Zachos, Georgios, Mantas, Georgios ORCID: 0000-0002-8074-0417, Essop, Ismael ORCID: 0000-0002-5583-0306, Porfyrakis, Kyriakos ORCID: 0000-0003-1364-0261, Ribeiro, Jose C. and Rodriguez, Jonathan (2022) Prototyping an anomaly-based intrusion detection system for Internet of Medical Things Networks. In: 2022 IEEE 27th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD). 02-03 November 2022. Paris, France. IEEE Xplore . Institute of Electrical and Electronics Engineers (IEEE), Piscataway, New Jersey, pp. 179-183. ISBN 978-1665461290; 978-1665461306 ISSN 2378-4873 (Print), 2378-4865 (Online) (doi:https://doi.org/10.1109/CAMAD55695.2022.9966912)

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Abstract

Over the past few years, the Internet of Things (IoT) is transforming the healthcare sector through the introduction of the Internet of Medical Things (IoMT) technology, whose purpose is the improvement of the patient's quality of life. Nevertheless, IoMT networks are still vulnerable to a wide range of threats because of their heterogeneity and resource-constrained characteristics. Thus, novel security mechanisms, such as accurate and efficient anomaly-based intrusion detection systems (AIDSs), taking into consideration the inherent limitations of the IoMT networks, are required to be developed before IoMT networks reach their full potential in the market. In our previous work, we presented the system architecture for a novel hybrid AIDS for IoMT networks. In this paper, we expand it by presenting details of the implementation process that led to a prototype of the proposed AIDS. Our target is this work to serve as a guidance for other researchers or engineers to develop their own specific implementations of AIDSs for IoMT networks.

Item Type:	Conference Proceedings
Title of Proceedings:	2022 IEEE 27th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD). 02-03 November 2022. Paris, France
Uncontrolled Keywords:	Internet of Medical Things (IoMT); intrusion detection system (IDS); machine learning algorithms; anomaly-based intrusion detection; IoMT AIDS implementation
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science R Medicine > R Medicine (General) T Technology > T Technology (General)
Faculty / School / Research Centre / Research Group:	Faculty of Engineering & Science Faculty of Engineering & Science > School of Engineering (ENG)
Last Modified:	14 Dec 2022 09:16
URI:	http://gala.gre.ac.uk/id/eprint/38260

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