REJI, Alan, PRANGGONO, Bernardi, MARCHANG, Jims and SHENFIELD, Alex (2023). Anomaly detection for the internet-of-medical-things. In: 2023 IEEE International Conference on Communications Workshops (ICC Workshops). IEEE, 1944-1949.
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Abstract
Cybersecurity for the Internet of Medical Things (IoMT) is a very concerning issue because of emerging cyber threats and security incidents targeting IoMT devices all over the world. The healthcare system has near-zero tolerance for inexplicability. In this paper, we propose a machine learning-based anomaly detection for the IoMT and evaluate the performance using a realistic public dataset. We implement various machine learning algorithms: Random Forest, Decision Tree, Logistic Regression, Support Vector Machine, and K-Nearest Neighbor with TON_IoT dataset. Two types of classifications are implemented: binary and categorical. In the categorical classification, evaluation for nine attack scenarios (Scanning, DoS, password cracking attack, and Man-in-The-Middle (MITM)) are performed. The test results demonstrate that Support Vector Machine models produce better performance compared to the other models.
Item Type: | Book Section |
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Additional Information: | Electronic ISSN: 2694-2941 |
Identification Number: | https://doi.org/10.1109/ICCWorkshops57953.2023.10283523 |
Page Range: | 1944-1949 |
SWORD Depositor: | Symplectic Elements |
Depositing User: | Symplectic Elements |
Date Deposited: | 29 Mar 2023 10:42 |
Last Modified: | 15 Nov 2023 08:00 |
URI: | https://shura.shu.ac.uk/id/eprint/31706 |
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