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Electronics, v.10 no.11, 2021년, pp.1257 -
Ferrag, Mohamed Amine (Department of Computer Science, Guelma University, Guelma 24000, Algeria) , Shu, Lei (College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China) , Djallel, Hamouda (Department of Computer Science, Guelma University, Guelma 24000, Algeria) , Choo, Kim-Kwang Raymond (Department of Information Systems and Cyber Security, University of Texas at San Antonio, San Antonio, TX 78249, USA)
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Smart Agriculture or Agricultural Internet of things, consists of integrating advanced technologies (e.g., NFV, SDN, 5G/6G, Blockchain, IoT, Fog, Edge, and AI) into existing farm operations to improve the quality and productivity of agricultural products. The convergence of Industry 4.0 and Intellig...
Chen Smart factory of industry 4.0: Key technologies, application case, and challenges IEEE Access 2017 10.1109/ACCESS.2017.2783682 6 6505
Friha Internet of Things for the Future of Smart Agriculture: A Comprehensive Survey of Emerging Technologies IEEE/CAA J. Autom. Sin. 2021 10.1109/JAS.2021.1003925 8 718
Liu From Industry 4.0 to Agriculture 4.0: Current Status, Enabling Technologies, and Research Challenges IEEE Trans. Ind. Inform. 2020 10.1109/TII.2020.3003910 17 4322
Ferrag Security and Privacy for Green IoT-Based Agriculture: Review, Blockchain Solutions, and Challenges IEEE Access 2020 10.1109/ACCESS.2020.2973178 8 32031
Yang A Survey on Smart Agriculture: Development Modes, Technologies, and Security and Privacy Challenges IEEE/CAA J. Autom. Sin. 2021 10.1109/JAS.2020.1003536 8 273
Buczak A survey of data mining and machine learning methods for cyber security intrusion detection IEEE Commun. Surv. Tutor. 2015 10.1109/COMST.2015.2494502 18 1153
10.3390/electronics10040372 Chen, J.W., Lin, W.J., Cheng, H.J., Hung, C.L., Lin, C.Y., and Chen, S.P. (2021). A smartphone-based application for scale pest detection using multiple-object detection methods. Electronics, 10.
Liao Intrusion detection system: A comprehensive review J. Netw. Comput. Appl. 2013 10.1016/j.jnca.2012.09.004 36 16
Muna Developing a Security Testbed for Industrial Internet of Things IEEE Internet Things J. 2020 8 5558
Kasongo A deep learning method with wrapper based feature extraction for wireless intrusion detection system Comput. Secur. 2020 10.1016/j.cose.2020.101752 92 101752
Hassan A hybrid deep learning model for efficient intrusion detection in big data environment Inf. Sci. 2020 10.1016/j.ins.2019.10.069 513 386
Li DeepFed: Federated Deep Learning for Intrusion Detection in Industrial Cyber-Physical Systems IEEE Trans. Ind. Inform. 2020 10.1109/TII.2020.3023430 17 5615
Gao Omni SCADA intrusion detection using deep learning algorithms IEEE Internet Things J. 2020 10.1109/JIOT.2020.3009180 8 951
Ferrag DeepCoin: A novel deep learning and blockchain-based energy exchange framework for smart grids IEEE Trans. Eng. Manag. 2019 10.1109/TEM.2019.2922936 67 1285
Nie Data-Driven Intrusion Detection for Intelligent Internet of Vehicles: A Deep Convolutional Neural Network-based Method IEEE Trans. Netw. Sci. Eng. 2020 10.1109/TNSE.2020.2990984 7 2219
Abusitta A deep learning approach for proactive multi-cloud cooperative intrusion detection system Future Gener. Comput. Syst. 2019 10.1016/j.future.2019.03.043 98 308
Kamilaris Deep learning in agriculture: A survey Comput. Electron. Agric. 2018 10.1016/j.compag.2018.02.016 147 70
(2021, May 01). Top 8 Challenges for Machine Learning Practitioners. Available online: https://towardsdatascience.com/top-8-challenges-for-machine-learning-practitioners-c4c0130701a1.
10.14236/ewic/icscsr19.16 Ferrag, M.A., Maglaras, L., Janicke, H., and Smith, R. (2019, January 10-12). Deep learning techniques for cyber security intrusion detection: A detailed analysis. Proceedings of the 6th International Symposium for ICS & SCADA Cyber Security Research 2019, Athens, Greece.
Diro Distributed attack detection scheme using deep learning approach for Internet of Things Future Gener. Comput. Syst. 2018 10.1016/j.future.2017.08.043 82 761
Muna Identification of malicious activities in industrial internet of things based on deep learning models J. Inf. Secur. Appl. 2018 41 1
HaddadPajouh A deep recurrent neural network based approach for internet of things malware threat hunting Future Gener. Comput. Syst. 2018 10.1016/j.future.2018.03.007 85 88
Vinayakumar A visualized botnet detection system based deep learning for the Internet of Things networks of smart cities IEEE Trans. Ind. Appl. 2020 10.1109/TIA.2020.2971952 56 4436
Parra Detecting Internet of Things attacks using distributed deep learning J. Netw. Comput. Appl. 2020 10.1016/j.jnca.2020.102662 163 102662
Latif A Novel Attack Detection Scheme for the Industrial Internet of Things Using a Lightweight Random Neural Network IEEE Access 2020 10.1109/ACCESS.2020.2994079 8 89337
Manimurugan Effective Attack Detection in Internet of Medical Things Smart Environment Using a Deep Belief Neural Network IEEE Access 2020 10.1109/ACCESS.2020.2986013 8 77396
Koroniotis A new network forensic framework based on deep learning for Internet of Things networks: A particle deep framework Future Gener. Comput. Syst. 2020 10.1016/j.future.2020.03.042 110 91
Zhou Variational LSTM enhanced anomaly detection for industrial big data IEEE Trans. Ind. Inform. 2020 10.1109/TII.2020.3022432 17 3469
Selvakumar Anomaly detection framework for Internet of things traffic using vector convolutional deep learning approach in fog environment Future Gener. Comput. Syst. 2020 10.1016/j.future.2020.07.020 113 255
10.1109/WCNC45663.2020.9120761 Khoa, T.V., Saputra, Y.M., Hoang, D.T., Trung, N.L., Nguyen, D., Ha, N.V., and Dutkiewicz, E. (2020, January 25-28). Collaborative learning model for cyberattack detection systems in iot industry 4.0. Proceedings of the 2020 IEEE Wireless Communications and Networking Conference (WCNC), Seoul, Korea.
Popoola Hybrid Deep Learning for Botnet Attack Detection in the Internet of Things Networks IEEE Internet Things J. 2021 10.1109/JIOT.2020.3034156 8 4944
10.1109/TNSE.2020.3032415 Al-Hawawreh, M., Moustafa, N., Garg, S., and Hossain, M.S. (2020). Deep Learning-enabled Threat Intelligence Scheme in the Internet of Things Networks. IEEE Trans. Netw. Sci. Eng.
Ge Towards a deep learning-driven intrusion detection approach for Internet of Things Comput. Netw. 2021 10.1016/j.comnet.2020.107784 186 107784
10.1109/CCST.2019.8888419 Sharafaldin, I., Lashkari, A.H., Hakak, S., and Ghorbani, A.A. (2019, January 1-3). Developing realistic distributed denial of service (DDoS) attack dataset and taxonomy. Proceedings of the 2019 International Carnahan Conference on Security Technology (ICCST), Chennai, India.
(2021, April 04). TON_IOT DATASETS. Available online: https://ieee-dataport.org/documents/toniot-datasets.
10.1016/B978-0-12-816176-0.00026-0 DiPietro, R., and Hager, G.D. (2020). Deep learning: RNNs and LSTM. Handbook of Medical Image Computing and Computer Assisted Intervention, Elsevier.
LeCun Deep learning Nature 2015 10.1038/nature14539 521 436
Alsaedi TON_IoT telemetry dataset: A new generation dataset of IoT and IIoT for data-driven Intrusion Detection Systems IEEE Access 2020 10.1109/ACCESS.2020.3022862 8 165130
Jia Flowguard: An intelligent edge defense mechanism against IoT DDoS attacks IEEE Internet Things J. 2020 10.1109/JIOT.2020.2993782 7 9552
Li Rtvd: A real-time volumetric detection scheme for ddos in the internet of things IEEE Access 2020 10.1109/ACCESS.2020.2974293 8 36191
Carvalho Near real-time security system applied to SDN environments in IoT networks using convolutional neural network Comput. Electr. Eng. 2020 10.1016/j.compeleceng.2020.106738 86 106738
Alamri Bandwidth Control Mechanism and Extreme Gradient Boosting Algorithm for Protecting Software-Defined Networks Against DDoS Attacks IEEE Access 2020 10.1109/ACCESS.2020.3033942 8 194269
10.1109/ICESS49830.2020.9301591 Zhang, Y., Xu, J., Wang, Z., Geng, R., Choo, K.K.R., Pérez-Díaz, J.A., and Zhu, D. (2020, January 10-11). Efficient and Intelligent Attack Detection in Software Defined IoT Networks. Proceedings of the 2020 IEEE International Conference on Embedded Software and Systems (ICESS), Shanghai, China.
Kumar TP2SF: A Trustworthy Privacy-Preserving Secured Framework for sustainable smart cities by leveraging blockchain and machine learning J. Syst. Archit. 2021 10.1016/j.sysarc.2020.101954 115 101954
10.1109/TNSM.2021.3075503 Pontes, C., Souza, M., Gondim, J., Bishop, M., and Marotta, M. (2021). A new method for flow-based network intrusion detection using the inverse Potts model. IEEE Trans. Netw. Serv. Manag.
Assis A GRU deep learning system against attacks in software defined networks J. Netw. Comput. Appl. 2021 10.1016/j.jnca.2020.102942 177 102942
Kumar An ensemble learning and fog-cloud architecture-driven cyber-attack detection framework for IoMT networks Comput. Commun. 2021 10.1016/j.comcom.2020.12.003 166 110
10.3390/electronics10080918 Javeed, D., Gao, T., and Khan, M.T. (2021). SDN-Enabled Hybrid DL-Driven Framework for the Detection of Emerging Cyber Threats in IoT. Electronics, 10.
10.1109/TCSS.2021.3063538 Nie, L., Wu, Y., Wang, X., Guo, L., Wang, G., Gao, X., and Li, S. (2021). Intrusion Detection for Secure Social Internet of Things Based on Collaborative Edge Computing: A Generative Adversarial Network-Based Approach. IEEE Trans. Comput. Soc. Syst.
Kumar Sp2f: A secured privacy-preserving framework for smart agricultural unmanned aerial vehicles Comput. Netw. 2021 10.1016/j.comnet.2021.107819 187 107819
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