A two‐layer attack‐robust protocol for IoT healthcare security

Abstract The majority of studies in the field of developing identification and authentication protocols for Internet of Things (IoT) used cryptographic algorithms. Using brain signals is also a relatively new approach in this field. EEG signal‐based authentication algorithms typically use feature ex...

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Autores principales:	Sharafi Afsaneh, Adabi Sepideh, Movaghar Ali, Al‐Majeed Salah
Formato:	article
Lenguaje:	EN
Publicado:	Wiley 2021
Materias:	Telecommunication TK5101-6720
Acceso en línea:	https://doaj.org/article/0c8fb478ad54471eab1fc481537fbd26
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spelling	oai:doaj.org-article:0c8fb478ad54471eab1fc481537fbd262021-11-09T04:19:38ZA two‐layer attack‐robust protocol for IoT healthcare security1751-86361751-862810.1049/cmu2.12278https://doaj.org/article/0c8fb478ad54471eab1fc481537fbd262021-12-01T00:00:00Zhttps://doi.org/10.1049/cmu2.12278https://doaj.org/toc/1751-8628https://doaj.org/toc/1751-8636Abstract The majority of studies in the field of developing identification and authentication protocols for Internet of Things (IoT) used cryptographic algorithms. Using brain signals is also a relatively new approach in this field. EEG signal‐based authentication algorithms typically use feature extraction algorithms that require high processing time. On the other hand, the dynamic nature of the EEG signal makes its use for identification/authentication difficult without relying on feature extraction. This paper presents an EEG‐and fingerprint‐based two‐stage identification‐authentication protocol for remote healthcare, which is fast, robust, and multilayer‐based. A modified Euclidean distance pattern matching method is proposed to match the EEG signal in the identification stage due to its dynamic nature. The authentication stage is also an optimized method with the Genetic Algorithm (GA), which utilizes a modified Diffie–Hellman algorithm. Due to the vulnerability of the Diffie–Hellman algorithm to different types of attacks, the parameters used for this algorithm are extracted from the fingerprint and the EEG signal of the patient to provide a fast and robust authentication method. The proposed method is evaluated using data from patients with spinal cord injuries. Simulating results demonstrated high identification and authentication accuracy of the proposed method. Furthermore, it is extremely fast and efficient.Sharafi AfsanehAdabi SepidehMovaghar AliAl‐Majeed SalahWileyarticleTelecommunicationTK5101-6720ENIET Communications, Vol 15, Iss 19, Pp 2390-2406 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Telecommunication TK5101-6720
spellingShingle	Telecommunication TK5101-6720 Sharafi Afsaneh Adabi Sepideh Movaghar Ali Al‐Majeed Salah A two‐layer attack‐robust protocol for IoT healthcare security
description	Abstract The majority of studies in the field of developing identification and authentication protocols for Internet of Things (IoT) used cryptographic algorithms. Using brain signals is also a relatively new approach in this field. EEG signal‐based authentication algorithms typically use feature extraction algorithms that require high processing time. On the other hand, the dynamic nature of the EEG signal makes its use for identification/authentication difficult without relying on feature extraction. This paper presents an EEG‐and fingerprint‐based two‐stage identification‐authentication protocol for remote healthcare, which is fast, robust, and multilayer‐based. A modified Euclidean distance pattern matching method is proposed to match the EEG signal in the identification stage due to its dynamic nature. The authentication stage is also an optimized method with the Genetic Algorithm (GA), which utilizes a modified Diffie–Hellman algorithm. Due to the vulnerability of the Diffie–Hellman algorithm to different types of attacks, the parameters used for this algorithm are extracted from the fingerprint and the EEG signal of the patient to provide a fast and robust authentication method. The proposed method is evaluated using data from patients with spinal cord injuries. Simulating results demonstrated high identification and authentication accuracy of the proposed method. Furthermore, it is extremely fast and efficient.
format	article
author	Sharafi Afsaneh Adabi Sepideh Movaghar Ali Al‐Majeed Salah
author_facet	Sharafi Afsaneh Adabi Sepideh Movaghar Ali Al‐Majeed Salah
author_sort	Sharafi Afsaneh
title	A two‐layer attack‐robust protocol for IoT healthcare security
title_short	A two‐layer attack‐robust protocol for IoT healthcare security
title_full	A two‐layer attack‐robust protocol for IoT healthcare security
title_fullStr	A two‐layer attack‐robust protocol for IoT healthcare security
title_full_unstemmed	A two‐layer attack‐robust protocol for IoT healthcare security
title_sort	two‐layer attack‐robust protocol for iot healthcare security
publisher	Wiley
publishDate	2021
url	https://doaj.org/article/0c8fb478ad54471eab1fc481537fbd26
work_keys_str_mv	AT sharafiafsaneh atwolayerattackrobustprotocolforiothealthcaresecurity AT adabisepideh atwolayerattackrobustprotocolforiothealthcaresecurity AT movagharali atwolayerattackrobustprotocolforiothealthcaresecurity AT almajeedsalah atwolayerattackrobustprotocolforiothealthcaresecurity AT sharafiafsaneh twolayerattackrobustprotocolforiothealthcaresecurity AT adabisepideh twolayerattackrobustprotocolforiothealthcaresecurity AT movagharali twolayerattackrobustprotocolforiothealthcaresecurity AT almajeedsalah twolayerattackrobustprotocolforiothealthcaresecurity
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A two‐layer attack‐robust protocol for IoT healthcare security

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