Forecasting Information Operations with Hybrid Transformer Architecture

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dc.contributor.authorFeher, Anatolii
dc.date.accessioned2025-04-10T09:04:46Z
dc.date.available2025-04-10T09:04:46Z
dc.date.issued2024
dc.description.abstractProactive decision-making in all processes is difficult to imagine without forecasting methods, especially in the field of cybersecurity where the speed and quality of response are often critical. For this reason, we proposed a unique methodology based on a new hybrid architecture Transformer that perfectly captures long-term dependencies and an adaptive algorithm ACWA that quantifies historical patterns. Thus, the described approach considers short-term fluctuations, long-term trends, and seasonal patterns more effectively than traditional forecasting models, as demonstrated by the application of Information Operations and Disinformation occurrences time series forecasting.
dc.format.pagerangeP. 83-88
dc.identifier.citationFeher, A. Forecasting Information Operations with Hybrid Transformer Architecture / Anatolii Feher // Theoretical and Applied Cybersecurity: scientific journal. – 2024. – Vol. 6, No. 2. – P. 83-88. – Bibliogr.: 12 ref.
dc.identifier.doihttps://doi.org/10.20535/tacs.2664-29132024.2.320024
dc.identifier.urihttps://ela.kpi.ua/handle/123456789/73326
dc.language.isoen
dc.publisherIgor Sikorsky Kyiv Polytechnic Institute
dc.publisher.placeKyiv
dc.relation.ispartofTheoretical and Applied Cybersecurity: scientific journal, Vol. 6, No. 2
dc.subjecttime series forecasting
dc.subjecttransformer models
dc.subjectadaptive contextual weighted average
dc.subjectOSINT
dc.subject.udc004.05
dc.titleForecasting Information Operations with Hybrid Transformer Architecture
dc.typeArticle

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Theoretical and Applied Cybersecurity: scientific journal, Vol. 6, No. 2