Anomaly detection for the internet-of-medical-things

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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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
Additional Information: Electronic ISSN: 2694-2941
Identification Number:
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

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