首页|期刊导航|自动化学报（英文版）|Cyber Security Intrusion Detection for Agriculture 4.0: Machine Learning-Based Solutions, Datasets, and Future Directions

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

自动化学报（英文版）2022，Vol.9Issue(3)：407-436,30.

自动化学报（英文版）2022，Vol.9Issue(3)：407-436,30.DOI:10.1109/JAS.2021.1004344

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

Mohamed Amine Ferrag ¹Lei Shu ²Othmane Friha ³Xing Yang⁴

作者信息

1. Department of Computer Science,Guelma University,B.P.401,24000,Algeria
2. College of Artificial Intelligence,Nanjing Agricultural University,Nanjing 210031,China
3. School of Engineering,University of Lincoln,Lincoln LN67TS,UK
4. Networks and Systems Laboratory(LRS),University of Badji Mokhtar-Annaba,B.P.12,Annaba 23000,Algeria
折叠

摘要

关键词

Agriculture 4.0/cyber security/intrusion detection system/machine learning approaches/smart agriculture

Key words

Agriculture 4.0/cyber security/intrusion detection system/machine learning approaches/smart agriculture

引用本文复制引用

Mohamed Amine Ferrag,Lei Shu,Othmane Friha,Xing Yang..Cyber Security Intrusion Detection for Agriculture 4.0: Machine Learning-Based Solutions, Datasets, and Future Directions[J].自动化学报（英文版）,2022,9(3):407-436,30.

基金项目

This work was supported in part by the Research Start-Up Fund for Talent Researcher of Nanjing Agricultural University(77H0603)and in part by the National Natural Science Foundation of China(62072248). （77H0603）

自动化学报（英文版）

OACSCDCSTPCDEI

ISSN：2329-9266

访问量0

下载量0

段落导航