A Hybrid KAN-BiLSTM Transformer with Multi-Domain Dynamic Attention Model for Cybersecurity

With the exponential growth of cyberbullying cases on social media, there is a growing need to develop effective mechanisms for its detection and prediction, which can create a safer and more comfortable digital environment. One of the areas with such potential is the application of natural language processing (NLP) and artificial intelligence (AI). This study applies a novel hybrid-structure Hybrid Transformer–Enriched Attention with Multi-Domain Dynamic Attention Network (Hyb-KAN), which combines a transformer-based architecture, an attention mechanism, and BiLSTM recurrent neural networks. In this study, a multi-class classification method is used to identify comments containing cyberbullying features. For better verification, we compared the proposed method with baseline methods. The Hyb-KAN model demonstrated high results on the multi-class classification dataset, achieving an accuracy of 95.25%. The synergy of BiLSTM, Transformer, MD-DAN, and KAN components provides flexibility and accuracy of text analysis. The study used explainable visualization techniques, including SHAP and LIME, to analyze the interpretability of the Hyb-KAN model, providing a deeper understanding of the decision-making mechanisms. In the final stage of the study, the results were compared with current research data to confirm their relevance to current trends.