Enhancing Banking Cybersecurity: An Ensemble-Based Predictive Machine Learning Approach

In this study, we propose a predictive cybersecurity framework for the banking sector by integrating ensemble-based machine learning models. Our approach leverages heterogeneous datasets—including internal firewall and intrusion detection system logs, banking transaction records, user behavior data, and external threat intelligence—to capture a comprehensive view of the cyber threat landscape. Following rigorous data preprocessing, feature selection, and feature engineering, we evaluated multiple models, including Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, and Deep Neural Networks. Comparative analysis revealed that while advanced individual models demonstrated strong predictive capabilities, the Ensemble Model consistently outperformed all others, achieving an accuracy of 92% and a ROC-AUC of 94%. These results underscore the model’s superior ability to minimize false negatives, which is critical for safeguarding financial assets. Our findings advocate for the adoption of ensemble techniques in real-world banking cybersecurity applications, providing a robust, scalable solution that adapts to evolving threat patterns while significantly enhancing detection performance.