The Development of a Data Protection System in Healthcare Using Deep Learning Models

Abstract

This research addresses the critical challenge of cybersecurity in healthcare by evaluating the effectiveness of machine learning (ML) and deep learning (DL) models in identifying and mitigating five significant cybersecurity threats: distributed denial-of-service (DDoS), man-in-the-middle (MitM), malware, phishing, and SQL injection. The study integrates a secure hardware–software architecture utilizing WireGuard, a lightweight, modern VPN protocol that establishes encrypted tunnels between network nodes, ensuring robust data integrity, confidentiality, and authenticated communication. Two ML models, support vector machine and random forest, and four DL architectures, dense neural networks (DNNs), convolutional neural networks-long short-term memory (CNN-LSTM), and LSTM-gated recurrent unit (LSTM-GRU), are systematically trained and tested using publicly available datasets specific to each threat category. The experimental outcomes demonstrate exceptional detection capabilities for structured network threats, with DNN and CNN-LSTM achieving accuracies and F1-scores from 95% to 97.6% for DDoS and MitM threats. In malware classification, the performance of DNN and CNN maintains precision and recall above 94%. Phishing and SQL injection attacks have lower classification scores of around 82% for most models. Visual analytics, including accuracy, loss plots, and confusion matrices, provide valuable insights into the convergence behaviors and sensitivity of different architectures, highlighting the strong generalization of DNN and variability in recurrent models. Overall, this research highlights the substantial potential of DL, combined with secure communication technologies like WireGuard, in enhancing healthcare cybersecurity, while also identifying areas for further development and optimization.