[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119307-en":3,"doc-seo-119307-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119307,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",6,"Technology","Quantum Machine Learning for Security Assessment in the Internet of Medical Things (IoMT)","Internet of Medical Things (IoMT) is a connected ecosystem of sensors, actuators, and cyber-physical devices that enables autonomous medical monitoring, analysis, and reporting. Limited onboard computing resources and the need to protect large volumes of sensitive health data create security vulnerabilities and motivate improved assessment methods. This work reviews traditional and quantum machine learning for IoMT vulnerability assessment and introduces a fused semi-supervised model. Extensive experiments confirm competitive results and indicate value of quantum learning for future IoMT security applications.","future internet  \nArticle  \nQuantum Machine Learning for Security Assessment in the Internet of Medical Things (IoMT)  \nAnand Singh Rajawat 1, S. B. Goyal 2, Pradeep Bedi 3, Tony Jan 4, Md Whaiduzzaman 5 and Mukesh Prasad 6, *  \nCitation: Rajawat, A.S.; Goyal, S.B.; Bedi, P.; Jan, T.; Whaiduzzaman, M.; Prasad, M. Quantum Machine Learning for Security Assessment in the Internet of Medical Things (IoMT). Future Internet 2023, 15, 271 . [https://doi.org/10.3390/](https://doi.org/10.3390/)fi15080271  \nAcademic Editor: Matthew Pediaditis  \nReceived: 13 July 2023  \nRevised: 8 August 2023  \nAccepted: 13 August 2023  \nPublished: 15 August 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 School of Computer Sciences & Engineering, Sandip University, Nashik 422213, India  \n2 Faculty of Information Technology, City University, Petaling Jaya 46100, Malaysia  \n3 School of Computing Science and Engineering, Galgotias University, Greater Noida 203201, India  \n4 Centre for Artificial Intelligence Research and Optimization, Design and Creative Technology Vertical, Torrens University, Sydney 2007, Australia  \n5 School of Information Technology, Torrens University, Brisbane 4006, Australia  \n6 School of Computer Science, Faculty of Engineering and IT (FEIT), University of Technology Sydney, Sydney 2007, Australia  \n* [Correspondence: mukesh.prasad@uts.edu.au](Correspondence: mukesh.prasad@uts.edu.au)  \nAbstract: 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.  \nKeywords: vulnerability prediction; Internet of Things; quantum machine learning; Internet of Medical Things  \n1. Introduction  \nSmart devices can be used to improve a wide range of services in ubiquitous computing. The gadgets that make up the “things” in the Internet of Things (IoT) can exist in any household, company, and city. The services based on IoT bring benefits but also security vulnerabilities in the form of blind spots and increased attack surfaces [1] . Smart devices with security vulnerabilities can allow malicious users to infiltrate private computing networks. Most IoT devices are vulnerable to cyber-attacks because they are not equipped with sufficient security features. These IoT networks are vulnerable to several factors, such as technological limitations and the users associated with the IoT applications [2] .  \nFirstly, there are security vulnerabilities in IoT devices on the market because of their hardware limitations. IoT devices can onl","cbCaijTjb6t6oALm","https://ap.wps.com/l/cbCaijTjb6t6oALm","pdf",561096,1,21,"English","en",105,"# Introduction\n## IoMT and security challenges\n## Hardware and protocol limitations\n## User control and security assurance\n# IoMT security assessment approach\n## Attacks on commercial IoMT devices\n## Review of processors and communication protocols\n## Cryptographic hardware/software and privacy concerns","[{\"question\":\"Why is security assessment important in the Internet of Medical Things (IoMT)?\",\"answer\":\"IoMT devices support autonomous medical monitoring and handle sensitive health data, but they often lack sufficient onboard resources for security assurance. This increases vulnerabilities and the need for proactive assessment methods.\"},{\"question\":\"What does the paper contribute to IoMT vulnerability assessment?\",\"answer\":\"It provides a comprehensive review of both traditional and quantum machine learning techniques for IoMT vulnerability assessment. It also proposes a fused semi-supervised learning model and evaluates it against state-of-the-art approaches.\"},{\"question\":\"What do the experimental results indicate about quantum machine learning for IoMT security?\",\"answer\":\"The experiments show competitive performance of the proposed fused semi-supervised model compared with traditional and quantum state-of-the-art models. The results also highlight the usefulness of quantum machine learning for IoMT security assessments and future applications.\"}]","Quantum Machine Learning for Security Assessment in the Internet of Medical Things (IoMT) | PDF",1785723636,53,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"quantum-machine-learning-for-security-assessment-in-the-internet-of-medical-things-iomt","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/technology/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/quantum-machine-learning-for-security-assessment-in-the-internet-of-medical-things-iomt/119307/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is security assessment important in the Internet of Medical Things (IoMT)?","Question",{"text":75,"@type":76},"IoMT devices support autonomous medical monitoring and handle sensitive health data, but they often lack sufficient onboard resources for security assurance. This increases vulnerabilities and the need for proactive assessment methods.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What does the paper contribute to IoMT vulnerability assessment?",{"text":80,"@type":76},"It provides a comprehensive review of both traditional and quantum machine learning techniques for IoMT vulnerability assessment. It also proposes a fused semi-supervised learning model and evaluates it against state-of-the-art approaches.",{"name":82,"@type":73,"acceptedAnswer":83},"What do the experimental results indicate about quantum machine learning for IoMT security?",{"text":84,"@type":76},"The experiments show competitive performance of the proposed fused semi-supervised model compared with traditional and quantum state-of-the-art models. 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