Knowledge Commons of Institute of Automation,CAS
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 | |
发表期刊 | IEEE/CAA Journal of Automatica Sinica |
ISSN | 2329-9266 |
2022 | |
卷号 | 9期号:3页码:407-436 |
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. |
关键词 | Agriculture 4.0 cyber security intrusion detection system machine learning approaches smart agriculture |
DOI | 10.1109/JAS.2021.1004344 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/47205 |
专题 | 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. |
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