A comprehensive secure system enabling healthcare 5.0 using federated learning, intrusion detection and blockchain

Recently, the use of the Internet of Medical Things (IoMT) has gained popularity across various sections of the health sector. The historical security risks of IoMT devices themselves and the data flowing from them are major concerns. Deploying many devices, sensors, services, and networks that conn...

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Bibliographic Details
Published in:PeerJ. Computer science 2024-01, Vol.10, p.e1778, Article e1778
Main Authors: Almalki, Jameel, Alshahrani, Saeed M, Khan, Nayyar Ahmed
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Bioinformatics
Blockchain
Computational linguistics
Cryptography
Data mining
Data Mining and Machine Learning
Disease
Federated learning
Health care
Healthcare5.0
Hospitals
Information storage
Information storage and retrieval
Internet of medical things
Internet of Things
IoMT
Language processing
Machine learning
Medical personnel
Natural language interfaces
Network security
Patients
Privacy
Professionals
Security and Privacy
Sensors
Wireless networks
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Summary:Recently, the use of the Internet of Medical Things (IoMT) has gained popularity across various sections of the health sector. The historical security risks of IoMT devices themselves and the data flowing from them are major concerns. Deploying many devices, sensors, services, and networks that connect the IoMT systems is gaining popularity. This study focuses on identifying the use of blockchain in innovative healthcare units empowered by federated learning. A collective use of blockchain with intrusion detection management (IDM) is beneficial to detect and prevent malicious activity across the storage nodes. Data accumulated at a centralized storage node is analyzed with the help of machine learning algorithms to diagnose disease and allow appropriate medication to be prescribed by a medical healthcare professional. The model proposed in this study focuses on the effective use of such models for healthcare monitoring. The amalgamation of federated learning and the proposed model makes it possible to reach 93.89 percent accuracy for disease analysis and addiction. Further, intrusion detection ensures a success rate of 97.13 percent in this study.
ISSN:2376-5992
2376-5992
DOI:10.7717/peerj-cs.1778