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Intelligent Intrusion Detection System for the Internet of Medical Things Based on Data-Driven Techniques

Okba Taouali1,*, Sawcen Bacha2, Khaoula Ben Abdellafou1, Ahamed Aljuhani1, Kamel Zidi3, Rehab Alanazi1, Mohamed Faouzi Harkat4

1 Faculty of Computers and Information Technology, University of Tabuk, Tabuk, 71491, Saudi Arabia
2 National Engineering School of Monastir, University of Monastir, Monastir, 5000, Tunisia
3 Applied College, University of Tabuk, Tabuk, 71491, Saudi Arabia
4 Department of Electronics, Faculty of Engineering Annaba, Badji Mokhtar BP. 12, Annaba, 23000, Algeria

* Corresponding Author: Okba Taouali. Email: email

Computer Systems Science and Engineering 2023, 47(2), 1593-1609. https://doi.org/10.32604/csse.2023.039984

Abstract

Introducing IoT devices to healthcare fields has made it possible to remotely monitor patients’ information and provide a proper diagnosis as needed, resulting in the Internet of Medical Things (IoMT). However, obtaining good security features that ensure the integrity and confidentiality of patient’s information is a significant challenge. However, due to the computational resources being limited, an edge device may struggle to handle heavy detection tasks such as complex machine learning algorithms. Therefore, designing and developing a lightweight detection mechanism is crucial. To address the aforementioned challenges, a new lightweight IDS approach is developed to effectively combat a diverse range of cyberattacks in IoMT networks. The proposed anomaly-based IDS is divided into three steps: pre-processing, feature selection, and decision. In the pre-processing phase, data cleaning and normalization are performed. In the feature selection step, the proposed approach uses two data-driven kernel techniques: kernel principal component analysis and kernel partial least square techniques to reduce the dimension of extracted features and to ameliorate the detection results. Therefore, in decision step, in order to classify whether the traffic flow is normal or malicious the kernel extreme learning machine is used. To check the efficiency of the developed detection scheme, a modern IoMT dataset named WUSTL-EHMS-2020 is considered to evaluate and discuss the achieved results. The proposed method achieved 99.9% accuracy, 99.8% specificity, 100% Sensitivity, 99.9 F-score.

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APA Style
Taouali, O., Bacha, S., Abdellafou, K.B., Aljuhani, A., Zidi, K. et al. (2023). Intelligent intrusion detection system for the internet of medical things based on data-driven techniques. Computer Systems Science and Engineering, 47(2), 1593-1609. https://doi.org/10.32604/csse.2023.039984
Vancouver Style
Taouali O, Bacha S, Abdellafou KB, Aljuhani A, Zidi K, Alanazi R, et al. Intelligent intrusion detection system for the internet of medical things based on data-driven techniques. Comput Syst Sci Eng. 2023;47(2):1593-1609 https://doi.org/10.32604/csse.2023.039984
IEEE Style
O. Taouali et al., "Intelligent Intrusion Detection System for the Internet of Medical Things Based on Data-Driven Techniques," Comput. Syst. Sci. Eng., vol. 47, no. 2, pp. 1593-1609. 2023. https://doi.org/10.32604/csse.2023.039984



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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