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Volume 9 Issue 3
Mar.  2022

IEEE/CAA Journal of Automatica Sinica

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Article Contents
Mohamed Amine Ferrag, Lei Shu, Othmane Friha and Xing Yang, "Cyber Security Intrusion Detection for Agriculture 4.0: Machine Learning-Based Solutions, Datasets, and Future Directions," IEEE/CAA J. Autom. Sinica, vol. 9, no. 3, pp. 407-436, Mar. 2022. doi: 10.1109/JAS.2021.1004344
Citation: Mohamed Amine Ferrag, Lei Shu, Othmane Friha and Xing Yang, "Cyber Security Intrusion Detection for Agriculture 4.0: Machine Learning-Based Solutions, Datasets, and Future Directions," IEEE/CAA J. Autom. Sinica, vol. 9, no. 3, pp. 407-436, Mar. 2022. doi: 10.1109/JAS.2021.1004344

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

doi: 10.1109/JAS.2021.1004344
Funds:  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)
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  • 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.


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    • We present the cyber security threats and evaluation metrics used in the performance evaluation of IDSs for Agriculture 4.0
    • We provide a comprehensive classification and in-depth analysis of machine learning and deep learning based IDSs for cyber security in Agriculture 4.0
    • We provide a detailed description of the current best practices, implementation frameworks, and public datasets used in the performance evaluation of IDSs for Agriculture 4.0
    • We highlight remaining challenges and future research directions in cyber security intrusion detection for Agriculture 4.0


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