[en] Cyber-security intelligence have made a great impact over healthcare industry where several researchers are developing new techniques to improve security for healthcare systems. Besides, Artificial Intelligence (AI) become the tremendous technology in recent decades to improve the existing methods to be more intelligent. In this paper, we proposed cyber attack detection system for healthcare sector with centralized and federated transfer learning mode. Edge of Things (EoT) framework is developed in connection with cloud and healthcare sectors to transmit the data efficiently and the proposed Centralized with Multi-Source Transfer Learning (CMTL) algorithm which is used for detection and classification of various threats such as information gathering, DoS/DDoS attacks, Malware attacks, Injection attacks, and Man in the Middle attacks. Performance of the proposed framework is evaluated using various datasets such as EMNIST, X-IIoTID, and Federated TON_IoT. Our framework outperforms with the analysis of execution time and obtains high level accuracy when compared with different algorithms.
Disciplines :
Computer science
Author, co-author :
Chakraborty, Chinmay; Electronics and Communication Engineering, Birla Institute of Technology, India
NAGARAJAN, Senthil Murugan ✱; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Mathematics (DMATH)
Devarajan, Ganesh Gopal; Department of Computer Science and Engineering, SRM Institute of Science and Technology Delhi-NCR Campus, India
Ramana, T V; Department of Computer Science and Engineering, Jain University, India
Mohanty, Rajanikanta; Department of Computer Science - Software Engineering, Jain University, India
✱ These authors have contributed equally to this work.
External co-authors :
yes
Language :
English
Title :
Intelligent AI-based Healthcare Cyber Security System using Multi-Source Transfer Learning Method
