Open Access

ARTICLE

Secured Cyber Security Algorithm for Healthcare System Using Blockchain Technology

by D. Doreen Hephzibah Miriam¹, Deepak Dahiya², Nitin³, C. R. Rene Robin^4,*

1 Computational Intelligence Research Foundation (CIRF), Chennai, 600 023, Tamilnadu, India
2 College of Computer and Information Sciences, Majmaah University, Majmaah, 11952, Saudi Arabia
3 Department of Electrical Engineering and Computer Science, College of Engineering and Applied Science, University of Cincinnati, Cincinnati, 45221, OH, United States
4 Department of Computer Science and Engineering, Sri Sairam Engineering College, Chennai, 600 044, Tamilnadu, India

* Corresponding Author: C. R. Rene Robin. Email:

Intelligent Automation & Soft Computing 2023, 35(2), 1889-1906. https://doi.org/10.32604/iasc.2023.028850

Received 19 February 2022; Accepted 29 March 2022; Issue published 19 July 2022

Abstract

Blockchain technology is critical in cyber security. The most recent cryptographic strategies may be hacked as efforts are made to build massive electronic circuits. Because of the ethical and legal implications of a patient’s medical data, cyber security is a critical and challenging problem in healthcare. The image secrecy is highly vulnerable to various types of attacks. As a result, designing a cyber security model for healthcare applications necessitates extra caution in terms of data protection. To resolve this issue, this paper proposes a Lionized Golden Eagle based Homomorphic Elapid Security (LGE-HES) algorithm for the cybersecurity of blockchain in healthcare networks. The blockchain algorithm preserves the security of the medical image by performing hash function. The execution of this research is carried out by MATLAB software. The suggested framework was tested utilizing Computed Tumor (CT) pictures and MRI image datasets, and the simulation results revealed the proposed model’s profound implications. During the simulation, 94.9% of malicious communications were recognized and identified effectively, according to the total outcomes statistics. The suggested model’s performance is also compared to that of standard approaches in terms of Root Mean Square Error (RMSE), Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), time complexity, and other factors.

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Keywords

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