Cyber Security Intrusion Detection for Agriculture 4.0: Machine Learning-Based Solutions, Datasets, and Future Directions | IEEE Journals & Magazine | IEEE Xplore

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


Abstract:

In this paper, we review and analyze intrusion detection systems for Agriculture 4.0 cyber security. Specifically, we present cyber security threats and evaluation metric...Show More

Abstract:

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.
Published in: IEEE/CAA Journal of Automatica Sinica ( Volume: 9, Issue: 3, March 2022)
Page(s): 407 - 436
Date of Publication: 10 December 2021

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