Optimizing DoS Attack Detection in Healthcare Systems Through Ensemble Learning

The healthcare industry is increasingly vulnerable to cyberattacks, particularly Denial of Service (DoS) attacks, because of the heavy use of digital technology in this field. Serious and far-reaching consequences may result from these attacks disrupting healthcare services. In this study, it can be seen how well machine learning (ML) algorithms can detect healthcare DoS threats. With the use of Extreme Gradient Boosting (XGBoost), Gradient Boosting Machine (GBM), Light Gradient Boosting Machine (LightGBM) and Random Forest (RF), these attacks can be identified and eradicated these threats. The technique collects data on the network traffic of healthcare systems, and then extracts and selects features from that data to identify important signals of DoS attacks. The performance of each model is evaluated using precision, accuracy, recall, and F1-score. Due to their speed and excellent detection accuracy, XGBoost and RF are the ideal models for healthcare security infrastructures, according to the results. This study emphasizes the importance of new ML methods in enhancing healthcare systems' resilience to cyber attacks and ensuring the safety and maintenance of critical healthcare services.