An Anomaly-Based Intrusion Detection System for Internet of Medical Things Networks

Over the past few years, the healthcare sector is being transformed due to the rise of the Internet of Things (IoT) and the introduction of the Internet of Medical Things (IoMT) technology, whose purpose is the improvement of the patient’s quality of life. Nevertheless, the heterogenous and resource...

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Autores principales:	Georgios Zachos, Ismael Essop, Georgios Mantas, Kyriakos Porfyrakis, José C. Ribeiro, Jonathan Rodriguez
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	Internet of Medical Things (IoMT) intrusion detection system (IDS) machine learning algorithms anomaly-based intrusion detection IoT datasets Electronics TK7800-8360
Acceso en línea:	https://doaj.org/article/b314d87ca08940398d2f57fc413c81d8
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id	oai:doaj.org-article:b314d87ca08940398d2f57fc413c81d8
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spelling	oai:doaj.org-article:b314d87ca08940398d2f57fc413c81d82021-11-11T15:36:25ZAn Anomaly-Based Intrusion Detection System for Internet of Medical Things Networks10.3390/electronics102125622079-9292https://doaj.org/article/b314d87ca08940398d2f57fc413c81d82021-10-01T00:00:00Zhttps://www.mdpi.com/2079-9292/10/21/2562https://doaj.org/toc/2079-9292Over the past few years, the healthcare sector is being transformed due to the rise of the Internet of Things (IoT) and the introduction of the Internet of Medical Things (IoMT) technology, whose purpose is the improvement of the patient’s quality of life. Nevertheless, the heterogenous and resource-constrained characteristics of IoMT networks make them vulnerable to a wide range of threats. Thus, novel security mechanisms, such as accurate and efficient anomaly-based intrusion detection systems (AIDSs), considering the inherent limitations of the IoMT networks, need to be developed before IoMT networks reach their full potential in the market. Towards this direction, in this paper, we propose an efficient and effective anomaly-based intrusion detection system (AIDS) for IoMT networks. The proposed AIDS aims to leverage host-based and network-based techniques to reliably collect log files from the IoMT devices and the gateway, as well as traffic from the IoMT edge network, while taking into consideration the computational cost. The proposed AIDS is to rely on machine learning (ML) techniques, considering the computation overhead, in order to detect abnormalities in the collected data and thus identify malicious incidents in the IoMT network. A set of six popular ML algorithms was tested and evaluated for anomaly detection in the proposed AIDS, and the evaluation results showed which of them are the most suitable.Georgios ZachosIsmael EssopGeorgios MantasKyriakos PorfyrakisJosé C. RibeiroJonathan RodriguezMDPI AGarticleInternet of Medical Things (IoMT)intrusion detection system (IDS)machine learning algorithmsanomaly-based intrusion detectionIoT datasetsElectronicsTK7800-8360ENElectronics, Vol 10, Iss 2562, p 2562 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Internet of Medical Things (IoMT) intrusion detection system (IDS) machine learning algorithms anomaly-based intrusion detection IoT datasets Electronics TK7800-8360
spellingShingle	Internet of Medical Things (IoMT) intrusion detection system (IDS) machine learning algorithms anomaly-based intrusion detection IoT datasets Electronics TK7800-8360 Georgios Zachos Ismael Essop Georgios Mantas Kyriakos Porfyrakis José C. Ribeiro Jonathan Rodriguez An Anomaly-Based Intrusion Detection System for Internet of Medical Things Networks
description	Over the past few years, the healthcare sector is being transformed due to the rise of the Internet of Things (IoT) and the introduction of the Internet of Medical Things (IoMT) technology, whose purpose is the improvement of the patient’s quality of life. Nevertheless, the heterogenous and resource-constrained characteristics of IoMT networks make them vulnerable to a wide range of threats. Thus, novel security mechanisms, such as accurate and efficient anomaly-based intrusion detection systems (AIDSs), considering the inherent limitations of the IoMT networks, need to be developed before IoMT networks reach their full potential in the market. Towards this direction, in this paper, we propose an efficient and effective anomaly-based intrusion detection system (AIDS) for IoMT networks. The proposed AIDS aims to leverage host-based and network-based techniques to reliably collect log files from the IoMT devices and the gateway, as well as traffic from the IoMT edge network, while taking into consideration the computational cost. The proposed AIDS is to rely on machine learning (ML) techniques, considering the computation overhead, in order to detect abnormalities in the collected data and thus identify malicious incidents in the IoMT network. A set of six popular ML algorithms was tested and evaluated for anomaly detection in the proposed AIDS, and the evaluation results showed which of them are the most suitable.
format	article
author	Georgios Zachos Ismael Essop Georgios Mantas Kyriakos Porfyrakis José C. Ribeiro Jonathan Rodriguez
author_facet	Georgios Zachos Ismael Essop Georgios Mantas Kyriakos Porfyrakis José C. Ribeiro Jonathan Rodriguez
author_sort	Georgios Zachos
title	An Anomaly-Based Intrusion Detection System for Internet of Medical Things Networks
title_short	An Anomaly-Based Intrusion Detection System for Internet of Medical Things Networks
title_full	An Anomaly-Based Intrusion Detection System for Internet of Medical Things Networks
title_fullStr	An Anomaly-Based Intrusion Detection System for Internet of Medical Things Networks
title_full_unstemmed	An Anomaly-Based Intrusion Detection System for Internet of Medical Things Networks
title_sort	anomaly-based intrusion detection system for internet of medical things networks
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/b314d87ca08940398d2f57fc413c81d8
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An Anomaly-Based Intrusion Detection System for Internet of Medical Things Networks

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