Please use this identifier to cite or link to this item: http://10.1.7.192:80/jspui/handle/123456789/12479
Title: Blockchain Enabled Secure IoMT Framework
Authors: Panchal, Barkha
Keywords: Computer 2022
Project Report
Project Report 2022
Computer Project Report
22MCE
22MCES
22MCES07
CE (CCS)
CCS 2022
Cyber Security
Issue Date: 1-Jun-2024
Publisher: Institute of Technology
Series/Report no.: 22MCES07;
Abstract: With its incredible efficiency, the Internet of Things (IoT) in particular is transforming care for patients and healthcare operations. The medical industry has seen firsthand how this state-of-the-art equipment can change things. The incorporation of sensing into IoT devices facilitates seamless connectivity and the gathering of essential data required for patient monitoring and treatment optimization. Numerous medical devices, including wheelchairs, nebulizers, and oxygen pumps, can be monitored in real time using connected sensors. The gathering of vital signs, prescription data, diagnoses for patients, and medical history may be made possible by this integration. Strong security measures are required to guarantee privacy and confidentiality of patients as worries about the safety of this private health information have surfaced. This research proposes a blockchain, software defined networking (SDN) and artificial intelligence(AI) for offering security measures for medical IoT data. Medical IoT data is classified into two classes: attack (1) and normal (0), using a variety of machine learning (ML) classifiers, such as support vector machine (SVM), random forest (RF), and K-Nearest neighbor (KNN). We discuss the solution of our literature is based on the security mechanism for the IoMT framework. This research work tackle these challenges and proposed a solution for healthcare IoT. Furthermore, other performance measures are taken into consideration evaluation, including accuracy, precision, recall and F1 score. The detection model using SVM obtained 75.3%, RF is 75.9%andK-NN is 84.3%. The K-NN gets the greatest accuracy among all ML classifiers.
URI: http://10.1.7.192:80/jspui/handle/123456789/12479
Appears in Collections:Dissertation, CE (CCS)

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