Cyber Security Intrusion Detection for Agriculture 4.0: Machine Learning-Based Solutions, Datasets, and Future Directions
文献类型：期刊论文
| 作者 | Mohamed Amine Ferrag; Lei Shu; Othmane Friha; Xing Yang |
| 刊名 | IEEE/CAA Journal of Automatica Sinica
 |
| 出版日期 | 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. |
| 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. |
入库方式： OAI收割
来源：自动化研究所
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