A Novel Approach for Improving the Security of IoT–Medical Data Systems Using an Enhanced Dynamic Bayesian Network
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Published:2023-10-18
Issue:20
Volume:12
Page:4316
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ISSN:2079-9292
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Container-title:Electronics
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language:en
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Short-container-title:Electronics
Author:
Almaiah Mohammed Amin12, Yelisetti Sandeep3, Arya Leena4, Babu Christopher Nelson Kennedy5, Kaliappan Kumaresan6, Vellaisamy Pandimurugan7, Hajjej Fahima8ORCID, Alkdour Tayseer9
Affiliation:
1. College of Information Technology, Aqaba University of Technology, Aqaba 11947, Jordan 2. Applied Science Research Center, Applied Science Private University, Amman 11931, Jordan 3. Department of IT, V R Siddhartha Engineering College, Vijayawada 520007, India 4. Department of CSE, Koneru Lakshmaiah Education Foundation, Mangalagiri, Vaddeswaram 522502, India 5. Department of Computer Science and Engineering, Saveetha School of Engineering, Chennai 602105, India 6. Maratsolutions, Coimbatore 641001, India 7. School of Computing, Department of Networking and Communications, SRM Institute of Science & Technology, Kattankulathur Campus, Chennai 603203, India 8. Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia 9. College of Computer Science and Information Technology, King Faisal University, Al-Ahsa 31982, Saudi Arabia
Abstract
IoT (Internet of Things) devices are increasingly being used in healthcare to collect and transmit patient data, which can improve patient outcomes and reduce costs. However, this also creates new challenges for data security and privacy. Thus, the major demand for secure and efficient data-sharing solutions has prompted significant attention due to the increasing volume of shared sensor data. Leveraging a data-fusion-based paradigm within the realm of IoT-protected healthcare systems enabled the collection and analysis of patient data from diverse sources, encompassing medical devices, electronic health records (EHRs), and wearables. This innovative approach holds the potential to yield immediate benefits in terms of enhancing patient care, including more precise diagnoses and treatment plans. It empowers healthcare professionals to devise personalized treatment regimens by amalgamating data from multiple origins. Moreover, it has the capacity to alleviate financial burdens, elevate healthcare outcomes, and augment patient satisfaction. Furthermore, this concept extends to fortifying patient records against unauthorized access and potential misuse. In this study, we propose a novel approach for secure transmission of healthcare data, amalgamating the improved context-aware data-fusion method with an emotional-intelligence-inspired enhanced dynamic Bayesian network (EDBN). The findings indicated that F1 score, accuracy, precision, recall, and ROC-AUC score using DCNN were 89.3%, 87.4%, 91.4%, 92.1%, and 0.56, respectively, which was second-highest to the proposed method. On the other hand, the F1 score, accuracy, precision, recall, and ROC-AUC scores of FRCNN and CNN were low in accuracy at 83.2% and 84.3%, respectively. Our experimental investigation demonstrated superior performance compared with existing methods, as evidenced by various performance metrics, including recall, precision, F measures, and accuracy.
Funder
Annual Funding track by the Deanship of Scientific Research, Vice Presidency for Graduate Studies and Scientific Research, King Faisal University, Saudi Arabia Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
Subject
Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing,Control and Systems Engineering
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