacts of infrastructure genesis is proposed, based on the identification of conflicts both within critical information infrastructure facilities and from outside caused by cyberattacks. A classification of conflicts is proposed, their nature and methods of their identification are determined, and a formalized model of an infrastructure conflict is developed, which makes it possible to diagnose the state of an infrastructure conflict on the basis of indicators of conflict and stability. The development of this methodology will make it possible to move from reactive response to information security incidents to preventive management of the stability of critical information infrastructure facilities, forming intelligent models for forecasting and neutralizing conflict situations. Further development of intelligent analysis systems based on machine learning and cognitive modeling opens up opportunities for managing infrastructure conflicts and makes it possible to create the basis for automated systems for monitoring conflict and determining the stability of critical information infrastructure in real time.
Scientific novelty: the proposed formalized model of infrastructure conflict allows to develop the theory of information security. The identification of conflicts of infrastructural genesis, as exploitable vulnerabilities and threats to information security, on the basis of the considered model, will create a new class of tools for ensuring audit and monitoring of the state of a critical information infrastructure object under the influence of threats of its violation information security. The developed system of indicators of the infrastructure conflict, taking into account the improvement of existing means of information protection and monitoring of cybersecurity of critical information infrastructure facilities, will make it possible to synthesize a new class of information security management systems in real time.

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Keywords: cyber-­physical security, industrial Internet of things, embedded systems, anomaly detection, machine learning, lightweight cryptography, microcontrollers, stochastic models.
Purpose of the study: to develop and justify an integrated approach to ensuring the cyber-­physical security of IIoT microcontroller nodes based on the integration of machine learning and cryptography methods to detect abnormal behavior and attacks in real time.
Methods of research: the use of threat analysis methods for cyber-­physical systems, machine learning for anomaly detection, cryptographic methods for data protection and device identification, as well as simulation and experimental verification on IIoT microcontroller platforms Result(s): the process of functioning of the cyber­-physical security system of microcontrollers in the Industrial Internet of Things is considered as a set of stochastic events of normal and abnormal behavior of IIoT devices formed in the process of data exchange, execution of control commands and exposure to physical factors. The occurrence of anomalies is mode led as a stream of events of deviation of the operating parameters of microcontrollers and network interactions from the reference profiles generated by machine learning methods. As a result of each such event, an intelligent classification of the state of the IIoT node is carried out, followed by the initiation of cryptographically secure response mechanisms aimed at preventing data compromise and disruption of the technological process.
The concept of degradation of the level of cyber­-physical security is introduced, which consists in the accumulation of unrecognized abnormal conditions, leading to a decrease in confidence in data, an increase in vulnerabilities and a violation of the integrity of management. The probability of system degradation is analyzed in the case of stationary and non­stationary flow of abnormal events, depending on the operating modes of the equipment and external influences. It is shown that with time­-varying intensity of anomalies, there is a nonlinear change in the probability of degradation due to the adaptation of machine learning models and the dynamics of cryptographic procedures. For linear and exponential changes in the intensity of abnormal events, dependences have been obtained that make it possible to determine the moments of maximum risk of degradation of the safety system, which are determined by the rate of change in operating conditions and model training parameters. The results obtained make it possible to substantiate the effectiveness of the integrated application of machine learning and cryptography to increase the stability of microcontroller IIoT systems.
Scientific novelty: the concept of degradation of the cyber-­physical security level of Industrial Internet of Things microcontroller systems caused by the accumulation of undetected abnormal conditions is introduced and formalized. The process of functioning of the IIoT cyber­-physical security system in the context of degradation analysis is considered as a stochastic flow of events of normal and abnormal behavior, the intensity of which varies over time. The nature of changes in the probability of degradation with an unsteady intensity of the flow of abnormal events is analyzed, the effect of maximum risk with a subsequent decrease in the probability of degradation due to the adaptation of machine learning models and the dynamics of cryptographic protection mechanisms is revealed.

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Keywords: security analysis; quantitative assessment; information security metrics; vulnerabilities; SLA; remediation discipline; ticketing system; hierarchical aggregation; integral rating.
Purpose of the study: the aim of the study is to develop a system of metrics for analyzing the security of software products and business lines in large enterprise, based on the integration of risk indicators and vulnerability remediation discipline. The model is designed to ensure comparability of objects and provide a managerial interpretation of the security level.
Methods of research: the methodology is based on hierarchical aggregation of ticket­system data (vulnerability remediation ticket → product → business line → organization) and normalization of indicators within the [0;1] range. A multiplicative integration of risk and SLA violations is applied, supplemented by marginal assessments of the contribution of individual vulnerabilities. Validation was carried out using data from a large technology company with the support of an analytical data warehouse.
Result: a metrics model has been developed that enables the transition from raw ticket data to an integral rating (0–100), reflecting the level of security across different layers of the organizational hierarchy. At the product level, the model captures the discipline of vulnerability remediation, including delays and priorities. At the business­line level, aggregated indicators reveal systematic SLA violations and allow for comparisons between teams. At the strategic level, the rating serves as an indicator of the distribution of technical debt and the maturity of interaction processes between information security and development. The pilot implementation covered 112 products and 22 business lines, recording an increase in integral ratings by 10–50 points and a reduction of manual workload for security specialists by approximately 20 %. In the business line initially in the «red zone», the rating increased from 9 to over 50 points within one month, enabling a transition to the «yellow zone». A limitation of the study is the local scope of the pilot: validation was conducted within a single organization, which defines the applied nature of the findings. The obtained results confirm that the proposed model is applicable as a tool for security analysis and can be integrated into managerial practices.
Scientific novelty: the scientific novelty of the study lies in the applied integration of risk indicators and remediation discipline into a unified hierarchical model for security analysis. This approach makes it possible to view security as a manageable process that can be quantitatively compared across products and business lines.

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Keywords: large language models; LLM service; Prompt Injection attacks; attacker model; Markov model; security states; context protection; tool calls; service-­level protection.
Purpose of the study: to develop a method for improving the security of LLM services against Prompt Injection attacks and to provide a quantitative assessment of the impact of LLM Security Proxy on the security state of the service.
Methods of research: the study employs analysis of security threats to large language models, construction of an attacker model and a classification of Prompt Injection attacks, as well as a Markov model of LLM service security states. To evaluate the proposed solutions, modeling of service transition probabilities between secure and compromised states is applied.
Results of the study: a method for improving the security of LLM services against Prompt Injection attacks is proposed, combining an attacker model, a classification of attack classes, the LLM Security Proxy architecture, and a Markov model of security states. It is shown that Prompt Injection attacks are implemented through different mechanisms that violate the secure functioning of an LLM service and therefore require multilayer service­-level protection. The proposed architecture includes semantic analysis of input queries, operating mode control, context management, restriction of tool-­related actions, and output validation. The modeling results show that the use of LLM Security Proxy reduces the probability of transition of the service into critical states S3–S4 and increases the probability of the secure state S1. The most significant effect is observed for attacks on tool calls and for data extraction attacks.
Scientific novelty: the paper proposes an integrated method that combines an attacker model for Prompt Injection attacks, an attack classification, the LLM Security Proxy architecture, and a Markov model of security states. This makes it possible to quantitatively assess the impact of different Prompt Injection mechanisms on LLM service security and the effectiveness of service­-level protection.

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Keywords: Indicators of Compromise, Threat Intelligence, information security, effectiveness evaluation, Security Opera-tions Center, targeted attack.
Purpose of the study: the introduction of indicators of compromise and threat intelligence technologies to increase the security of organizations from targeted cyber attacks in the face of increasing requirements for information protection. The work is aimed at creating an adaptive threat intelligence portfolio, which is a set of data from various sources about existing threats.
Methods of research: the study is based on a systematic analysis of open sources of data on indicators of compromise, methods of their extraction and methods of application in organizing cyber intelligence within the protected infrastructure and systematization of knowledge.
Results: a methodology for systematization and selection of sources of cyber intelligence was proposed, taking into account the quality, relevance and industry specifics, and author's metrics (Coverage, Detection Effect, Integration Speed) were developed to assess the practical effectiveness of integrated indicators. This case study confirms the high performance of the proposed approach to create and implement an adaptive threat intelligence portfolio in the real conditions of a large financial institution. The key success factors, typical difficulties and prospects for applying machine learning to improve the quality of threat intelligence data are considered.
Scientific novelty: consists in the development and testing of an adaptive threat intelligence portfolio, which allows you to timely identify and prevent modern threats in the face of a rapid increase in the number and complexity of attacks. This cybersecurity study has shown the effectiveness of adaptive threat intelligence portfolios, indicators of compromise integration and the need to further develop machine learning algorithms.

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17. Chen S. S., Hwang R. H,, Ali A., Lin Y. D., Wei Y. C, Pai TW. Improving quality of indicators of compromise using STIX graphs // Computers & Security. 2024;144:103972. DOI:10.1016/j.cose.2024.103972.

Keywords: signal-­to-­noise ratio, correlation coefficient, mean squared error, invisibility of embedding, optimization quality indicator, linear programming problem, simplex method.
Purpose of the study: the purpose of this study is to improve the imperceptibility of data embedding in digital images by optimizing the parameters of steganographic algorithms operating in the discrete Fourier transform (DFT) domain.
Methods of research: the study employs discrete Fourier transform–based image steganography, linear programming techniques, including the simplex method, and correlation analysis. A mathematical formulation of the message embedding problem is proposed, where the embedding process is reduced to minimizing a proxy functional related to spectral coefficient modifications. To reduce computational complexity, an energy­-based heuristic for selecting the embedding region is introduced and experimentally validated.
Result(s): A mathematical and conceptual formulation of the message embedding problem in the DFT domain is presented. The optimization objective is defined as the minimization of the sum of absolute changes in the real parts of Fourier coefficients. The original nonlinear optimization problem is transformed into a linear programming problem by eliminating absolute values through additional constraints and solved using the simplex method. A brute­-force algorithm for selecting the embedding location is analyzed, and its computational limitations are discussed. Based on experimental correlation analysis, it is shown that the proposed proxy functional exhibits a strong correlation with spatial­-domain distortion metrics, including mean squared error (MSE) and peak signal-­to­-noise ratio (PSNR). An approximate algorithm for selecting the embedding region based on an energy characteristic of the spectral coefficients is proposed, allowing the search space to be significantly reduced without solving a linear programming problem at each step. Experimental results demonstrate that the proposed approach enables error­free message extraction while maintaining high imperceptibility and acceptable computational complexity.
Scientific novelty: the scientific novelty of this work lies in the formalization of the steganographic embedding problem in the DFT domain as a linear programming problem, as well as in the development of an energy­-based heuristic for efficient selection of the embedding region that preserves a strong correlation with conventional distortion metrics.

1. Melman, A. S., & Evsutin, O. O. Effektivnoe i bezoshibochnoe sokrytie informatsii v gibridnom domene tsifrovykh izobrazhenii s ispolzovaniem metaevristicheskoi optimizatsii [Efficient and error-free information hiding in the hybrid domain of digital images using metaheuristic optimization]. Computer Research and Modeling. 2023. Vol. 15 (1). P. 197–210. DOI: https://doi.org/10.20537/2076-7633-2023-15-1-197-210.
2. Anna Melman, Oleg Evsutin. Comparative study of metaheuristic optimization algorithms for image steganography based on discrete Fourier transform domain. Applied Soft Computing. 2023. Vol. 132. DOI: 10.1016/j.asoc.2022.109847.
3. Richard Apau, Frimpong Twum, Michael Asante, James Ben Hayfron-Acquah. A Secure and High Embedding Capacity Image Steganography Scheme Using Image Enhancement Technique, Compression, and Genetic Algorithm. Proceedings of Ninth International Congress on Information and Communication Technology (ICICT 2024). Р. 307–325. DOI: 10.1007/978-981-97-5441-0_27.
4. Sabyasachi Pramanik, R. P. Singh, Ramkrishna Ghosh. Application of bi-orthogonal wavelet transform and genetic algorithm in image steganography. Multimedia Tools and Applications. 2020. Vol. 79. P. 17463–17482. DOI: 10.1007/s11042-020-08676-1.
5. Aya Jaradat, Eyad Taqieddin, Moad Mowafi. A High-Capacity Image Steganography Method Using Chaotic Particle Swarm Optimization. Security and Communication Networks. 2021. DOI: 10.1155/2021/6679284.
6. Sabyasachi Pramanik, Debabrata Samanta, Samir Kumar Bandyopadhyay, Ramkrishna Ghosh. A New Combinational Technique in Image Steganography. International Journal of Information Security and Privacy (IJISP). 2021. Vol. 15. Iss. 3. DOI: 10.4018/IJISP.2021070104.
7. Zinah Khalid Jasim Jasim, Sefer Kurnaz. An Improved Image Steganography Security and Capacity Using Ant Colony Algorithm Optimization. Computers, Materials & Continua. 2024. 80 (3). P. 4643–4662. DOI: 10.32604/cmc.2024.055195.
8. Ahmed Shihab Ahmed, Hussein Ali Salah. High Security and Robustness Image Steganography Based On Ant Colony Optimization Algorithms and Discrete Cosine Transform. Journal of Education for Pure Science- University of Thi-Qar. 2023. Vol. 13. No. 4. P. 118–150. DOI: 10.32792/jeps.v13i4.370.
9. Narimen Hafsi, Hanaa Hachimi, Djamel Benterki, Yacine Slimani. Genetic-Firefly Algorithm Based Approach in Image Steganography. 2025 11th International Conference on Optimization and Applications (ICOA). DOI: 10.1109/ICOA66896.2025.11236921.
10. Sahil Nokhwal, Manoj Chandrasekharan, Ankit Chaudhary. Secure information embedding in images with hybrid firefly algorithm. 2023 India International Congress on Computational Intelligence. 2024. Vol. 37. P. 27675–27688. DOI: 10.1007/s00521-024-10712-2.
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Keywords: blockchain, stability, quantum­-inspired algorithms, simulated bifurcation, Ising model, quantum annealing, game theory, stability optimization.
Purpose of work is to develop a formalized approach to global optimization of the resilience of national blockchain platforms and ecosystems to classical and quantum attacks based on the QUBO model of cumulative risk and quantum­-inspired simulated bifurcation algorithms.
Research methods: game­-theoretic interpretation of the QUBO risk model as a potential game with the construction of local payoff functions for each variable, development of the GdSB algorithm combining the SB phase stage with local adjustment using the game phase, numerical experimental study of SB, dSB, HdSB, GdSB algorithms and a genetic algorithm based on synthetic QUBO problems of dimension from 20 to 200 variables with an assessment of the quality of the solution and the time to reach the target level.
Results of the study: based on the game­-theoretic interpretation of the QUBO model of cumulative risk as a potential game, a new GdSB (Game-­theoretical discrete Simulated Bifurcation) algorithm has been developed that combines the global search stage through the evolution of the discrete dynamics of the simulated bifurcation with the local optimization stage based on the procedure for choosing the best answer. The algorithm is formalized as a two­-phase scheme, in the first phase, the evolution of spin variables and momenta is performed until a steady state is reached according to dSB dynamics, after which the resulting configuration is converted into a binary representation. In the second phase, a local payoff function is randomly calculated for each variable and the variable value is switched if this leads to a decrease in the global risk function, while the procedure is iterated until local equilibrium is reached. Theoretically, it is proved that in the case of a potential game, which is a QUBO problem, the mentioned procedure with updating variables in any order is guaranteed to converge to a local Nash equilibrium in a finite number of iterations, which ensures the correctness of the second phase of the GdSB algorithm. The experimental results show that GdSB is not inferior to HdSB in terms of the quality of the solutions found and in many modes surpasses it in key indicators, while remaining significantly more effective than the classical heuristics used as the base, with an increase in the problem dimension and an increase in the number of modeled vulnerabilities and protective measures.
Scientific novelty: for the first time, the paper proposes the integration of potential game theory with a quantum-­inspired simulated bifurcation algorithm in the form of the GdSB algorithm, as well as a specialized QUBO model of cumulative risk adapted to the challenges of ensuring the stability of blockchain systems while taking into account complex interactions between vulnerabilities and limited resources.

1. Petrenko A. S. Solving optimization problems of ensuring the quantum stability of national blockchain platforms by the method of quantum annealing // Information protection. Inside. – 2025. – № 3 (123). – Pp. 78–90.
2. Petrenko A. S., Petrenko S. A. A method for assessing the quantum stability of blockchain platforms // Cybersecurity issues. – 2022. – № 3 (49). – Pp. 3–22.
3. Petrenko A. S. Method of analysis of quantum stability of national blockchain ecosystems and platforms // Information protection. Inside. – 2025. – № 2 (122). – Pp. 18–27.
4. Goto H., Endo K., Suzuki M., Sakai Y., Kanao T., Hamakawa Y. and others . High-performance combinatorial optimization based on classical mechanics // Achievements of Science. – 2021. – Volume 7. – Article number eabe7953.
5. Kanao T., Goto H. Modeling of bifurcation using thermal fluctuations // Physics of communication. – 2022. – Volume 5, No. 1. – Article 153.
6. Petrenko A. S. A method for constructing post-quantum EDS algorithms with two hidden groups / A. S. Petrenko // Cybersecurity issues. – 2025. – № 2 (58). – Pp. 52–63. – DOI: 10.21681/2311- 3456-2025-2-52-63.
7. Moldovyan N. A., Petrenko A. S. Algebraic algorithm of EDS with two hidden groups / N. A. Moldovyan, A. S. Petrenko // Cybersecurity issues. – 2024. – No. 6. – Pp. 98–107. – DOI: 10.21681/2311-3456-2024-6-98-107.
8. Petrenko A. S. Podhod k optimizacii ustojchivosti nacional'nyh blokchejn-platform i ehkosistem s pomoshch'yu kvantovo vdohnovlyonnyh algoritmov. chast' 1 // Voprosy kiberbezopasnosti. – 2026. – № 2(72) – S. 101–111. – DOI: 10.21681/2311-3456-2026-2-101-111.

Keywords: blockchain, transaction, block, consensus, cryptography, cryptanalysis, threat, cyber resilience, crypto wallet, identification, authentication.
Purpose: the aim of this work is to construct a mathematical model of cyber-­resilient information systems built on modern blockchain platforms. When constructing a mathematical model, it is important to consider all structural and functional characteristics, both primary and secondary, that may impact the system's reliability.
Method: The research methods are based on the use of information theory, the mathematical apparatus of probability theory and mathematical statistics, the theory of constructing blockchain systems, and the theory of information security.
Results: An analysis of existing approaches to assessing the reliability and security of modern blockchain systems is conducted. A comprehensive approach is proposed that takes into account that the cyber resilience of an information system is influenced by all its components, which are categorized into four levels. A mathematical model of a cyber resilient information system based on a blockchain platform is proposed.
The scientific novelty lies in the use of a comprehensive approach to assessing the reliability of information systems built on the basis of modern blockchain technologies. A mathematical model of a cyber­resilient information system built on a blockchain platform is proposed.

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Keywords: corporate computer network, insider, model, analytical record, indicator, aggregator, feature, artificial intelligence.
The goal of the research: to improve the efficiency of insider detection in corporate computer networks by modeling their characteristics.
Research methods: systems analysis, analytical modeling, hypothetical experiment.
Results: an analytical model of insider characteristics was synthesized. It represents a hierarchical relationship between the following elements, calculated sequentially: atomic indicators of network activity obtained from relevant sources; aggregators of indicators with more comprehensive content, calculated using standard formulas; direct insider features, calculated using specialized formulas; and a final decision on the presence of an insider in the organization. A list of 5 sources, 50 indicators, and 30 aggregators was also compiled (15 of the features were previously proposed by the author) The scientific novelty lies in the fact that, unlike existing models, the proposed model establishes a hierarchical relationship between low­level characteristics of corporate computer network traffic and high­level features of an insider operating within it, allowing for the detection of an insider's presence in qualitatively different ways.

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Keywords: information security; data leak prevention; semantic search; embeddings; ranked list truncation; sensitive information detection.
Purpose of the study: Development and experimental evaluation of a method for detecting sensitive information in data leak prevention (DLP) systems based on semantic vector search with ranked list truncation.
Methods of research: A comparative analysis of traditional text analysis methods (full-­text search, regular expressions, BM25) and semantic vector search was conducted. Text representations were generated using the multilingual-­gte­-base model. Binary decision-­making was performed via ranked list truncation using a global relevance threshold with the application of the TMP Adapter for threshold calibration.
Result(s): The experimental evaluation was conducted across multiple text-­processing scenarios, including long formalized documents, short variable noisy messages, and a streaming search scenario. Detection performance was evaluated using Accuracy, Precision, Recall, and F1 metrics. The results show that the proposed approach significantly outperforms lexical methods across all scenarios. The use of semantic vector representations combined with ranked list truncation pro-vides a more stable balance between detection recall and false positive rates compared to traditional text analysis methods.
Scientific novelty: The scientific novelty lies in formalizing the task of textual data leak detection as a semantic search problem with subsequent transformation of ranking results into a binary decision via adaptive ranked list truncation.

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Keywords: Internet of Things systems, cybersecurity, intrusions, operating model, security, integrated security indicator, normal state profile, resource constraints.
Purpose of the study: analysis of the dependence of the security indicator of distributed Internet of Things systems on the parameters of their operation in the context of cyber threats, as well as the determination of areas for improving security based on a model that takes into account the observed behavior of the system, the accumulation of threat indicators, and the resource state of peripheral nodes.
Research methods: the methods used were system analysis of distributed IoT systems, statistical description of the normal state, assessment of the current deviation of observed behavior from the normal profile, cumulative analysis of threat indicators, normalization of private indicators, and a computational experiment on an open IoT traffic dataset.
Results obtained: a model for the operation of distributed Internet of Things systems under cyberthreat conditions is proposed. This model is designed to quantitatively assess security as an integral property of the research object. The com-position of observed parameters, the normal state profile, the current deviation indicator, the accumulated indicator of threat indicators, and the resource state indicator are determined, which are used to form an integral assessment of the system's security. A computational experiment compared the proposed model with a static baseline scheme that does not take into account adaptive refinement of the normal profile and resource constraints. The obtained results confirm that the proposed model allows not only for a quantitative assessment of the current state of the system but also for identifying ranges of conditions under which security exceeds the level of the baseline scheme.
Scientific novelty: consists in the fact that, in contrast to approaches focused primarily on detecting anomalies and intrusions or on assessing the quality of individual classifiers, the proposed model introduces basic and minimum acceptable levels of security and allows for quantitative determination of the increase in security relative to the basic scheme under various operating conditions.
Contribution: Igor Kotenko and Igor Saenko contributed to the problem formulation, the definition of the scientific and methodological requirements for the study, and the scientific editing of the paper. Valeriy Lipatnikov proposed the research direction, participated in the problem formulation, and supervised the study. Ilia Andreev carried out the computational experiment, processed the results, and participated in preparing the text of the article.

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Keywords: information security model, assessment of complex systems, logical­probabilistic method, theory of relations, system analysis.
The purpose of the article: adaptation of the logical-­probabilistic method of evaluating complex systems to the tasks of building information security systems in a multi­agent system.
Research method: during the research, the main provisions of the methodology of structural analysis, system analysis, decision theory, methods of evaluating events under the condition of incomplete information were used.
The result: this article concludes the consideration of information security issues based on an analysis of the relationship between the subjects and the object of protection. The probabilistic parameters of the logic algebra function have been developed, which make it possible to give quantitative and qualitative assessments of the possibility of a subject achieving the ultimate goal of his activity. It is shown that these parameters can be determined on the basis of the corresponding structure of relations and estimates of the states of relations of agents on these relations. The results obtained provide a reasonable calculation and use of estimates of the subject's capabilities from the point of view of information security based on the application of the logical­-probabilistic method.
Scientific novelty: consideration of information security issues using the apparatus of mathematical and logical relations. Methods have been developed for quantifying the capabilities of a subject to achieve the ultimate goal of his activity based on the agents' information resources available to him. It is shown that the developed probabilistic parameters of the algebra function of logic for achieving the ultimate goal of the subject can be used as initial data for predicting the actions of the subject, including from the point of view of estimating the required resources or choosing the optimal way to achieve the goal based on the availability and accessibility of these resources. Methods have been developed to obtain integral estimates of the possibilities of achieving the ultimate goal of a subject for managing the structure of information flows and information resources both at the level of individual agents and various subsystems of modern information systems and systems as a whole. The conclusion is substantiated that the probability of success of an entity's operations can be obtained as a result of its assessment of the current state of relations between IP agents based on data on the functioning of information resources and the flows of these agents. It is shown that when determining the success of a subject's operations, the arguments at his disposal «against» the current state of relations are preferable to the arguments «for».

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Keywords: control, phase space of states, quasi-­attractors, chaotic dynamics, cyber incident prediction, Shewhart control charts, security data analysis.
Research Objective: To develop a new methodological approach to the analysis and management of information security (IS) risks that overcome the limitations of traditional statistical and deterministic methods in the context of the nonlinearity of digital systems and the exponential growth of threats. The study aims to ensure the early detection of crisis trends and the transition from reactive to proactive IS management.
Research method: the apparatus of chaotic dynamics theory, including the construction of a phase space of states (PSS) and the identification of quasi­-attractors, as well as the integration of PSS analysis with Shewhart control charts.
Result: The proposed approach allows the information security system to be viewed as a complex dynamic system, the evolution of which is described by a trajectory in a multidimensional phase space of key security parameters. The concept of information security quasi­-attractors is introduced—regions of relative stability where the system remains controllable despite constant fluctuations and attacks. A model example analyzing the dynamics of information attacks on the corporate sector demonstrates that the method enables the prediction of critical transitions (bifurcations), such as the onset of a large­scale campaign of targeted attacks or a transition to uncontrolled growth in incidents, which opens up the possibility of timely preemptive action against risks before irreversible consequences occur.
Scientific novelty: The use of the concepts of quasi-­attractors and phase space of states is proposed for describing and monitoring the stability of information security systems. The novelty lies in shifting the focus from the analysis of static indicators to the study of the dynamics of changes (rate and acceleration) in security parameters, integrating methods from chaos theory and statistical quality control.

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Keywords: cluster protection model, automation of architecture synthesis, heterogeneous oriented multigraph, ML model.
The purpose of the study: to propose an approach to the synthesis of constructive information protection in the context of a quantum threat based on a cluster protection model, a logical inference model and a heterogeneous graph neural network.
Research methods: mathematical logic, machine learning, theory of modular cluster networks, graph theory, matrix theory.
Research result: the concept of a technology for synthesizing constructive information protection based on the integration of a cluster protection model with full overlap, a graph neural network for building and optimizing a multigraph according to constructive protection criteria with a formal proof of architecture in a logical inference machine is proposed. The structural­-parametric synthesis is provided by describing the system model in terms of a modular­-cluster multigraph. The accuracy of the model is achieved by the formal representation of protection measures, access policies, and protection criteria by the cluster structure of a multigraph and the decomposition of information links into interaction levels through which information processing and protection functions are implemented, as well as by the formal analysis of the fulfillment of protection requirements through the assessment of states in a logical inference machine using preprocessing in a machine learning model. The results can be used to build systems for synthesizing constructive protection for distributed registries and other critical information systems, taking into account the declared access policy, regulatory requirements, and the risk level of a quantum threat, which is formally taken into account in the cluster model and controlled by means of structural parametric optimization with the possibility of modifying post­quantum cryptography.
Scientific novelty: a technology for synthesizing constructive protection taking into account the risk level of a quantum threat is proposed based on the integration of machine learning methods with structural and parametric optimization of the system model presented as a heterogeneous oriented multigraph, and proof of architecture security by checking the compliance of subgraphs with formal protection criteria in a logical inference machine.

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