Quantum Machine Learning for Security Assessment in the Internet of Medical Things (IoMT)
Internet of Medical Things (IoMT) is an ecosystem composed of connected electronic items such as small sensors/actuators and other cyber-physical devices (CPDs) in medical services. When these devices are linked together, they can support patients through medical monitoring, analysis, and reporting...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Future Internet |
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| Online Access: | https://www.mdpi.com/1999-5903/15/8/271 |
| _version_ | 1797584719945138176 |
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| author | Anand Singh Rajawat S. B. Goyal Pradeep Bedi Tony Jan Md Whaiduzzaman Mukesh Prasad |
| author_facet | Anand Singh Rajawat S. B. Goyal Pradeep Bedi Tony Jan Md Whaiduzzaman Mukesh Prasad |
| author_sort | Anand Singh Rajawat |
| collection | DOAJ |
| description | Internet of Medical Things (IoMT) is an ecosystem composed of connected electronic items such as small sensors/actuators and other cyber-physical devices (CPDs) in medical services. When these devices are linked together, they can support patients through medical monitoring, analysis, and reporting in more autonomous and intelligent ways. The IoMT devices; however, often do not have sufficient computing resources onboard for service and security assurance while the medical services handle large quantities of sensitive and private health-related data. This leads to several research problems on how to improve security in IoMT systems. This paper focuses on quantum machine learning to assess security vulnerabilities in IoMT systems. This paper provides a comprehensive review of both traditional and quantum machine learning techniques in IoMT vulnerability assessment. This paper also proposes an innovative fused semi-supervised learning model, which is compared to the state-of-the-art traditional and quantum machine learning in an extensive experiment. The experiment shows the competitive performance of the proposed model against the state-of-the-art models and also highlights the usefulness of quantum machine learning in IoMT security assessments and its future applications. |
| first_indexed | 2024-03-10T23:55:37Z |
| format | Article |
| id | doaj.art-baaf30cf2c95486590ba386a190667a2 |
| institution | Directory Open Access Journal |
| issn | 1999-5903 |
| language | English |
| last_indexed | 2024-03-10T23:55:37Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Future Internet |
| spelling | doaj.art-baaf30cf2c95486590ba386a190667a22023-11-19T01:12:25ZengMDPI AGFuture Internet1999-59032023-08-0115827110.3390/fi15080271Quantum Machine Learning for Security Assessment in the Internet of Medical Things (IoMT)Anand Singh Rajawat0S. B. Goyal1Pradeep Bedi2Tony Jan3Md Whaiduzzaman4Mukesh Prasad5School of Computer Sciences & Engineering, Sandip University, Nashik 422213, IndiaFaculty of Information Technology, City University, Petaling Jaya 46100, MalaysiaSchool of Computing Science and Engineering, Galgotias University, Greater Noida 203201, IndiaCentre for Artificial Intelligence Research and Optimization, Design and Creative Technology Vertical, Torrens University, Sydney 2007, AustraliaSchool of Information Technology, Torrens University, Brisbane 4006, AustraliaSchool of Computer Science, Faculty of Engineering and IT (FEIT), University of Technology Sydney, Sydney 2007, AustraliaInternet of Medical Things (IoMT) is an ecosystem composed of connected electronic items such as small sensors/actuators and other cyber-physical devices (CPDs) in medical services. When these devices are linked together, they can support patients through medical monitoring, analysis, and reporting in more autonomous and intelligent ways. The IoMT devices; however, often do not have sufficient computing resources onboard for service and security assurance while the medical services handle large quantities of sensitive and private health-related data. This leads to several research problems on how to improve security in IoMT systems. This paper focuses on quantum machine learning to assess security vulnerabilities in IoMT systems. This paper provides a comprehensive review of both traditional and quantum machine learning techniques in IoMT vulnerability assessment. This paper also proposes an innovative fused semi-supervised learning model, which is compared to the state-of-the-art traditional and quantum machine learning in an extensive experiment. The experiment shows the competitive performance of the proposed model against the state-of-the-art models and also highlights the usefulness of quantum machine learning in IoMT security assessments and its future applications.https://www.mdpi.com/1999-5903/15/8/271vulnerability predictionInternet of Thingsquantum machine learningInternet of Medical Things |
| spellingShingle | Anand Singh Rajawat S. B. Goyal Pradeep Bedi Tony Jan Md Whaiduzzaman Mukesh Prasad Quantum Machine Learning for Security Assessment in the Internet of Medical Things (IoMT) Future Internet vulnerability prediction Internet of Things quantum machine learning Internet of Medical Things |
| title | Quantum Machine Learning for Security Assessment in the Internet of Medical Things (IoMT) |
| title_full | Quantum Machine Learning for Security Assessment in the Internet of Medical Things (IoMT) |
| title_fullStr | Quantum Machine Learning for Security Assessment in the Internet of Medical Things (IoMT) |
| title_full_unstemmed | Quantum Machine Learning for Security Assessment in the Internet of Medical Things (IoMT) |
| title_short | Quantum Machine Learning for Security Assessment in the Internet of Medical Things (IoMT) |
| title_sort | quantum machine learning for security assessment in the internet of medical things iomt |
| topic | vulnerability prediction Internet of Things quantum machine learning Internet of Medical Things |
| url | https://www.mdpi.com/1999-5903/15/8/271 |
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