Football cybersecurity threat severity prediction using multi-head transformer-based deep learning models

Cybersecurity incidents targeting professional football organizations pose significant operational, financial, and reputational risks. Accurate assessment of incident severity is therefore essential for effective prioritization and response. This study proposes a Multi-Head Transformer–based neural network for predicting cybersecurity incident severity scores in football ecosystems using heterogeneous tabular data. The model captures complex interactions among technical attack characteristics and football-specific contextual features, including club and league attributes. The proposed approach is evaluated against established machine learning baselines, including Random Forest and XGBoost. Experimental results demonstrate that the Transformer model consistently outperforms baseline methods, achieving high predictive accuracy with strong generalization performance. Robustness is validated through strict data partitioning, 5-fold cross-validation, and multiple random seeds. Furthermore, SHAP-based explainability analysis confirms that predictions are driven by multiple interacting features rather than any single dominant variable. The results indicate that the proposed framework provides a reliable and interpretable solution for severity assessment in football cybersecurity environments. By enabling data-driven incident prioritization and informed decision-making, this work contributes a practical and scalable tool for enhancing cyber resilience in professional football organizations.