Mendeley
TY  _  JOUR
ID  -  2023102244500
TI  -  FLHB-AC: Federated Learning History-Based Access Control Using Deep Neural Networks in Healthcare System
JO  -  Journal of Information Systems and Telecommunication (JIST) 
JA  -  ES
LA  -  en
SN  -  2322-1437
AU  -   
AU  -  Rezakhani Afshin
AU  -  Haj Seyyed Javadi Hamid
AU  -   
AD  -  Ayatollah Borojerdi
AD  -  Islamic Azad University, Boroujerd branch, Iran
AD  -  دانشگاه شاهد
AD  -  Parvaneh Asghari, Faculty of Engineering, Central Tehran Branch, Islamic Azad University
Y1  -  2024
PY  -  2024
VL  -  46
IS  -  
SP  -  90
EP  -  104
KW  -  Healthcare System
KW  -   History-based Access control
KW  -   Intelligent Module
KW  -   Deep Recurrent Networks
KW  -   Federated Learning 
DO  -  
N2  -  <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>
UR  - rimag.ir/en/Article/44500
L1  - rimag.ir/en/Article/Download/44500
ER  -  
