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The study performs a comprehensive survey of machine-learning methods and tools for vulnerability detection across multiple datasets, analyzes likely vulnerabilities across IoT architecture layers, and outlines the end-to-end ML workflow. A machine-learning-based vulnerability detection and mitigation framework is proposed, alongside a review of recent research trends.","ORCA – Online Research @ Cardiff  \nThis is an Open Access document downloaded from ORCA, Cardiff University's institutional repository: [https://orca.cardiff.ac.uk/id/eprint/162617/](https://orca.cardiff.ac.uk/id/eprint/162617/)  \nThis is the author’s version of a work that was submitted to / accepted for publication.  \nCitation for final published version:  \nHulayyil, Sarah, Li, Shancang and Xu, Lida 2023. Machine-learning-based vulnerability detection and classification in Internet of Things device security. Electronics 12 (18) , 3927.  \n10.3390/electronics12183927  \nPublishers page: [https://doi.org/10.3390/electronics12183927](https://doi.org/10.3390/electronics12183927)  \nPlease note:  \nChanges made as a result of publishing processes such as copy-editing, formatting and page numbers may not be reflected in this version. For the definitive version of this publication, please refer to the published source. You are advised to consult the publisher’s version if you wish to cite  \nthis paper.  \nThis version is being made available in accordance with publisher policies. See [http://orca.cf.ac.uk/policies.html](http://orca.cf.ac.uk/policies.html) for usage policies. Copyright and moral rights for publications made available in ORCA are retained by the copyright holders.  \n electronics   \nArticle  \nMachine-Learning-Based Vulnerability Detection and Classi􀀂cation in Internet of Things Device Security  \nSarah Bin Hulayyil 1,2, Shancang Li 1,* and Lida Xu 3  \nCitation: Bin Hulayyil, S.; Li, S.; Xu, L. Machine-Learning-Based Vulnerability Detection and Classi􀀂cation in Internet of Things Device Security. Electronics 2023, 12, 3927. [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)electronics12183927  \nAcademic Editor: Hung-Yu Chien  \nReceived: 30 July 2023  \nRevised: 6 September 2023  \nAccepted: 8 September 2023  \nPublished: 18 September 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 Science and Informatics, Cardiff University, Cardiff CF10 3AT, UK ; [binhulayyilsh1@cardiff.ac.uk](binhulayyilsh1@cardiff.ac.uk)  \n2 College of Applied Studies and Community Service, King Saud University, Riyadh 11451, Saudi Arabia  \n3 Department of Information Technology & Decision Sciences, Old Dominion University, Norfolk, VA 23529, USA  \n* Correspondence: [lis117@cardiff.ac.uk](lis117@cardiff.ac.uk)  \nAbstract: Detecting cyber security vulnerabilities in the Internet of Things (IoT) devices before they are exploited is increasingly challenging and is one of the key technologies to protect IoT devices from cyber attacks. This work conducts a comprehensive survey to investigate the methods and tools used in vulnerability detection in IoT environments utilizing machine learning techniques on various datasets, i.e., IoT23 . During this study, the common potential vulnerabilities of IoT architectures are analyzed on each layer and the machine learning work􀀃ow is described for detecting IoT vulnerabilities. A vulnerability detection and mitigation framework was proposed for machine learning-based vulnerability detection in IoT environments, and a review of recent research trends is presented.  \nKeywords: IoT security; vulnerability detection; cyber attacks; device security  \n1. Introduction  \nThe growth of technologies such as arti􀀂cial intelligence (AI), smart sensors, cloud computing, the sixth-generation wireless (6G), and edge computing has signi􀀂cantly enhanced the capability of the Internet of Things (IoT), which is successfully transforming our daily lives through developments such as smart homes, smart cities, Industry 4.0, healthcare, and more [1,2] . They are now becoming broadly","cbCaiid0PJbbrTqx","https://ap.wps.com/l/cbCaiid0PJbbrTqx","pdf",927560,1,25,"English","en",105,"# Introduction\n## IoT growth and security challenges\n## IoT architecture layers and core security properties\n# Related Work and Survey Scope\n## Machine learning methods and tools for vulnerability detection\n## Datasets and evaluation scope\n# Proposed ML-Based Framework\n## Layer-wise vulnerability analysis\n## Vulnerability detection and mitigation workflow\n# Research Trends","[{\"question\":\"Why is vulnerability detection in IoT devices challenging?\",\"answer\":\"IoT systems face increasing complexity as they expand with advanced technologies, and securing all data transfers and storage across constrained layers is difficult. Weaknesses in core security properties can enable multiple attack types.\"},{\"question\":\"What does the document cover in the survey?\",\"answer\":\"It surveys machine-learning-based vulnerability detection approaches for IoT environments, including methods and tools used across various datasets. It also analyzes common potential vulnerabilities across IoT architecture layers and describes the ML workflow.\"},{\"question\":\"What framework is proposed?\",\"answer\":\"A vulnerability detection and mitigation framework is proposed for machine-learning-based vulnerability detection in IoT environments. The document also presents recent research trends in this area.\"}]","Machine-Learning-Based Vulnerability Detection and Classification in Internet of Things Device Security | PDF",1785680241,63,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"machine-learning-based-vulnerability-detection-and-classification-in-internet-of-things-device-security","",{"@graph":36,"@context":86},[37,54,69],{"@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/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-based-vulnerability-detection-and-classification-in-internet-of-things-device-security/117895/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05","2026-08-02",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why is vulnerability detection in IoT devices challenging?","Question",{"text":76,"@type":77},"IoT systems face increasing complexity as they expand with advanced technologies, and securing all data transfers and storage across constrained layers is difficult. 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