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Improved Wireless Medical Cyber-Physical System (IWMCPS) Based on Machine Learning

Medical cyber-physical systems (MCPS) represent a platform through which patient health data are acquired by emergent Internet of Things (IoT) sensors, preprocessed locally, and managed through improved machine intelligence algorithms. Wireless medical cyber-physical systems are extensively adopted...

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Autores principales: Alzahrani, Ahmad, Alshehri, Mohammed, AlGhamdi, Rayed, Sharma, Sunil Kumar
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9913988/
https://www.ncbi.nlm.nih.gov/pubmed/36766959
http://dx.doi.org/10.3390/healthcare11030384
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author Alzahrani, Ahmad
Alshehri, Mohammed
AlGhamdi, Rayed
Sharma, Sunil Kumar
author_facet Alzahrani, Ahmad
Alshehri, Mohammed
AlGhamdi, Rayed
Sharma, Sunil Kumar
author_sort Alzahrani, Ahmad
collection PubMed
description Medical cyber-physical systems (MCPS) represent a platform through which patient health data are acquired by emergent Internet of Things (IoT) sensors, preprocessed locally, and managed through improved machine intelligence algorithms. Wireless medical cyber-physical systems are extensively adopted in the daily practices of medicine, where vast amounts of data are sampled using wireless medical devices and sensors and passed to decision support systems (DSSs). With the development of physical systems incorporating cyber frameworks, cyber threats have far more acute effects, as they are reproduced in the physical environment. Patients’ personal information must be shielded against intrusions to preserve their privacy and confidentiality. Therefore, every bit of information stored in the database needs to be kept safe from intrusion attempts. The IWMCPS proposed in this work takes into account all relevant security concerns. This paper summarizes three years of fieldwork by presenting an IWMCPS framework consisting of several components and subsystems. The IWMCPS architecture is developed, as evidenced by a scenario including applications in the medical sector. Cyber-physical systems are essential to the healthcare sector, and life-critical and context-aware health data are vulnerable to information theft and cyber-okayattacks. Reliability, confidence, security, and transparency are some of the issues that must be addressed in the growing field of MCPS research. To overcome the abovementioned problems, we present an improved wireless medical cyber-physical system (IWMCPS) based on machine learning techniques. The heterogeneity of devices included in these systems (such as mobile devices and body sensor nodes) makes them prone to many attacks. This necessitates effective security solutions for these environments based on deep neural networks for attack detection and classification. The three core elements in the proposed IWMCPS are the communication and monitoring core, the computational and safety core, and the real-time planning and administration of resources. In this study, we evaluated our design with actual patient data against various security attacks, including data modification, denial of service (DoS), and data injection. The IWMCPS method is based on a patient-centric architecture that preserves the end-user’s smartphone device to control data exchange accessibility. The patient health data used in WMCPSs must be well protected and secure in order to overcome cyber-physical threats. Our experimental findings showed that our model attained a high detection accuracy of 92% and a lower computational time of 13 sec with fewer error analyses.
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spelling pubmed-99139882023-02-11 Improved Wireless Medical Cyber-Physical System (IWMCPS) Based on Machine Learning Alzahrani, Ahmad Alshehri, Mohammed AlGhamdi, Rayed Sharma, Sunil Kumar Healthcare (Basel) Article Medical cyber-physical systems (MCPS) represent a platform through which patient health data are acquired by emergent Internet of Things (IoT) sensors, preprocessed locally, and managed through improved machine intelligence algorithms. Wireless medical cyber-physical systems are extensively adopted in the daily practices of medicine, where vast amounts of data are sampled using wireless medical devices and sensors and passed to decision support systems (DSSs). With the development of physical systems incorporating cyber frameworks, cyber threats have far more acute effects, as they are reproduced in the physical environment. Patients’ personal information must be shielded against intrusions to preserve their privacy and confidentiality. Therefore, every bit of information stored in the database needs to be kept safe from intrusion attempts. The IWMCPS proposed in this work takes into account all relevant security concerns. This paper summarizes three years of fieldwork by presenting an IWMCPS framework consisting of several components and subsystems. The IWMCPS architecture is developed, as evidenced by a scenario including applications in the medical sector. Cyber-physical systems are essential to the healthcare sector, and life-critical and context-aware health data are vulnerable to information theft and cyber-okayattacks. Reliability, confidence, security, and transparency are some of the issues that must be addressed in the growing field of MCPS research. To overcome the abovementioned problems, we present an improved wireless medical cyber-physical system (IWMCPS) based on machine learning techniques. The heterogeneity of devices included in these systems (such as mobile devices and body sensor nodes) makes them prone to many attacks. This necessitates effective security solutions for these environments based on deep neural networks for attack detection and classification. The three core elements in the proposed IWMCPS are the communication and monitoring core, the computational and safety core, and the real-time planning and administration of resources. In this study, we evaluated our design with actual patient data against various security attacks, including data modification, denial of service (DoS), and data injection. The IWMCPS method is based on a patient-centric architecture that preserves the end-user’s smartphone device to control data exchange accessibility. The patient health data used in WMCPSs must be well protected and secure in order to overcome cyber-physical threats. Our experimental findings showed that our model attained a high detection accuracy of 92% and a lower computational time of 13 sec with fewer error analyses. MDPI 2023-01-29 /pmc/articles/PMC9913988/ /pubmed/36766959 http://dx.doi.org/10.3390/healthcare11030384 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Alzahrani, Ahmad
Alshehri, Mohammed
AlGhamdi, Rayed
Sharma, Sunil Kumar
Improved Wireless Medical Cyber-Physical System (IWMCPS) Based on Machine Learning
title Improved Wireless Medical Cyber-Physical System (IWMCPS) Based on Machine Learning
title_full Improved Wireless Medical Cyber-Physical System (IWMCPS) Based on Machine Learning
title_fullStr Improved Wireless Medical Cyber-Physical System (IWMCPS) Based on Machine Learning
title_full_unstemmed Improved Wireless Medical Cyber-Physical System (IWMCPS) Based on Machine Learning
title_short Improved Wireless Medical Cyber-Physical System (IWMCPS) Based on Machine Learning
title_sort improved wireless medical cyber-physical system (iwmcps) based on machine learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9913988/
https://www.ncbi.nlm.nih.gov/pubmed/36766959
http://dx.doi.org/10.3390/healthcare11030384
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