2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 14 , Issue 1 , PP: 221-251, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Aya Hamid Ameen 1 , Mazin Abed Mohammed 2 * , Ahmed Noori Rashid 3
Doi: https://doi.org/10.54216/FPA.140117
The Internet of Medical Things (IoMT) revolutionizes healthcare, enhances patient care, and optimizes workflows. However, the integration of IoMT introduces concerns related to privacy and security. In addressing these issues and aiming to bolster privacy and data security, this study presents a novel cybersecurity framework based on blockchain (BC) technology. The primary goal is to ensure secure communication among IoMT devices, preventing unauthorized access and tampering with sensitive data. The proposed framework is implemented in a model designed for classifying electrocardiogram (ECG) signals, utilizing two datasets: a Medical Technology Database (MTDB) with a limited sample size and the Massachusetts Institute of Technology–Beth Israel Hospital (MITBIH) dataset with a more extensive sample size. The datasets are subsequently partitioned into training and testing data. Feature extraction and selection are performed using the Pan-Tomkins and genetic algorithms. To enhance security, BC technology is employed to encrypt the test data. Finally, signal classification is performed using the support vector machine (SVM) classifier. Thus, the model trained on the MITBIH dataset outperforms its small data counterpart, achieving an impressive accuracy rate of 99.9%. Additionally, the model exhibits a true positive rate (TPR) and true negative rate (TNR) of 100%, an F-score of 100%, and a positive predictive value (PPV) of 100%.
The Internet of Medical Things (IoMT) revolutionizes healthcare, enhances patient care, and optimizes workflows. However, the integration of IoMT introduces concerns related to privacy and security. In addressing these issues and aiming to bolster privacy and data security, this study presents a novel cybersecurity framework based on blockchain (BC) technology. The primary goal is to ensure secure communication among IoMT devices, preventing unauthorized access and tampering with sensitive data. The proposed framework is implemented in a model designed for classifying electrocardiogram (ECG) signals, utilizing two datasets: a Medical Technology Database (MTDB) with a limited sample size and the Massachusetts Institute of Technology&ndash , Beth Israel Hospital (MITBIH) dataset with a more extensive sample size. The datasets are subsequently partitioned into training and testing data. Feature extraction and selection are performed using the Pan-Tomkins and genetic algorithms. To enhance security, BC technology is employed to encrypt the test data. Finally, signal classification is performed using the support vector machine (SVM) classifier. Thus, the model trained on the MITBIH dataset outperforms its small data counterpart, achieving an impressive accuracy rate of 99.9%. Additionally, the model exhibits a true positive rate (TPR) and true negative rate (TNR) of 100%, an F-score of 100%, and a positive predictive value (PPV) of 100%.
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