BLOCK-SECURE: AI-Based Blockchain Enabled Secure Framework for IoMT Applications

The Internet of Medical Things (IoMT) revolution-izes healthcare by integrating medical devices and systems with the internet. However, the vast amounts of sensitive medical data in IoMT networks pose significant security and privacy concerns. Traditional security measures often fall short in identifying the malicious data attacks within the IoMT ecosystems. This paper introduces an AI-based non-malicious data classification scheme based on blockchain. We applied and evaluated the machine learning (ML) classifiers, such as support vector machine (SVM), random forest (RF), and K-Nearest neighbor (KNN). We evaluated the proposed framework based on various performance metrics that includes accuracy, precision, recall, and F1 score. The accuracy using SVM obtained 75.3%, RF is 75.9%, and K-NN is 84.3%. The results shows that KNN performs better than other models hy the factor of 9%.

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Publication Details

Published
2024-11-18
DOI
https://doi.org/10.1109/healthcom60970.2024.10880826
Primary Topic
Blockchain Technology Applications and Security
Type
article
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article

BLOCK-SECURE: AI-Based Blockchain Enabled Secure Framework for IoMT Applications

Jitendra Bhatia, Ashit Kumar Dutta, Malaram Kumhar, Joel J. P. C. Rodrigues et al.
Blockchain Technology Applications and Security
article

BLOCK-SECURE: AI-Based Blockchain Enabled Secure Framework for IoMT Applications

Jitendra Bhatia, Ashit Kumar Dutta, Malaram Kumhar, Joel J. P. C. Rodrigues, Sudeep Tanwar, Barkha Panchal
article en

Abstract

The Internet of Medical Things (IoMT) revolution-izes healthcare by integrating medical devices and systems with the internet. However, the vast amounts of sensitive medical data in IoMT networks pose significant security and privacy concerns. Traditional security measures often fall short in identifying the malicious data attacks within the IoMT ecosystems. This paper introduces an AI-based non-malicious data classification scheme based on blockchain. We applied and evaluated the machine learning (ML) classifiers, such as support vector machine (SVM), random forest (RF), and K-Nearest neighbor (KNN). We evaluated the proposed framework based on various performance metrics that includes accuracy, precision, recall, and F1 score. The accuracy using SVM obtained 75.3%, RF is 75.9%, and K-NN is 84.3%. The results shows that KNN performs better than other models hy the factor of 9%.

Nirma University (IN), Universidade do Estado do Amazonas (BR)
Openalex Percentile: Top 56%
Blockchain Technology Applications and Security
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BLOCK-SECURE: AI-Based Blockchain Enabled Secure Framework for IoMT Applications — Jitendra Bhatia, Ashit Kumar Dutta, et al. · (2024) | TGRS Research Map | TGRS