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

10.1109/TII.2020.3003910

10.1109/COMST.2019.2938259

2020, Industry 4.0 and cybersecurity: Managing risk in an age of connected production

10.1109/ACCESS.2020.2988663

10.1109/ITIA50152.2020.9312286

10.1016/j.future.2018.04.027

10.1109/JAS.2017.7510754

10.1109/JAS.2016.7510178

10.1016/j.jisa.2019.102419

10.1109/COMST.2015.2494502

10.1007/s10586-017-1117-8

10.1109/COMST.2020.2988293

10.1109/COMST.2018.2847722

10.1016/j.comnet.2019.01.023

10.1109/COMST.2019.2896380

10.3390/app9204396

10.1007/s12083-017-0630-0

10.1109/GCWkshps50303.2020.9367477

10.1002/ett.4150

10.1016/j.jnca.2021.102983

10.1016/j.jisa.2020.102500

10.3233/ais-170440

10.1016/j.compag.2018.02.016

10.1109/JIOT.2018.2844296

10.1016/j.compag.2018.12.039

10.1016/j.compag.2020.105256

10.1109/JAS.2020.1003536

10.1109/JAS.2021.1003925

10.3390/s20226458

2021, Threats to precision agriculture

Munaiah, 2016, Are intrusion detection studies evaluated consistently? A systematic literature review

10.1145/2808691

Opitz, 2019, Macro F1 and macro F1, arXiv preprint

Narasimhan, Consistent multiclass algorithms for complex performance measures, Proc. 32nd Int. Conf. Machine Learning, 2398

10.1016/j.cose.2020.101732

10.1016/j.jnca.2019.102507

10.1016/j.jisa.2020.102532

10.1016/j.cose.2019.101646

10.1016/j.future.2018.05.008

10.1109/TII.2019.2938778

10.1016/j.jpdc.2019.12.008

10.1186/s13677-020-0154-7

10.1016/j.jnca.2020.102688

10.1016/j.eswa.2019.113105

10.1007/s00521-019-04152-6

10.1016/j.cose.2020.101906

Moustafa, New generations of internet of things datasets for cybersecurity applications based machine learning: TON_IoT datasets, Proc. eResearch Australasia Conf., 1

2019, KDD cup 1999 data

10.1016/j.jnca.2017.03.018

10.1109/TII.2019.2920831

10.1016/j.comcom.2007.05.002

10.1145/846183.846200

10.1016/j.future.2020.04.013

10.1007/s00521-019-04187-9

10.1016/j.comnet.2020.107168

10.1109/JAS.2017.7510730

10.1007/s11036-019-01437-x

Ahmadon, Detection and update method for attack behavior models in intrusion detection systems, Proc. 3rd Int. Conf. Computing for Sustainable Global Development, 2119

10.1109/GCCE.2015.7398575

10.1016/j.comnet.2020.107364

10.1109/TETC.2018.2879714

10.1016/j.future.2019.07.045

10.1007/s11235-020-00747-w

10.3390/s19143119

10.1016/j.comnet.2020.107247

10.1016/j.knosys.2020.105648

10.1007/s00521-019-04477-2

Chellammal, 2020, Real-time anomaly detection using parallelized intrusion detection architecture for streaming data, Concurr. Comput. Pract. Exper., 32, e5013, 10.1002/cpe.5013

10.1016/j.comnet.2020.107183

10.1016/j.ins.2019.09.055

10.1109/ACCESS.2020.2977007

10.1109/ACCESS.2020.2973730

10.1109/JSAC.2020.2986618

10.3390/computers8030058

10.3390/electronics7090162

10.1007/978-3-030-24202-2_10

10.1109/MWC.2018.1800155

10.1016/j.adhoc.2020.102177

10.1016/j.cose.2019.101645

10.1016/j.comnet.2019.107044

10.1007/s00521-019-04103-1

10.1016/j.comnet.2019.107042

10.1016/j.jisa.2020.102530

10.1109/TIFS.2019.2922262

10.1007/s00138-020-01089-y

10.1016/j.jnca.2020.102631

10.1109/ACCESS.2020.2973178

10.1109/TITS.2020.2970610

10.1016/j.vehcom.2019.100198

10.1109/TITS.2019.2906038

10.1016/j.neucom.2019.11.016

10.1016/j.future.2019.10.022

10.1109/JSYST.2019.2945555

10.1007/s12652-020-01850-1

10.1016/j.comnet.2020.107315

10.1186/s13638-019-1591-1

10.1016/j.comnet.2020.107174

10.1016/j.cose.2020.101851

10.3390/fi12030044

2021, How to choose an activation function for deep learning

10.1016/j.simpat.2019.102031

10.1016/j.ins.2018.02.060

10.1007/s11227-018-2397-3

10.1109/TSUSC.2018.2793284

10.1016/j.ins.2019.09.024

10.1109/JIOT.2020.3002255

10.1007/s12652-019-01387-y

10.1016/j.ins.2019.10.069

10.1109/JIOT.2019.2948149

10.1016/j.eswa.2019.112963

10.1016/j.techfore.2020.120532

10.1109/TII.2019.2946791

10.1007/s12652-018-0998-6

10.1109/TII.2019.2956474

10.1016/j.eswa.2020.113578

10.1016/j.future.2018.07.027

10.1016/j.cose.2020.101926

10.1109/TSG.2018.2878570

10.1016/j.cose.2016.07.002

10.1007/s00521-019-04453-w

10.3390/s21041113

10.1109/ETFA.2016.7733515

10.1016/j.knosys.2015.01.009

10.1109/CBD.2014.41

10.1109/CUBE.2013.31

10.1016/j.eswa.2011.07.032

10.1016/j.compind.2020.103187

10.1109/MilCIS.2015.7348942

10.1109/CISDA.2009.5356528

10.1016/j.cose.2011.12.012

2020, Isot cloud intrusion dataset

2020, HTTP DATASET CSIC 2010

2020, Kyoto 2006 dataset

10.5220/0006639801080116

2020, Industrial control system (ICS) cyber attack datasets

2020, AWID dataset

10.1016/j.future.2019.05.041

2021, TON_IOT datasets

10.1109/ACCESS.2020.3022633

10.1109/ACCESS.2020.3022862

10.1145/3416013.3426457

10.1109/DISCEX.2000.821515

10.1109/DISCEX.2000.821506

10.1109/TC.2016.2519914

10.1016/j.iot.2020.100306

10.1155/2019/7130868

10.1109/ACCESS.2019.2925838

10.1186/s13677-018-0123-6

2021, Tensorflow

2021, The microsoft cognitive toolkit

2021, Apache SINGA

2021, Caffe

2021, Eclipse deeplearning4j

2021, PyTorch

2021, Keras

2021, Apache MXNet

2021, Theano

10.1007/s11042-019-7367-0

10.1109/JAS.2020.1003039

10.1109/TCSS.2020.2990103

10.1109/MSP.2017.2743240

10.1109/JAS.2020.1003072

10.1109/JIOT.2020.2969326

Cheng, 2019, The scalability for parallel machine learning training algorithm: Dataset matters, arXiv preprint

Muñoz-González, 2019, Poisoning attacks with generative adversarial nets, arXiv preprint

10.1109/NCA51143.2020.9306691