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A threat intelligence framework for protecting smart satellite-based healthcare networks
Human-to-machine (H2M) communication is an important evolution in the industrial internet of health things (IIoHT), where many H2M interfaces are remotely interacting with industrial and medical assets. Lightweight protocols, such as constrained application protocol (CoAP), have been widely utilised...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer London
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8425465/ https://www.ncbi.nlm.nih.gov/pubmed/34518744 http://dx.doi.org/10.1007/s00521-021-06441-5 |
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author | Al-Hawawreh, Muna Moustafa, Nour Slay, Jill |
author_facet | Al-Hawawreh, Muna Moustafa, Nour Slay, Jill |
author_sort | Al-Hawawreh, Muna |
collection | PubMed |
description | Human-to-machine (H2M) communication is an important evolution in the industrial internet of health things (IIoHT), where many H2M interfaces are remotely interacting with industrial and medical assets. Lightweight protocols, such as constrained application protocol (CoAP), have been widely utilised in transferring sensing data of medical devices to end-users in smart satellite-based healthcare IIoT networks (SmartSat-IIoHT). However, such protocols are extensively deployed without appropriate security configurations, making attackers’ mission easier for abusing these protocols to launch advanced cyber threats. This paper, therefore, presents a new threat intelligence framework to examine and model CoAP protocol’s attacks in these systems. We present a ransom denial of service (RDoS) as a new threat that would exploit this protocol’s vulnerabilities. We propose many RDoS attack’s techniques to understand the attack indicators and analyse their behaviour on systems. Moreover, we present a real-time discovery of attacks’ network behaviours using deep learning. The experiment results demonstrate that this proposed discovery model obtains a better performance in revealing RDoS than other conventional machine learning algorithms and accomplishing high fidelity of protecting SmartSat-IIoHT networks. |
format | Online Article Text |
id | pubmed-8425465 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer London |
record_format | MEDLINE/PubMed |
spelling | pubmed-84254652021-09-09 A threat intelligence framework for protecting smart satellite-based healthcare networks Al-Hawawreh, Muna Moustafa, Nour Slay, Jill Neural Comput Appl S.I. : Improving Healthcare outcomes using Multimedia Big Data Analytics Human-to-machine (H2M) communication is an important evolution in the industrial internet of health things (IIoHT), where many H2M interfaces are remotely interacting with industrial and medical assets. Lightweight protocols, such as constrained application protocol (CoAP), have been widely utilised in transferring sensing data of medical devices to end-users in smart satellite-based healthcare IIoT networks (SmartSat-IIoHT). However, such protocols are extensively deployed without appropriate security configurations, making attackers’ mission easier for abusing these protocols to launch advanced cyber threats. This paper, therefore, presents a new threat intelligence framework to examine and model CoAP protocol’s attacks in these systems. We present a ransom denial of service (RDoS) as a new threat that would exploit this protocol’s vulnerabilities. We propose many RDoS attack’s techniques to understand the attack indicators and analyse their behaviour on systems. Moreover, we present a real-time discovery of attacks’ network behaviours using deep learning. The experiment results demonstrate that this proposed discovery model obtains a better performance in revealing RDoS than other conventional machine learning algorithms and accomplishing high fidelity of protecting SmartSat-IIoHT networks. Springer London 2021-09-08 /pmc/articles/PMC8425465/ /pubmed/34518744 http://dx.doi.org/10.1007/s00521-021-06441-5 Text en © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | S.I. : Improving Healthcare outcomes using Multimedia Big Data Analytics Al-Hawawreh, Muna Moustafa, Nour Slay, Jill A threat intelligence framework for protecting smart satellite-based healthcare networks |
title | A threat intelligence framework for protecting smart satellite-based healthcare networks |
title_full | A threat intelligence framework for protecting smart satellite-based healthcare networks |
title_fullStr | A threat intelligence framework for protecting smart satellite-based healthcare networks |
title_full_unstemmed | A threat intelligence framework for protecting smart satellite-based healthcare networks |
title_short | A threat intelligence framework for protecting smart satellite-based healthcare networks |
title_sort | threat intelligence framework for protecting smart satellite-based healthcare networks |
topic | S.I. : Improving Healthcare outcomes using Multimedia Big Data Analytics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8425465/ https://www.ncbi.nlm.nih.gov/pubmed/34518744 http://dx.doi.org/10.1007/s00521-021-06441-5 |
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