The DOI matches its Crossref record; 56 of the 56 references it cites were found in a registry or an index.
Machine learning techniques for IoT security: Current research and future vision with generative AI and large language models
Open accessAbstract
Despite providing unparalleled connectivity and convenience, the exponential growth of the Internet of Things (IoT) ecosystem has triggered significant cybersecurity concerns. These concerns stem from various factors, including the heterogeneity of IoT devices, widespread deployment, and inherent computational limitations. Integrating emerging technologies to address these concerns becomes imperative as the dynamic IoT landscape evolves. Machine Learning (ML), a rapidly advancing technology, has shown considerable promise in addressing IoT security issues. It has significantly influenced and advanced research in cyber threat detection. This survey provides a comprehensive overview of current trends, methodologies, and challenges in applying machine learning for cyber threat detection in IoT environments. Specifically, we further perform a comparative analysis of state-of-the-art ML-based Intrusion Detection Systems (IDSs) in the landscape of IoT security. In addition, we shed light on the pressing unresolved issues and challenges within this dynamic field. We provide a future vision with Generative AI and large language models to enhance IoT security. The discussions present an in-depth understanding of different cyber threat detection methods, enhancing the knowledge base of researchers and practitioners alike. This paper is a valuable resource for those keen to delve into the evolving world of cyber threat detection leveraging ML and IoT security.
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Cites
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A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security
Al-Garadi, M. A., Mohamed, A., Al-Ali, A. K., Du, X., Ali, I., & Guizani, M. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.2988293 · Cited by 1,392
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The Falcon Series of Open Language ModelsPreprint
Almazrouei, E., Alobeidli, H., Alshamsi, A., Cappelli, A., Cojocaru, R., Debbah, M. A., Goffinet, É., Hesslow, D., Launay, J., Malartic, Q., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2311.16867 · Cited by 953
Network Intrusion Detection for IoT Security Based on Learning Techniques
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Machine learning based solutions for security of Internet of Things (IoT): A survey
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Cited by
Sai, S., Yashvardhan, U., Chamola, V., & Sikdar, B. (2024) · IEEE Access · doi:10.1109/access.2024.3385107 · Cited by 125
Farrukh, Y. A., Wali, S., Khan, I., & Bastian, N. D. (2024) · arXiv · doi:10.48550/arxiv.2408.16021 · Cited by 56
A Comprehensive Survey on Generative AI Solutions in IoT Security
López Delgado, J. L., & López Ramos, J. A. (2024) · Electronics · doi:10.3390/electronics13244965 · Cited by 33
Joshi, A., & Baidya, S. (2026) · Journal of Cybersecurity and Privacy · doi:10.3390/jcp6020063 · Cited by 4
Metadata
- Authors
- Alwahedi, F., Aldhaheri, A., Ferrag, M. A., Battah, A., & Tihanyi, N.
- Venue
- Internet of Things and Cyber-Physical Systems
- Publisher
- Elsevier BV
- Published
- 2024
- Volume
- 4
- Pages
- 167-185
- ISSN
- 2667-3452 (ISSN-L)
- OpenAlex
- W4390561469
- Cited by
- 259
- References
- 56
- FWCI
- 43.91
- Type
- journal article
- Language
- en
- Open access
- diamond
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- Alwahedi, F., Aldhaheri, A., Ferrag, M. A., Battah, A., & Tihanyi, N. (2024). Machine learning techniques for IoT security: Current research and future vision with generative AI and large language models. Internet of Things and Cyber-Physical Systems. https://doi.org/10.1016/j.iotcps.2023.12.003
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