Machine-learning-based vulnerability detection and classification in Internet of Things device security

Hulayyil, Sarah, Li, Shancang and Xu, Lida 2023. Machine-learning-based vulnerability detection and classification in Internet of Things device security. Electronics 12 (18) , 3927. 10.3390/electronics12183927

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Abstract

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 workflow 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 presented.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Publisher:	MDPI
ISSN:	2079-9292
Date of First Compliant Deposit:	20 September 2023
Date of Acceptance:	10 September 2023
Last Modified:	30 Sep 2023 07:32
URI:	https://orca.cardiff.ac.uk/id/eprint/162617

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