RBEF: Ransomware Efficient Public Blockchain Framework for Digital Healthcare Application

Sensors (Basel). 2023 Jun 1;23(11):5256. doi: 10.3390/s23115256.

Abstract

These days, the use of digital healthcare has been growing in practice. Getting remote healthcare services without going to the hospital for essential checkups and reports is easy. It is a cost-saving and time-saving process. However, digital healthcare systems are suffering from security and cyberattacks in practice. Blockchain technology is a promising technology that can process valid and secure remote healthcare data among different clinics. However, ransomware attacks are still complex holes in blockchain technology and prevent many healthcare data transactions during the process on the network. The study presents the new ransomware blockchain efficient framework (RBEF) for digital networks, which can identify transaction ransomware attacks. The objective is to minimize transaction delays and processing costs during ransomware attack detection and processing. The RBEF is designed based on Kotlin, Android, Java, and socket programming on the remote process call. RBEF integrated the cuckoo sandbox static and dynamic analysis application programming interface (API) to handle compile-time and runtime ransomware attacks in digital healthcare networks. Therefore, code-, data-, and service-level ransomware attacks are to be detected in blockchain technology (RBEF). The simulation results show that the RBEF minimizes transaction delays between 4 and 10 min and processing costs by 10% for healthcare data compared to existing public and ransomware efficient blockchain technologies healthcare systems.

Keywords: RBEF; blockchain; delays; ransomware; sandbox; static and dynamic analysis.

MeSH terms

  • Blockchain*
  • Computer Simulation
  • Delivery of Health Care
  • Hospitals
  • Software

Grants and funding

Partial support for this research was developed and provided by Kristiania University College and Chiang Mai University which were collaborated in this research. The author expresses gratitude to the NSRF via the Program Management Unit for Human Resources and Institutional Development, Research and Innovation (Grant number B16F640189), and research group of Embedded System and Computational Science, Chiang Mai University (Grant number R000029859).