Cyber Security Intrusion Detection for Agriculture 4.0: Machine Learning-Based Solutions, Datasets, and Future Directions

doi:10.1109/JAS.2021.1004344

CORC > 自动化研究所 > 中国科学院自动化研究所 > 学术期刊 > IEEE/CAA Journal of Automatica Sinica

	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
刊名	IEEE/CAA Journal of Automatica Sinica
	2022
卷号	9 期号:3 页码:407-436
关键词	Agriculture 4.0 cyber security intrusion detection system machine learning approaches smart agriculture
ISSN号	2329-9266
DOI	10.1109/JAS.2021.1004344
英文摘要	In 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.
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/47205]
专题	自动化研究所_学术期刊_IEEE/CAA Journal of Automatica Sinica
推荐引用方式 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.