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Cyber Security Intrusion Detection for Agriculture 4.0: Machine Learning-Based Solutions, Datasets, and Future Directions
Mohamed Amine Ferrag; Lei Shu; Othmane Friha; Xing Yang
Source PublicationIEEE/CAA Journal of Automatica Sinica
ISSN2329-9266
2022
Volume9Issue:3Pages:407-436
AbstractIn this paper, we review and analyze intrusion detection systems for Agriculture 4.0 cyber security. Specifically, we present cyber security threats and evaluation metrics used in the performance evaluation of an intrusion detection system for Agriculture 4.0. Then, we evaluate intrusion detection systems according to emerging technologies, including, Cloud computing, Fog/Edge computing, Network virtualization, Autonomous tractors, Drones, Internet of Things, Industrial agriculture, and Smart Grids. Based on the machine learning technique used, we provide a comprehensive classification of intrusion detection systems in each emerging technology. Furthermore, we present public datasets, and the implementation frameworks applied in the performance evaluation of intrusion detection systems for Agriculture 4.0. Finally, we outline challenges and future research directions in cyber security intrusion detection for Agriculture 4.0.
KeywordAgriculture 4.0 cyber security intrusion detection system machine learning approaches smart agriculture
DOI10.1109/JAS.2021.1004344
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Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/47205
Collection学术期刊_IEEE/CAA Journal of Automatica Sinica
Recommended Citation
GB/T 7714
Mohamed Amine Ferrag,Lei Shu,Othmane Friha,et al. Cyber Security Intrusion Detection for Agriculture 4.0: Machine Learning-Based Solutions, Datasets, and Future Directions[J]. IEEE/CAA Journal of Automatica Sinica,2022,9(3):407-436.
APA Mohamed Amine Ferrag,Lei Shu,Othmane Friha,&Xing Yang.(2022).Cyber Security Intrusion Detection for Agriculture 4.0: Machine Learning-Based Solutions, Datasets, and Future Directions.IEEE/CAA Journal of Automatica Sinica,9(3),407-436.
MLA Mohamed Amine Ferrag,et al."Cyber Security Intrusion Detection for Agriculture 4.0: Machine Learning-Based Solutions, Datasets, and Future Directions".IEEE/CAA Journal of Automatica Sinica 9.3(2022):407-436.
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