A method for semantic graph analysis of the evolution of internet fraud is proposed based on the construction and comparative analysis of temporal semantic networks. The study is conducted using a corpus of 51,755 thematic documents retrieved from the InfoStream information system covering the period 2011–2025. A Retrieval-Augmented Generation (RAG)-based workflow was employed to retrieve relevant document contexts and support their semantic processing during network construction. Temporal semantic networks representing the conceptual structure of internet fraud at successive stages of its evolution were constructed for three five-year intervals, together with an integral semantic network characterizing the stable conceptual core of the subject domain.
The topological characteristics of the networks were analyzed using the framework of graph theory—specifically degree, betweenness, and eigenvector centrality—enabling the quantitative assessment of structural changes, the identification of concepts with a high transformational role, and the tracking of thematic cluster evolution. It is demonstrated that the development of internet fraud is predominantly evolutionary in nature, occurring through the restructuring of existing semantic structures rather than their complete replacement.
Building upon this foundation, a hypothesis of structural continuity for semantic networks is formulated, and a model for forecasting their evolution is proposed. This model involves estimating the structural transformation operator, predicting the emergence of new concepts and semantic relationships, and constructing the forecasted network for the subsequent time interval. The validity of the proposed approach is confirmed through retrospective validation, which entails forecasting semantic networks for the subsequent period using solely data from preceding temporal slices, followed by a comparison between the forecasted and actual structures. The obtained results demonstrate the potential of utilizing temporal semantic-graph analysis as a tool for the early detection of structural changes in the information space and the forecasting of new trends in the development of internet fraud. The retrospective validation confirmed the model's high predictive capability, achieving a Precision of 0.870, a Recall of 0.816, and an F1-score of 0.842 for the 2021–2025 forecast interval, alongside a strong rank correlation (ρ = 0.811) in preserving node centrality. These quantitative results demonstrate the practical validity of temporal semantic-graph analysis as a tool for the early detection of structural changes and the forecasting of new trends in the development of internet fraud.

The study is devoted to assessing the risks of cyber threats in the future based on expert sampling patterns. One of the key problems of modern cybersecurity is the dynamic nature of threats that change under the influence of technological progress and socio-economic factors. In this context, the authors consider a methodological approach that involves the use of a multi-level analysis of expert opinions. The main emphasis is placed on taking into account the different points of view, experience and professional activities of experts from the public, private and academic sectors. An important stage of the study is the procedure of data cleaning to form a representative sample that takes into account only logically consistent responses of experts. The paper focuses on the integration of the expert sample patterns‘ features. The key differences in threat assessments between different groups of experts depending on their professional role and experience are identified. This made it possible to formulate comprehensive recommendations for strategic cyber risk management focused on both short-term and long-term priorities. The study makes a significant contribution to understanding the peculiarities of cyber risk assessment through the use of multivariate analysis of expert opinions. The proposed methodology allows not only to improve the quality of forecasts of future cyber threats, but also contributes to the creation of adaptive cybersecurity strategies that take into account the specifics of each sector. The findings of the study emphasize the importance of a multidimensional approach to analyzing cyber threats, taking into account the specifics of each expert group. Integration of assessments and consideration of local peculiarities are key to the development of adaptive and effective cyber defense strategies focused on global and local challenges.