Machine learning techniques for IoT security: Current research and future vision with generative AI and large language models
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 co...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2024-01-01
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| Series: | Internet of Things and Cyber-Physical Systems |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667345223000585 |
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| author | Fatima Alwahedi Alyazia Aldhaheri Mohamed Amine Ferrag Ammar Battah Norbert Tihanyi |
| author_facet | Fatima Alwahedi Alyazia Aldhaheri Mohamed Amine Ferrag Ammar Battah Norbert Tihanyi |
| author_sort | Fatima Alwahedi |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-cf07860bff774d24916703f14a10c89a |
| institution | Kabale University |
| issn | 2667-3452 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Internet of Things and Cyber-Physical Systems |
| spelling | doaj-art-cf07860bff774d24916703f14a10c89a2025-01-27T04:22:35ZengKeAi Communications Co., Ltd.Internet of Things and Cyber-Physical Systems2667-34522024-01-014167185Machine learning techniques for IoT security: Current research and future vision with generative AI and large language modelsFatima Alwahedi0Alyazia Aldhaheri1Mohamed Amine Ferrag2Ammar Battah3Norbert Tihanyi4Technology Innovation Institute, 9639, Masdar City, Abu Dhabi, United Arab EmiratesTechnology Innovation Institute, 9639, Masdar City, Abu Dhabi, United Arab EmiratesCorresponding author.; Technology Innovation Institute, 9639, Masdar City, Abu Dhabi, United Arab EmiratesTechnology Innovation Institute, 9639, Masdar City, Abu Dhabi, United Arab EmiratesTechnology Innovation Institute, 9639, Masdar City, Abu Dhabi, United Arab EmiratesDespite 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.http://www.sciencedirect.com/science/article/pii/S2667345223000585Cyber threat detectionIntrusion detectionIoTMachine learningSecurity |
| spellingShingle | Fatima Alwahedi Alyazia Aldhaheri Mohamed Amine Ferrag Ammar Battah Norbert Tihanyi 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 Cyber threat detection Intrusion detection IoT Machine learning Security |
| title | Machine learning techniques for IoT security: Current research and future vision with generative AI and large language models |
| title_full | Machine learning techniques for IoT security: Current research and future vision with generative AI and large language models |
| title_fullStr | Machine learning techniques for IoT security: Current research and future vision with generative AI and large language models |
| title_full_unstemmed | Machine learning techniques for IoT security: Current research and future vision with generative AI and large language models |
| title_short | Machine learning techniques for IoT security: Current research and future vision with generative AI and large language models |
| title_sort | machine learning techniques for iot security current research and future vision with generative ai and large language models |
| topic | Cyber threat detection Intrusion detection IoT Machine learning Security |
| url | http://www.sciencedirect.com/science/article/pii/S2667345223000585 |
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