مرکز منطقه ای اطلاع رسانی علوم و فناوری Journal of Information Systems and Telecommunication (JIST) 2322-1437 12 46 2024 6 24 FLHB-AC: Federated Learning History-Based Access Control Using Deep Neural Networks in Healthcare System FLHB-AC: Federated Learning History-Based Access Control Using Deep Neural Networks in Healthcare System 90 104 10.61186/jist.44500.12.46.90 en Nasibeh Mohammadi Islamic Azad University Afshin Rezakhani Ayatollah Borojerdi Hamid Haj Seyyed Javadi Shahed University Parvaneh asghari Central Tehran Branch, Islamic Azad University 2023 10 22 <p>Giving access permission based on histories of access is now one of the security needs in healthcare systems. However, current access control systems are unable to review all access histories online to provide access permission. As a result, this study first proposes a method to perform access control in healthcare systems in real time based on access histories and the decision of the suggested intelligent module. The data is used to train the intelligent module using the LSTM time series machine learning model. Medical data, on the other hand, cannot be obtained from separate systems and trained using different machine-learning models due to the sensitivity and privacy of medical records. As a result, the suggested solution employs the federated learning architecture, which remotely performs machine learning algorithms on healthcare systems and aggregates the knowledge gathered in the servers in the second phase. Based on the experiences of all healthcare systems, the servers communicate the learning aggregation back to the systems to control access to resources. The experimental results reveal that the accuracy of history-based access control in local healthcare systems before the application of the suggested method is lower than the accuracy of the access control in these systems after aggregating training with federated learning architecture.</p> http://jist.ir/fa/Article/Download/44500