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 is presented.
© 2023 by the authors.
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International (CC BY 4.0) license.
Original Publication Citation
Bin Hulayyil, S., Li, S., & Xu, L. D. (2023). Machine-learning-based vulnerability detection and classification in Internet of Things device security. Electronics, 12(18), 1-24, Article 3927. https://doi.org/10.3390/electronics12183927
Bin Hulayyil, Sarah; Li, Shancang; and Xu, Li Da, "Machine-Learning-Based Vulnerability Detection and Classification in Internet of Things Device Security" (2023). Information Technology & Decision Sciences Faculty Publications. 91.