Secure IoMT Pattern Recognition and Exploitation for Multimedia Information Processing using Private Blockchain and Fuzzy Logic

Author:

Ghazal Taher M.121,Hasan Mohammad Kamrul1,Abdallah Siti Norul Huda1,Abubakkar Khairul Azmi1

Affiliation:

1. Center for cyber Security, Faculty of Information Science and Technology, UniversitiKebangsaan Malaysia (UKM), 43600 Bangi, Selangor, Malaysia

2. School of Information Technology, Skyline University college, University City Sharjah, 1797, Sharjah, UAE

Abstract

The Internet of Medical Things (IoMT) is a definite IoT connecting atmosphere that contracts with communication via intelligent medical equipment. Activity detection, motion tracking, information extraction, consumer retrieval, etc., have all been solved due to advances in the autonomous study of human behavior from multimedia information processing. Though the IoT connecting atmosphere enables and provisions our daily actions, it too has certain disadvantages. IoTgrieves from numerous safety and confidentiality challenges, such as reiteration, impersonation, man-in-the-middle, remote hijacking, privileged-insider attack, denial of service (DoS) attacks, password guessing, and malware bouts. Hence, in this paper, Private Blockchain and Fuzzy Logic based Attack Detection system (PBFL-ADS) has been proposed for secure IoMT disease prediction using Multimedia Information Processing techniques. The proposed method utilizes Bayesian inference-based trust administration to perceived malevolent nodes in Health Smartphone Structures (HSS) for PBFL-ADS. This paper focuses on the specific form of IoMT called HSS because smartphones have been widely used in the healthcare profession. Then, blockchains have been utilized to improve Bayesian trust management's efficacy in detecting hostile nodes in HSSs. The effectiveness of the suggested technique has been assessed, and test results show that blockchain technology helps identify fraudulent nodes with an acceptable workload. The proposed PBFL-ADS method increases the HS2 scenario 1.1, processor utilization at nodes 33.5%, pattern recognition ratio 92.1%.and server utilization 40.1%.

Publisher

Association for Computing Machinery (ACM)

Subject

General Computer Science

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