Forecasting Information Operations with Hybrid Transformer Architecture
DOI:
https://doi.org/10.20535/tacs.2664-29132024.2.320024Abstract
Proactive 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.
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