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

被引：0

作者：

Mohamed Amine Ferrag ^{[1
]}

Lei Shu ^{[2
,3
,4
]}

Othmane Friha ^{[5
]}

Xing Yang ^{[6
]}

机构：

[1] Department of Computer Science, Guelma University

[2] the School of Engineering,University of Lincoln

[3] IEEE

[4] the College of Artificial Intelligence, Nanjing Agricultural University

[5] the College of Engineering, Nanjing Agricultural University

[6] the Networks and Systems Laboratory (LRS), University of Badji Mokhtar-Annaba

来源：

IEEE/CAAJournalofAutomaticaSinica | 2022年 / 9卷 / 03期

基金：

中国国家自然科学基金;

关键词：

D O I：

暂无

中图分类号：

S126 [电子技术、计算机技术在农业上的应用]; TP181 [自动推理、机器学习]; TP393.08 [];

学科分类号：

082804 ; 0839 ; 1402 ;

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.

引用

页码：407 / 436

页数：30

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