Blockchain Assisted Intrusion Detection and Data Classification on Smart Healthcare Management System
Blockchain Assisted Intrusion Detection and Data Classification on Smart Healthcare Management System |
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| © 2023 by IJETT Journal | ||
| Volume-71 Issue-2 |
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| Year of Publication : 2023 | ||
| Author : K. Rajeshkumar, C. Ananth, N. Mohananthini |
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| DOI : 10.14445/22315381/IJETT-V71I2P202 | ||
How to Cite?
K. Rajeshkumar, C. Ananth, N. Mohananthini, "Blockchain Assisted Intrusion Detection and Data Classification on Smart Healthcare Management System," International Journal of Engineering Trends and Technology, vol. 71, no. 2, pp. 9-20, 2023. Crossref, https://doi.org/10.14445/22315381/IJETT-V71I2P202
Abstract
Blockchain (BC) is a newer technology being applied for making creative solutions in different fields involving
healthcare. A Blockchain network is used in the medical field for preserving and exchanging patient datasets through
diagnostic laboratories, hospitals, physicians, and pharmaceutical firms. BC application could precisely classify severe and
dangerous mistakes in the healthcare sector. Accordingly, it could enhance the transparency, performance, and security of
sharing healthcare datasets in the medical system. This technique is useful to healthcare institutions to gain insight and
improve the analysis of healthcare records. Therefore, this study focuses on the design of healthcare solutions with BC
technology to accomplish security. This study presents a Blockchain Assisted Intrusion Detection and Data Classification on
Smart Healthcare Management System (BAIDDC-SHMS). The presented BAIDDC-SHMS technique initially performs
intrusion detection using spiral search optimization (SPO) with a deep stacked sparse autoencoder (DSSAE) model. Besides,
the BAIDDC-SHMS model involves EfficientNet feature extraction with a softmax (SM) classifier for the medical image
classification process. The SPO algorithm was utilized for optimal modification of the hyperparameters of the DSSAE method
and thereby raised the intrusion detection efficacy of the DSSAE algorithm. To demonstrate the betterment of the BAIDDCSHMS model, a wide range of experimental analyses can be conducted, and the outcomes are inspected under different
measures. The comprehensive comparison study emphasized the superior performance of the BAIDDC-SHMS algorithm over
other approaches.
Keywords
Blockchain, Security, Healthcare management, Deep learning, Intrusion detection, Privacy.
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