Trustworthy Intrusion Detection in E-Healthcare Systems

In Internet of Things (IoT)-based network systems (IoT-net), intrusion detection systems (IDS) play a significant role to maintain patient health records (PHR) in e-healthcare. IoT-net is a massive technology with security threats on the network layer, as it is considered the most common source for...

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Autores principales:	Faiza Akram, Dongsheng Liu, Peibiao Zhao, Natalia Kryvinska, Sidra Abbas, Muhammad Rizwan
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	network security privacy ANFIS intrusion detection IoT based networks Public aspects of medicine RA1-1270
Acceso en línea:	https://doaj.org/article/ae5d5268ef554d4eb9bbf74712c69ca6
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id	oai:doaj.org-article:ae5d5268ef554d4eb9bbf74712c69ca6
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spelling	oai:doaj.org-article:ae5d5268ef554d4eb9bbf74712c69ca62021-12-03T07:19:57ZTrustworthy Intrusion Detection in E-Healthcare Systems2296-256510.3389/fpubh.2021.788347https://doaj.org/article/ae5d5268ef554d4eb9bbf74712c69ca62021-12-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fpubh.2021.788347/fullhttps://doaj.org/toc/2296-2565In Internet of Things (IoT)-based network systems (IoT-net), intrusion detection systems (IDS) play a significant role to maintain patient health records (PHR) in e-healthcare. IoT-net is a massive technology with security threats on the network layer, as it is considered the most common source for communication and data storage platforms. The security of data servers in all sectors (mainly healthcare) has become one of the most crucial challenges for researchers. This paper proposes an approach for effective intrusion detection in the e-healthcare environment to maintain PHR in a safe IoT-net using an adaptive neuro-fuzzy inference system (ANFIS). In the proposed security model, the experiments present a security tool that helps to detect malicious network traffic. The practical implementation of the ANFIS model on the MATLAB framework with testing and training results compares the accuracy rate from the previous research in security.Faiza AkramDongsheng LiuPeibiao ZhaoNatalia KryvinskaSidra AbbasMuhammad RizwanFrontiers Media S.A.articlenetwork securityprivacyANFISintrusion detectionIoT based networksPublic aspects of medicineRA1-1270ENFrontiers in Public Health, Vol 9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	network security privacy ANFIS intrusion detection IoT based networks Public aspects of medicine RA1-1270
spellingShingle	network security privacy ANFIS intrusion detection IoT based networks Public aspects of medicine RA1-1270 Faiza Akram Dongsheng Liu Peibiao Zhao Natalia Kryvinska Sidra Abbas Muhammad Rizwan Trustworthy Intrusion Detection in E-Healthcare Systems
description	In Internet of Things (IoT)-based network systems (IoT-net), intrusion detection systems (IDS) play a significant role to maintain patient health records (PHR) in e-healthcare. IoT-net is a massive technology with security threats on the network layer, as it is considered the most common source for communication and data storage platforms. The security of data servers in all sectors (mainly healthcare) has become one of the most crucial challenges for researchers. This paper proposes an approach for effective intrusion detection in the e-healthcare environment to maintain PHR in a safe IoT-net using an adaptive neuro-fuzzy inference system (ANFIS). In the proposed security model, the experiments present a security tool that helps to detect malicious network traffic. The practical implementation of the ANFIS model on the MATLAB framework with testing and training results compares the accuracy rate from the previous research in security.
format	article
author	Faiza Akram Dongsheng Liu Peibiao Zhao Natalia Kryvinska Sidra Abbas Muhammad Rizwan
author_facet	Faiza Akram Dongsheng Liu Peibiao Zhao Natalia Kryvinska Sidra Abbas Muhammad Rizwan
author_sort	Faiza Akram
title	Trustworthy Intrusion Detection in E-Healthcare Systems
title_short	Trustworthy Intrusion Detection in E-Healthcare Systems
title_full	Trustworthy Intrusion Detection in E-Healthcare Systems
title_fullStr	Trustworthy Intrusion Detection in E-Healthcare Systems
title_full_unstemmed	Trustworthy Intrusion Detection in E-Healthcare Systems
title_sort	trustworthy intrusion detection in e-healthcare systems
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/ae5d5268ef554d4eb9bbf74712c69ca6
work_keys_str_mv	AT faizaakram trustworthyintrusiondetectioninehealthcaresystems AT dongshengliu trustworthyintrusiondetectioninehealthcaresystems AT peibiaozhao trustworthyintrusiondetectioninehealthcaresystems AT nataliakryvinska trustworthyintrusiondetectioninehealthcaresystems AT sidraabbas trustworthyintrusiondetectioninehealthcaresystems AT muhammadrizwan trustworthyintrusiondetectioninehealthcaresystems
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Trustworthy Intrusion Detection in E-Healthcare Systems

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