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• MODELING OF SECURITY THREATS FOR BUILDING A COMPREHENSIVE INFORMATION PROTECTION SYSTEM AT OBJECT OF INFORMATIZATION

  I. А. Eremin , А.Е. Yakushina , I.L. Shcherbov

  41-54

  2025-07-24

  Abstract ▼

  Within the framework of this study, the typical structure of the informatization facility was analyzed in detail, which allowed qualified specialists to better understand the mechanisms and aspects through which various categories of objects and subjects of information processing that may be subject to security threats. The main mechanism for building a comprehensive information security system is the threat model. This model is aimed at identifying and identifying potential threats, their subsequent analysis and minimizing the risks of their implementation associated with damage to the informatization facility. In the framework of this study, the domestic FSTEC knowledge base and the international ATT&CK and CAPEC knowledge bases are considered to build a threat model. They contain comprehensive information about the tactics and techniques used by intruders in carrying out attacks on informatization facilities. In the course of the research, various tactics used by the attackers were classified in detail. Special attention was paid to the definition of the main tactics that determine the entry points of the informatization object, which are used to further carry out the attack. In the context of developing an effective threat model, it seems advisable to conduct a comprehensive analysis of the data contained in knowledge bases and their subsequent joint use in the process of building a threat model at informatization facilities. This approach makes it possible to systematize and structure information, which contributes to a more accurate and reasonable construction of a model for the implementation of potential threats at different stages of an attack on an informatization facility. To build a comprehensive information security system, a decision support system was considered. The analysis of modern scientific research devoted to the applied methods in the construction of support systems is carried out. As a result of the work, the relationship between knowledge bases of tactics and techniques, as well as well-known vulnerabilities, was shown using the ontology method, which allows us to build a model of a complex threat attack, and identify the targets affected by an attacker at various stages of a complex attack, the criticality of the vulnerability used and the platform on which this vulnerability is implemented, and the definition of negative consequences

• A STOCHASTIC FRAMEWORK FOR MODELING TRADERS’ COGNITIVE RISK UNDER VOLATILITY IN DECENTRALIZED FINANCIAL MARKETS

  D. G. Veselova , N. Е. Sergeev

  189-199

  2025-12-30

  Abstract ▼

  This study is devoted to the development of a stochastic model of traders’ cognitive risk as a core component of an intelligent decision support system (DSS) for decentralized cryptocurrency markets.
  The relevance of the research is determined by the specific characteristics of the DeFi environment, which include high and nonstationary volatility, the absence of centralized stabilization mechanisms, information asymmetry, and a strong influence of behavioral factors on trading decisions. Under these conditions, traditional deterministic and static DSS frameworks demonstrate limited effectiveness, as they fail to account for the dynamic perception of risk by market participants and the associated cognitive biases.
  The objective of this research is to formalize traders’ cognitive risk as a memory-dependent stochastic process and to integrate the proposed model into the architecture of an adaptive DSS for risk management. To achieve this objective, a stochastic differential equation is developed to describe the dynamics of cognitive risk as a function of market volatility and prevailing market regimes. In addition, a probabilistic transition kernel is introduced to link objective market characteristics with the subjective perception of risk. For parameter estimation, an identification framework based on the Expectation–Maximization algorithm combined with particle filtering is proposed, enabling robust inference in the presence of nonlinear dynamics and latent state variables. The research methodology includes numerical simulations on synthetic data, parameter estimation using real cryptocurrency time series, and validation of the proposed approach through walk-forward and purged K-fold schemes. The quality of probabilistic forecasts is evaluated using the Negative Log-Likelihood (NLL), Brier Score, and Expected Calibration Error (ECE) metrics. Experimental results demonstrate that incorporating the stochastic cognitive layer improves probabilistic forecasting performance by an average of 10–15%, reduces NLL by approximately 8%, decreases the Brier Score by about 11%, and lowers ECE by nearly 35%. Furthermore, the accuracy of predicting key transitions between market regimes increases by 5–7 percentage points. The obtained results confirm the effectiveness of the proposed stochastic cognitive-risk model and demonstrate its applicability for the development of adaptive DSS solutions in the DeFi domain. The proposed framework provides a foundation for further research on predictive models of trader behavior and the design of intelligent risk-management systems for decentralized financial ecosystems.

• SEMANTIC ANALYSIS AND INTEGRATION OF HETEROGENEOUS INFORMATION STREAMS IN DECISION SUPPORT SYSTEMS: A TECHNOLOGY REVIEW

  V. V. Gapochka , Е. Е. Polupanova

  2026-02-27

  Abstract ▼

  Modern decision support systems (DSS) increasingly rely on heterogeneous data streams from IoT sensors, databases, text messages, and social media, represented in different formats and characterized by diverse semantic models and quality levels. The lack of semantically aligned integration results in inconsistent entity interpretation, duplication, and loss of context, which reduces the quality and timeliness of decisions. The aim of this paper is to systematize methods for semantic analysis and integration of heterogeneous information streams in DSS and to identify their benefits, limitations, and application domains. The study is conducted as an analytical review of publications from 2018–2025 focusing on semantic interoperability, ontologies and knowledge graphs, multi-source data fusion, data federation, and real-time stream processing. The review shows that semantic compatibility is primarily achieved through ontologies and knowledge graphs that define shared entities and identifiers and provide a flexible integration schema. For real-time decision-making, hybrid solutions combining a semantic layer with data fusion algorithms and source trust assessment are the most effective; published case studies report accuracy gains of about 15–20% and response-time reductions of up to 70–80% in multi-source settings. For unstructured streams, NLP and machine learning play a key role by extracting entities and relations and enabling semantic enrichment. The results can be used to design DSS for smart city, industrial, and healthcare domains. Furthermore, the paper highlights the role of standards like SHACL for validation and SPARQL for querying, enhancing the practical applicability of semantic approaches. Future directions include automating ontology alignment to reduce labor costs and integrating with AI for dynamic adaptation to new data sources.

• RISK‑ORIENTED GEOPORTAL DECISION SUPPORT SYSTEM FOR TERRITORIALLY DISTRIBUTED ORGANIZATIONAL SYSTEMS

  А.М. Bershadsky , S.А. Yamashkin

  278-297

  2026-07-07

  Abstract ▼

  The article discusses the development of a risk-oriented geoportal decision support system for territorially distributed organizational systems (TDOS). The aim of the work is to develop an architecture and a formalized model that integrates spatial data, risk structures, key performance indicators (KPIs), and management action options within a unified analytical framework. The relevance is due to the fact that classical DSS and geoportals fragmentarily cover the tasks of TDOS management due to the lack of integration of spatial analysis with risk cascading models, which leads to inconsistency of decisions and increased territorial vulnerability. The methodological foundation includes the formalization of "object-risk-indicator-impact" relationships, the construction of directed influence graphs, and mechanisms for the propagation of risk effects across the territory and management levels. A multi-layered architecture of the geoportal platform is proposed, including subsystems for spatial data collection, risk analytics, KPI dashboards, and scenario modeling. The technical implementation is based on open geoportals of the Russian Geographical Society and a unified spatial data repository. As a pilot area, the regional management system for development and natural resource use of the Republic of Mordovia was studied, where a prototype of the risk-oriented module was implemented. The article demonstrates the ability to visualize the distribution of natural and man-made risks, assess integral territorial vulnerability indices, and select priority management scenarios. The results of implementation at EM-KAT LLC showed a reduction in energy consumption. The application of the system in the Main Directorate of the Ministry of Emergency Situations for the Republic of Mordovia made it possible to optimize territorial management and increase risk predictability. The proposed approach ensures holistic and reproducible management of territorially distributed systems, increasing their resilience to local and transitive impacts, decision-making transparency, and the efficiency of interdepartmental coordination