Secure medical digital twin via human-centric interaction and cyber vulnerability resilience
As a fundamental service in near future, medical digital twin (MDT) is the virtual replica of a person. MDT applies new technologies of IoT, AI and big data to predict the state of health and offer clinical suggestions. It is crucial to secure medical digital twins through deep understanding of the...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2022-12-01
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| Series: | Connection Science |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/09540091.2021.2013443 |
| _version_ | 1797684029697294336 |
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| author | Jun Zhang Yonghang Tai |
| author_facet | Jun Zhang Yonghang Tai |
| author_sort | Jun Zhang |
| collection | DOAJ |
| description | As a fundamental service in near future, medical digital twin (MDT) is the virtual replica of a person. MDT applies new technologies of IoT, AI and big data to predict the state of health and offer clinical suggestions. It is crucial to secure medical digital twins through deep understanding of the design of digital twins and applying the new vulnerability tolerant approach. In this paper, we present a new medical digital twin, which systematically combines Haptic-AR navigation and deep learning techniques to achieve virtual replica and cyber–human interaction. We report an innovative study of the cyber–human interaction performance in different scenarios. With the focus on cyber resilience, a new solution of vulnerability tolerant is the must in the real-world MDT scenarios. We propose a novel scheme for recognising and fixing MDT vulnerabilities, in which a new CodeBERT-based neural network is applied to better understand risky code and capture cybersecurity semantics. We develop a prototype of the new MDT and collect several real-world datasets. In the empirical study, a number of well-designed experiments are conducted to evaluate the performance of digital twin, cyber–human interaction and vulnerability detection. The results confirm that our new platform works well, can support clinical decision and has great potential in cyber resilience. |
| first_indexed | 2024-03-12T00:24:39Z |
| format | Article |
| id | doaj.art-ccb5d0c9c363432ebf682378535c2671 |
| institution | Directory Open Access Journal |
| issn | 0954-0091 1360-0494 |
| language | English |
| last_indexed | 2024-03-12T00:24:39Z |
| publishDate | 2022-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Connection Science |
| spelling | doaj.art-ccb5d0c9c363432ebf682378535c26712023-09-15T10:48:00ZengTaylor & Francis GroupConnection Science0954-00911360-04942022-12-0134189591010.1080/09540091.2021.20134432013443Secure medical digital twin via human-centric interaction and cyber vulnerability resilienceJun Zhang0Yonghang Tai1Yunnan Normal UniversityYunnan Normal UniversityAs a fundamental service in near future, medical digital twin (MDT) is the virtual replica of a person. MDT applies new technologies of IoT, AI and big data to predict the state of health and offer clinical suggestions. It is crucial to secure medical digital twins through deep understanding of the design of digital twins and applying the new vulnerability tolerant approach. In this paper, we present a new medical digital twin, which systematically combines Haptic-AR navigation and deep learning techniques to achieve virtual replica and cyber–human interaction. We report an innovative study of the cyber–human interaction performance in different scenarios. With the focus on cyber resilience, a new solution of vulnerability tolerant is the must in the real-world MDT scenarios. We propose a novel scheme for recognising and fixing MDT vulnerabilities, in which a new CodeBERT-based neural network is applied to better understand risky code and capture cybersecurity semantics. We develop a prototype of the new MDT and collect several real-world datasets. In the empirical study, a number of well-designed experiments are conducted to evaluate the performance of digital twin, cyber–human interaction and vulnerability detection. The results confirm that our new platform works well, can support clinical decision and has great potential in cyber resilience.http://dx.doi.org/10.1080/09540091.2021.2013443healthcaredigital twindeep learningcyber resilienceinternet of things |
| spellingShingle | Jun Zhang Yonghang Tai Secure medical digital twin via human-centric interaction and cyber vulnerability resilience Connection Science healthcare digital twin deep learning cyber resilience internet of things |
| title | Secure medical digital twin via human-centric interaction and cyber vulnerability resilience |
| title_full | Secure medical digital twin via human-centric interaction and cyber vulnerability resilience |
| title_fullStr | Secure medical digital twin via human-centric interaction and cyber vulnerability resilience |
| title_full_unstemmed | Secure medical digital twin via human-centric interaction and cyber vulnerability resilience |
| title_short | Secure medical digital twin via human-centric interaction and cyber vulnerability resilience |
| title_sort | secure medical digital twin via human centric interaction and cyber vulnerability resilience |
| topic | healthcare digital twin deep learning cyber resilience internet of things |
| url | http://dx.doi.org/10.1080/09540091.2021.2013443 |
| work_keys_str_mv | AT junzhang securemedicaldigitaltwinviahumancentricinteractionandcybervulnerabilityresilience AT yonghangtai securemedicaldigitaltwinviahumancentricinteractionandcybervulnerabilityresilience |