A Hybrid Neural Network Transformer for Detecting and Classifying Destructive Content in Digital Space

Abstract

Cybersecurity remains a key challenge in the development of intelligent telecommunications systems and the Internet of Things (IoT). The growing destructive impact of the digital environment, coupled with high-performance computing (HPC), requires the development of effective countermeasures to ensure the security of the digital space. Traditional approaches to detecting destructive content are primarily limited to static text analysis, which ignores the temporal dynamics and evolution of destructive impact scenarios. This is critical for monitoring tasks in the digital environment, where threats rapidly evolve. To overcome this limitation, this study proposes a hybrid architecture, Hyb-TKAN, based on adaptive algorithms that account for the temporal component and nonlinear dependencies. This approach enables not only the classification of destructive messages but also the analysis of their development and transformation over time. Unlike existing studies, which focus on individual aspects of aggressive content, the model utilizes multilayered data analysis to identify hidden relationships and nonlinear patterns in destructive messages. The integration of these components ensures high adaptability and accuracy of text processing. The presented approach was implemented in a multi-class classification task with evaluation based on real text data. The obtained results demonstrate improved classification accuracy. In the Experimental Analysis Section, the results are compared with the closest modern analogs, confirming the relevance and competitiveness of the proposed hybrid neural network.