Abstract
Due to the fast-pacing development of technology in the healthcare domain, many problems arise surrounding the security and privacy preservation of medical data. Secure authentication on the Internet of Medical Things (IoMT) is essential. The lack of security in critical and sensitive information of IoMT may lead to high-risk issues in patient privacy. When new data is transmitted from the sensor node, it cannot be assured as authenticated data. Therefore, a blockchain-based system is needed. Such a system allows healthcare providers to access the health records of patients in a more secured authentication-based approach across various network connections. In this paper, a new secure authentication approach using machine learning is proposed. To identify the dynamic time attack detection and authentication in an IoMT environment, this work implements K-Nearest neighbour (KNN) and machine learning using smart contract (KNN-MLSC). It improves security, reduces latency, and maintains health data privacy for both physicians and patients. The accuracy of KNN-MLSC got 0.96 compared with KNN using a smart contract. Also, the results showed that KNN-MLSC has the minimum computation time.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.