Securing healthcare data in industrial cyber-physical systems using combining deep learning and blockchain technology
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F24%3A10253879" target="_blank" >RIV/61989100:27240/24:10253879 - isvavai.cz</a>
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S0952197623017967" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0952197623017967</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.engappai.2023.107612" target="_blank" >10.1016/j.engappai.2023.107612</a>
Alternative languages
Result language
angličtina
Original language name
Securing healthcare data in industrial cyber-physical systems using combining deep learning and blockchain technology
Original language description
Industrial cyber-physical systems (ICPS) are emerging platforms for various industrial applications. For instance, remote healthcare monitoring, real-time healthcare data generation, and many other applications have been integrated into the ICPS platform. These healthcare applications encompass workflow tasks, such as processing within hospitals, laboratory tests, and insurance companies for patient payments, which necessitate a sequential flow. The external wireless, fog, and cloud services within ICPS face security issues that impact end-users' healthcare applications. Blockchain technology offers an optimal solution for ICPS-enabled applications. However, blockchain technology for the ICPS platform is still vulnerable to cyberattacks, while microservices are essential for executing applications. This paper introduces the novel "Pattern-Proof Malware Validation" (PoPMV) algorithm designed for blockchain in ICPS. It exploits a deep learning model (LSTM) with reinforcement learning techniques to receive feedback and rewards in real-time. The primary objective is to mitigate security vulnerabilities, enhance processing speed, identify both familiar and unfamiliar attacks, and optimize the functionality of ICPS. Simulations demonstrate the superiority of the proposed approach compared to current blockchain frameworks, showcasing dynamic allocation of microservices and improved security with comprehensive attack detection by 30%. (C) 2023 The Author(s)
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20200 - Electrical engineering, Electronic engineering, Information engineering
Result continuities
Project
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Continuities
O - Projekt operacniho programu
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Engineering Applications of Artificial Intelligence
ISSN
0952-1976
e-ISSN
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Volume of the periodical
129
Issue of the periodical within the volume
2024
Country of publishing house
GB - UNITED KINGDOM
Number of pages
11
Pages from-to
"nestrankovano"
UT code for WoS article
001134750700001
EID of the result in the Scopus database
2-s2.0-85179001796