Machine learning and smart card based two-factor authentication scheme for preserving anonymity in telecare medical information system (TMIS)

Gupta, BB; Prajapati, V; Nedjah, N; Vijayakumar, P; El-Latif, AAA; Chang, X

Machine learning and smart card based two-factor authentication scheme for preserving anonymity in telecare medical information system (TMIS)

Gupta, BB Prajapati, V Nedjah, N Vijayakumar, P El-Latif, AAA Chang, X

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Publisher:: Springer
Publication Type:: Journal Article
Citation:: Neural Computing and Applications, 2022, 35, (7), pp. 5055-5080
Issue Date:: 2022-01-01

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Field	Value	Language
dc.contributor.author	Gupta, BB
dc.contributor.author	Prajapati, V
dc.contributor.author	Nedjah, N
dc.contributor.author	Vijayakumar, P
dc.contributor.author	El-Latif, AAA
dc.contributor.author	Chang, X https://orcid.org/0000-0002-7778-8807
dc.date.accessioned	2023-03-28T09:16:18Z
dc.date.available	2023-03-28T09:16:18Z
dc.date.issued	2022-01-01
dc.identifier.citation	Neural Computing and Applications, 2022, 35, (7), pp. 5055-5080
dc.identifier.issn	0941-0643
dc.identifier.issn	1433-3058
dc.identifier.uri	http://hdl.handle.net/10453/168741
dc.description.abstract	Telecare medical information system (TMIS) is used to connect patients and doctors who are at a different location from each other. The authentication of the user and system is very crucial as the medical data of the user is stored on the server. Many systems have been developed in order to achieve this goal. We show some vulnerabilities of existing systems in this paper. We then propose a secure authentication mechanism to achieve the same goal. Machine learning and the nonce-based system is used for authentication of the entity and to prove the freshness of transmitted messages. Smart card blocking mechanisms have been included in each phase of the proposed system to prevent unauthorized access of data. The proposed system has been evaluated formally with the AVISPA tool. Then the proposed model has also been checked against different attacks and evaluated for different functionalities. We provide relative analysis with some recently proposed models and show our proposed system is relatively more efficient and secure.
dc.language	English
dc.publisher	Springer
dc.relation.ispartof	Neural Computing and Applications
dc.relation.isbasedon	10.1007/s00521-021-06152-x
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 0906 Electrical and Electronic Engineering, 1702 Cognitive Sciences
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Machine learning and smart card based two-factor authentication scheme for preserving anonymity in telecare medical information system (TMIS)
dc.type	Journal Article
utslib.citation.volume	35
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	1702 Cognitive Sciences
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2023-03-28T09:16:12Z
pubs.issue	7
pubs.publication-status	Published
pubs.volume	35
utslib.citation.issue	7

Abstract:

Telecare medical information system (TMIS) is used to connect patients and doctors who are at a different location from each other. The authentication of the user and system is very crucial as the medical data of the user is stored on the server. Many systems have been developed in order to achieve this goal. We show some vulnerabilities of existing systems in this paper. We then propose a secure authentication mechanism to achieve the same goal. Machine learning and the nonce-based system is used for authentication of the entity and to prove the freshness of transmitted messages. Smart card blocking mechanisms have been included in each phase of the proposed system to prevent unauthorized access of data. The proposed system has been evaluated formally with the AVISPA tool. Then the proposed model has also been checked against different attacks and evaluated for different functionalities. We provide relative analysis with some recently proposed models and show our proposed system is relatively more efficient and secure.

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http://hdl.handle.net/10453/168741