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Volume 9 Issue 3
Mar.  2022

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2022  >  9(3): 407-436
M. A. Ferrag, L. Shu, O. Friha, and X. 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: M. A. Ferrag, L. Shu, O. Friha, and X. 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
shu

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

doi: 10.1109/JAS.2021.1004344
  • 1.

    Department of Computer Science, Guelma University, B.P. 401, 24000, Algeria

  • 2.

    College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China

  • 3.

    School of Engineering, University of Lincoln, Lincoln LN67TS, UK

  • 4.

    Networks and Systems Laboratory (LRS), University of Badji Mokhtar-Annaba, B.P.12, Annaba 23000, Algeria

  • 5.

    College of Engineering, Nanjing Agricultural University, Nanjing 210031, China

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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    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.

     

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  • [1]
    Y. Liu, X. Y. Ma, L. Shu, G. P. Hancke, and A. M. Abu-Mahfouz, “From industry 4.0 to agriculture 4.0: Current status, enabling technologies, and research challenges,” IEEE Trans. Ind. Inf., vol. 17, no. 6, pp. 4322–4334, Jun. 2021. doi: 10.1109/TII.2020.3003910
    [2]
    G. Aceto, V. Persico, and A. Pescapé, “A survey on information and communication technologies for industry 4.0: State-of-the-art, taxonomies, perspectives, and challenges,” IEEE Commun. Surv. Tutor., vol. 21, no. 4, pp. 3467–3501, Aug. 2019. doi: 10.1109/COMST.2019.2938259
    [3]
    Industry 4.0 and cybersecurity: Managing risk in an age of connected production [Online]. Available: https://www2.deloitte.com/content/dam/insights/us/articles/3749_Industry4-0_cybersecurity/DUP_Industry4-0_cybersecurity.pdf, Accessed on: May 30, 2020.
    [4]
    K. Huang, L. Shu, K. L. Li, F. Yang, G. J. Han, X. C. Wang, and S. Pearson, “Photovoltaic agricultural internet of things towards realizing the next generation of smart farming,” IEEE Access, vol. 8, pp. 76300–76312, Apr. 2020. doi: 10.1109/ACCESS.2020.2988663
    [5]
    O. Friha, M. A. Ferrag, L. Shu, and M. Nafa, “A robust security framework based on blockchain and SDN for fog computing enabled agricultural internet of things,” in Proc. Int. Conf. Internet Things and Intelligent Applications, Zhenjiang, China, 2020, pp. 1−5.
    [6]
    A. Tewari and B. Gupta, “Security, privacy and trust of different layers in internet-of-things (IoTs) framework,” Future Gener. Comput. Syst., vol. 108, pp. 909–920, Jul. 2020. doi: 10.1016/j.future.2018.04.027
    [7]
    W. J. Zhu, M. L. Deng, and Q. L. Zhou, “An intrusion detection algorithm for wireless networks based on ASDL,” IEEE/CAA J. Autom. Sinica, vol. 5, no. 1, pp. 92–107, Jan. 2018. doi: 10.1109/JAS.2017.7510754
    [8]
    M. Agarwal, S. Purwar, S. Biswas, and S. Nandi, “Intrusion detection system for PS-poll DoS attack in 802.11 networks using real time discrete event system,” IEEE/CAA J. Autom. Sinica, vol. 4, no. 4, pp. 792–808, 2017. doi: 10.1109/JAS.2016.7510178
    [9]
    M. A. Ferrag, L. Maglaras, S. Moschoyiannis, and H. Janicke, “Deep learning for cyber security intrusion detection: Approaches, datasets, and comparative study,” J. Inf. Secur. Appl., vol. 50, p. 102419, Feb. 2020.
    [10]
    A. L. Buczak and E. Guven, “A survey of data mining and machine learning methods for cyber security intrusion detection,” IEEE Commun. Surv. Tutor., vol. 18, no. 2, pp. 1153–1176, 2016. doi: 10.1109/COMST.2015.2494502
    [11]
    D. Kwon, H. Kim, J. Kim, S. C. Suh, I. Kim, and K. J. Kim, “A survey of deep learning-based network anomaly detection,” Cluster Comput., vol. 22, no. 1, pp. 949–961, Jan. 2019.
    [12]
    M. A. Al-Garadi, A. Mohamed, A. K. Al-Ali, X. J. Du, I. Ali, and M. Guizani, “A survey of machine and deep learning methods for internet of things (IoT) security,” IEEE Commun. Surv. Tutor., vol. 22, no. 3, pp. 1646–1685, Apr. 2020. doi: 10.1109/COMST.2020.2988293
    [13]
    P. Mishra, V. Varadharajan, U. Tupakula, and E. S. Pilli, “A detailed investigation and analysis of using machine learning techniques for intrusion detection,” IEEE Commun. Surv. Tutor., vol. 21, no. 1, pp. 686–728, Feb. 2019. doi: 10.1109/COMST.2018.2847722
    [14]
    K. A. P. da Costa, J. P. Papa, C. O. Lisboa, R. Munoz, and V. H. C. de Albuquerque, “Internet of things: A survey on machine learning-based intrusion detection approaches,” Comput. Netw., vol. 151, pp. 147–157, Mar. 2019. doi: 10.1016/j.comnet.2019.01.023
    [15]
    N. Chaabouni, M. Mosbah, A. Zemmari, C. Sauvignac, and P. Faruki, “Network intrusion detection for IoT security based on learning techniques,” IEEE Commun. Surv. Tutor., vol. 21, no. 3, pp. 2671–2701, Jan. 2019. doi: 10.1109/COMST.2019.2896380
    [16]
    H. Y. Liu and B. Lang, “Machine learning and deep learning methods for intrusion detection systems: A survey,” Appl. Sci., vol. 9, no. 20, p. 4396, Oct. 2019.
    [17]
    N. Sultana, N. Chilamkurti, W. Peng, and R. Alhadad, “Survey on SDN based network intrusion detection system using machine learning approaches,” Peer-to-Peer Netw. Appl., vol. 12, no. 2, pp. 493–501, Mar. 2019. doi: 10.1007/s12083-017-0630-0
    [18]
    A. Ahmad, E. Harjula, M. Ylianttila, and I. Ahmad, “Evaluation of machine learning techniques for security in SDN,” in Proc. IEEE Globecom Workshops, Taiwan, China, 2020, pp. 1−6.
    [19]
    Z. Ahmad, A. S. Khan, C. W. Shiang, J. Abdullah, and F. Ahmad, “Network intrusion detection system: A systematic study of machine learning and deep learning approaches,” Trans. Emerg. Telecommun. Technol., vol. 32, no. 1, p. e4150, Jan. 2021.
    [20]
    M. Mohammadi, T. A. Rashid, S. H. T. Karim, A. H. M. Aldalwie, Q. T. Tho, M. Bidaki, A. M. Rahmani, and M. Hosseinzadeh, “A comprehensive survey and taxonomy of the SVM-based intrusion detection systems,” J. Netw. Comput. Appl., vol. 178, p. 102983, Mar. 2021.
    [21]
    M. A. Ferrag, M. Babaghayou, and M. A. Yazici, “Cyber security for fog-based smart grid SCADA systems: Solutions and challenges,” J. Inf. Secur. Appl., vol. 52, p. 102500, Jun. 2020.
    [22]
    P. P. Ray, “Internet of things for smart agriculture: Technologies, practices and future direction,” J. Amb. Intel. Smart Environ., vol. 9, no. 4, pp. 395–420, Jun. 2017.
    [23]
    A. Kamilaris and F. X. Prenafeta-Boldú, “Deep learning in agriculture: A survey,” Comput. Electron. Agric., vol. 147, pp. 70–90, Apr. 2018. doi: 10.1016/j.compag.2018.02.016
    [24]
    O. Elijah, T. A. Rahman, I. Orikumhi, C. Y. Leow, and M. H. D. N. Hindia, “An overview of internet of things (IoT) and data analytics in agriculture: Benefits and challenges,” IEEE Internet Things J., vol. 5, no. 5, pp. 3758–3773, Oct. 2018. doi: 10.1109/JIOT.2018.2844296
    [25]
    A. Khanna and S. Kaur, “Evolution of internet of things (IoT) and its significant impact in the field of precision agriculture,” Comput. Electron. Agric., vol. 157, pp. 218–231, Feb. 2019. doi: 10.1016/j.compag.2018.12.039
    [26]
    Z. Zhai, J. F. Martínez, V. Beltran, and N. L. Martínez, “Decision support systems for agriculture 4.0: Survey and challenges,” Comput. Electron. Agric., vol. 170, p. 105256, Mar. 2020.
    [27]
    X. Yang, L. Shu, J. N. Chen, M. A. Ferrag, J. Wu, E. Nurellari, and K. Huang, “A survey on smart agriculture: Development modes, technologies, and security and privacy challenges,” IEEE/CAA J. Autom. Sinica, vol. 8, no. 2, pp. 273–302, Feb. 2021. doi: 10.1109/JAS.2020.1003536
    [28]
    O. Friha, M. A. Ferrag, L. Shu, L. Maglaras, and X. C. Wang, “Internet of things for the future of smart agriculture: A comprehensive survey of emerging technologies,” IEEE/CAA J. Autom. Sinica, vol. 8, no. 4, pp. 718–752, Apr. 2021. doi: 10.1109/JAS.2021.1003925
    [29]
    K. Demestichas, N. Peppes, and T. Alexakis, “Survey on security threats in agricultural IoT and smart farming,” Sensors, vol. 20, no. 22, p. 6458, Nov. 2020.
    [30]
    Threats to precision agriculture [Online]. Available: https://www.dhs.gov/sites/default/files/publications/2018%20AEP_Threats_to_Precision_Agriculture.pdf, Accessed on: Mar. 3, 2021.
    [31]
    N. Munaiah, A. Meneely, R. Wilson, and B. Short, “Are intrusion detection studies evaluated consistently? A systematic literature review,” 2016.
    [32]
    A. Milenkoski, M. Vieira, S. Kounev, A. Avritzer, and B. D. Payne, “Evaluating computer intrusion detection systems: A survey of common practices,” ACM Comput. Surv., vol. 48, no. 1, pp. 1–41, Sept. 2015.
    [33]
    J. Opitz and S. Burst, “Macro F1 and macro F1,” arXiv preprint arXiv: 1911.03347, Nov. 2019.
    [34]
    H. Narasimhan, H. G. Ramaswamy, A. Saha, and S. Agarwal, “Consistent multiclass algorithms for complex performance measures,” in Proc. 32nd Int. Conf. Machine Learning, Lille, France, 2015, pp. 2398−2407.
    [35]
    K. S. Gill, S. Saxena, and A. Sharma, “GTM-CSec: Game theoretic model for cloud security based on ids and honeypot,” Comput. Secur., vol. 92, p. 101732, May 2020.
    [36]
    M. Rabbani, Y. L. Wang, R. Khoshkangini, H. Jelodar, R. X. Zhao, and P. Hu, “A hybrid machine learning approach for malicious behaviour detection and recognition in cloud computing,” J. Netw. Comput. Appl., vol. 151, p. 102507, Feb. 2020.
    [37]
    G. S. Kushwah and V. Ranga, “Voting extreme learning machine based distributed denial of service attack detection in cloud computing,” J. Inf. Secur. Appl., vol. 53, p. 102532, Aug. 2020.
    [38]
    A. Aldribi, I. Traoré, B. Moa, and O. Nwamuo, “Hypervisor-based cloud intrusion detection through online multivariate statistical change tracking,” Comput. Secur., vol. 88, p. 101646, Jan. 2020.
    [39]
    P. Y. Zhang, M. C. Zhou, and G. Fortino, “Security and trust issues in fog computing: A survey,” Future Gener. Comput. Syst., vol. 88, pp. 16–27, Nov. 2018. doi: 10.1016/j.future.2018.05.008
    [40]
    Z. H. Tian, C. C. Luo, J. Qiu, X. J. Du, and M. Guizani, “A distributed deep learning system for web attack detection on edge devices,” IEEE Trans. Ind. Inf., vol. 16, no. 3, pp. 1963–1971, Mar. 2020. doi: 10.1109/TII.2019.2938778
    [41]
    A. S. Almogren, “Intrusion detection in edge-of-things computing,” J. Paral. Distrib. Comput., vol. 137, pp. 259–265, Mar. 2020. doi: 10.1016/j.jpdc.2019.12.008
    [42]
    Z. P. Jiang, K. Zhao, R. Li, J. Z. Zhao, and J. Z. Du, “PHYAlert: Identity spoofing attack detection and prevention for a wireless edge network,” J. Cloud Comput., vol. 9, no. 1, pp. 1–13, Jan. 2020. doi: 10.1186/s13677-020-0154-7
    [43]
    Z. D. Wu, J. J. Wang, L. Q. Hu, Z. Zhang, and H. Wu, “A network intrusion detection method based on semantic Re-encoding and deep learning,” J. Netw. Comput. Appl., vol. 164, p. 102688, Aug. 2020.
    [44]
    M. Ahsan, M. Mashuri, M. H. Lee, H. Kuswanto, and D. D. Prastyo, “Robust adaptive multivariate hotelling’s T.2 control chart based on kernel density estimation for intrusion detection system,” Expert Syst. Appl., vol. 145, p. 113105, May 2020.
    [45]
    A. S. Qureshi, A. Khan, N. Shamim, and M. H. Durad, “Intrusion detection using deep sparse auto-encoder and self-taught learning,” Neural Comput. Appl., vol. 32, no. 9, pp. 3135–3147, Apr. 2020.
    [46]
    W. Haider, N. Moustafa, M. Keshk, A. Fernandez, K. K. R. Choo, and A. Wahab, “FGMC-HADS: Fuzzy Gaussian mixture-based correntropy models for detecting zero-day attacks from Linux systems,” Comput. Secur., vol. 96, p. 101906, Sep. 2020.
    [47]
    N. Moustafa, “New generations of internet of things datasets for cybersecurity applications based machine learning: TON_IoT datasets,” in Proc. eResearch Australasia Conf., Brisbane, Australia, 2019, pp. 1−2.
    [48]
    KDD cup 1999 data [Online]. http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html, Accessed on: May 30, 2019.
    [49]
    W. Haider, J. Hu, J. Slay, B. P. Turnbull, and Y. Xie, “Generating realistic intrusion detection system dataset based on fuzzy qualitative modeling,” J. Netw. Comput. Appl., vol. 87, pp. 185–192, Jun. 2017. doi: 10.1016/j.jnca.2017.03.018
    [50]
    B. Naik, M. S. Obaidat, J. Nayak, D. Pelusi, P. Vijayakumar, and S. H. Islam, “Intelligent secure ecosystem based on metaheuristic and functional link neural network for edge of things,” IEEE Trans. Ind. Inf., vol. 16, no. 3, pp. 1947–1956, Mar. 2020. doi: 10.1109/TII.2019.2920831
    [51]
    A. N. Toosi and M. Kahani, “A new approach to intrusion detection based on an evolutionary soft computing model using neuro-fuzzy classifiers,” Comput. Commun., vol. 30, no. 10, pp. 2201–2212, Jul. 2007. doi: 10.1016/j.comcom.2007.05.002
    [52]
    B. Pfahringer, “Winning the KDD99 classification cup: Bagged boosting,” ACM SIGKDD Explor. Newsl., vol. 1, no. 2, pp. 65–66, Jan. 2000. doi: 10.1145/846183.846200
    [53]
    M. Pawlicki, M. Choraś, and R. Kozik, “Defending network intrusion detection systems against adversarial evasion attacks,” Future Gener. Comput. Syst., vol. 110, pp. 148–154, Sept. 2020. doi: 10.1016/j.future.2020.04.013
    [54]
    S. Kaur and M. Singh, “Hybrid intrusion detection and signature generation using deep recurrent neural networks,” Neural Comput. Appl., vol. 32, no. 12, pp. 7859–7877, Jun. 2020. doi: 10.1007/s00521-019-04187-9
    [55]
    S. Hosseini and B. M. H. Zade, “New hybrid method for attack detection using combination of evolutionary algorithms, SVM, and ANN,” Comput. Netw., vol. 173, p. 107168, May 2020.
    [56]
    S. H. Teng, N. Q. Wu, H. B. Zhu, L. Y. Teng, and W. Zhang, “SVM-DT-based adaptive and collaborative intrusion detection,” IEEE/CAA J. Autom. Sinica, vol. 5, no. 1, pp. 108–118, Jan. 2018. doi: 10.1109/JAS.2017.7510730
    [57]
    D. M. Ngo, C. Pham-Quoc, and T. N. Thinh, “Heterogeneous hardware-based network intrusion detection system with multiple approaches for SDN,” Mobile Netw. Appl., vol. 25, no. 3, pp. 1178–1192, Jun. 2020. doi: 10.1007/s11036-019-01437-x
    [58]
    M. A. B. Ahmadon, S. Yamaguchi, Z. L. Gou, and B. B. Gupta, “Detection and update method for attack behavior models in intrusion detection systems,” in Proc. 3rd Int. Conf. Computing for Sustainable Global Development, New Delhi, India, 2016, pp. 2119−2124.
    [59]
    Z. L. Gou, M. A. B. Ahmadon, S. Yamaguchi, and B. B. Gupta, “A petri net-based framework of intrusion detection systems,” in Proc. IEEE 4th Global Conf. Consumer Electronics, Osaka, Japan, 2015, pp. 579−583.
    [60]
    I. H. Abdulqadder, S. J. Zhou, D. Q. Zou, I. T. Aziz, and S. M. A. Akber, “Multi-layered intrusion detection and prevention in the SDN/NFV enabled cloud of 5G networks using AI-based defense mechanisms,” Comput. Netw. , vol. 179, p. 107364, Oct. 2020.
    [61]
    I. H. Abdulqadder, D. Q. Zou, I. T. Aziz, B. Yuan, and W. Q. Dai, “Deployment of robust security scheme in SDN based 5G network over NFV enabled cloud environment,” IEEE Trans. Emerg. Topics Comput., vol. 9, no. 2, pp. 866–877, Apr.–Jun. 2021. doi: 10.1109/TETC.2018.2879714
    [62]
    W. Zong, Y. W. Chow, and W. Susilo, “Interactive three-dimensional visualization of network intrusion detection data for machine learning,” Future Gener. Comput. Syst., vol. 102, pp. 292–306, Jan. 2020. doi: 10.1016/j.future.2019.07.045
    [63]
    A. Mishra, N. Gupta, and B. B. Gupta, “Defense mechanisms against DDoS attack based on entropy in SDN-cloud using POX controller,” Telecommun. Syst., vol. 77, no. 1, pp. 47–62, Jan. 2021. doi: 10.1007/s11235-020-00747-w
    [64]
    A. Derhab, M. Guerroumi, A. Gumaei, L. Maglaras, M. A. Ferrag, M. Mukherjee, and F. A. Khan, “Blockchain and random subspace learning-based IDS for SDN-enabled industrial IoT security,” Sensors, vol. 19, no. 14, p. 3119, Jul. 2019.
    [65]
    Y. Y. Zhou, G. Cheng, S. Q. Jiang, and M. Dai, “Building an efficient intrusion detection system based on feature selection and ensemble classifier,” Comput. Netw., vol. 174, p. 107247, Jun. 2020.
    [66]
    L. Lv, W. H. Wang, Z. Y. Zhang, and X. G. Liu, “A novel intrusion detection system based on an optimal hybrid kernel extreme learning machine,” Knowl.-Based Syst., vol. 195, p. 105648, May 2020.
    [67]
    S. Velliangiri and P. Karthikeyan, “Hybrid optimization scheme for intrusion detection using considerable feature selection,” Neural Comput. Appl., vol. 32, no. 12, pp. 7925–7939, Jun. 2019.
    [68]
    P. Chellammal and K. M. P. D. Sheba, “Real-time anomaly detection using parallelized intrusion detection architecture for streaming data,” Concurr. Comput. Pract. Exper., vol. 32, no. 4, p. e5013, Feb. 2020.
    [69]
    C. Khammassi and S. Krichen, “A NSGA2-LR wrapper approach for feature selection in network intrusion detection,” Comput. Netw., vol. 172, p. 107183, May 2020.
    [70]
    S. J. Bu and S. B. Cho, “A convolutional neural-based learning classifier system for detecting database intrusion via insider attack,” Inf. Sci., vol. 512, pp. 123–136, Feb. 2020. doi: 10.1016/j.ins.2019.09.055
    [71]
    Y. Q. Yang, K. F. Zheng, B. Wu, Y. X. Yang, and X. J. Wang, “Network intrusion detection based on supervised adversarial variational auto-encoder with regularization,” IEEE Access, vol. 8, pp. 42169–42184, Feb. 2020. doi: 10.1109/ACCESS.2020.2977007
    [72]
    K. Y. Jiang, W. Y. Wang, A. L. Wang, and H. B. Wu, “Network intrusion detection combined hybrid sampling with deep hierarchical network,” IEEE Access, vol. 8, pp. 32464–32476, Feb. 2020. doi: 10.1109/ACCESS.2020.2973730
    [73]
    N. Guizani and A. Ghafoor, “A network function virtualization system for detecting malware in large IoT based networks,” IEEE J. Select. Areas Commun., vol. 38, no. 6, pp. 1218–1228, Jun. 2020. doi: 10.1109/JSAC.2020.2986618
    [74]
    M. A. Ferrag and L. Maglaras, “DeliveryCoin: An IDS and blockchain-based delivery framework for drone-delivered services,” Computers, vol. 8, no. 3, p. 58, Aug. 2019.
    [75]
    C. Ju and H. I. Son, “Multiple UAV systems for agricultural applications: Control, implementation, and evaluation,” Electronics, vol. 7, no. 9, p. 162, Aug. 2018.
    [76]
    D. Albani, T. Manoni, A. Arik, D. Nardi, and V. Trianni, “Field coverage for weed mapping: Toward experiments with a UAV swarm,” in Proc. 11th EAI Int. Conf. Bio-inspired Information and Communication, Pittsburgh, USA, 2019, pp. 132−146.
    [77]
    U. Challita, A. Ferdowsi, M. Z. Chen, and W. Saad, “Machine learning for wireless connectivity and security of cellular-connected UAVs,” IEEE Wirel. Commun., vol. 26, no. 1, pp. 28–35, Feb. 2019. doi: 10.1109/MWC.2018.1800155
    [78]
    S. K. Huang and K. Lei, “IGAN-IDS: An imbalanced generative adversarial network towards intrusion detection system in ad-hoc networks,” Ad Hoc Netw., vol. 105, p. 102177, Aug. 2020.
    [79]
    M. Wang, Y. Q. Lu, and J. C. Qin, “A dynamic MLP-based DDoS attack detection method using feature selection and feedback,” Comput. Secur., vol. 88, p. 101645, Jan. 2020.
    [80]
    S. Sciancalepore, O. A. Ibrahim, G. Oligeri, and R. Di Pietro, “PiNcH: An effective, efficient, and robust solution to drone detection via network traffic analysis,” Comput. Netw., vol. 168, p. 107044, Feb. 2020.
    [81]
    Q. M. Alzubi, M. Anbar, Z. N. M. Alqattan, M. A. Al-Betar, and R. Abdullah, “Intrusion detection system based on a modified binary grey wolf optimisation,” Neural Comput. Appl., vol. 32, no. 10, pp. 6125–6137, May 2020. doi: 10.1007/s00521-019-04103-1
    [82]
    W. Elmasry, A. Akbulut, and A. H. Zaim, “Evolving deep learning architectures for network intrusion detection using a double PSO metaheuristic,” Comput. Netw., vol. 168, p. 107042, Feb. 2020.
    [83]
    M. Al Qurashi, C. M. Angelopoulos, and V. Katos, “An architecture for resilient intrusion detection in ad-hoc networks,” J. Inf. Secur. Appl., vol. 53, p. 102530, Aug. 2020.
    [84]
    N. V. Abhishek, A. Tandon, T. J. Lim, and B. Sikdar, “A GLRT-based mechanism for detecting relay misbehavior in clustered IoT networks,” IEEE Trans. Inf. Foren. Secur., vol. 15, pp. 435–446, Aug. 2019.
    [85]
    M. Villamizar, A. Martínez-González, O. Canévet, and J. M. Odobez, “WatchNet++: Efficient and accurate depth-based network for detecting people attacks and intrusion,” Mach. Vision Appl., vol. 31, no. 6, p. 41, Jun. 2020.
    [86]
    W. J. Li, W. Z. Meng, and M. H. Au, “Enhancing collaborative intrusion detection via disagreement-based semi-supervised learning in IoT environments,” J. Netw. Comput. Appl., vol. 161, p. 102631, Jul. 2020.
    [87]
    M. A. Ferrag, L. Shu, X. Yang, A. Derhab, and L. Maglaras, “Security and privacy for green IoT-based agriculture: Review, blockchain solutions, and challenges,” IEEE Access, vol. 8, pp. 32031–32053, Feb. 2020. doi: 10.1109/ACCESS.2020.2973178
    [88]
    Y. H. Wang, Z. H. Tian, Y. B. Sun, X. J. Du, and N. Guizani, “Locjury: An IBN-based location privacy preserving scheme for IoCV,” IEEE Trans. Intell. Transp. Syst., vol. 22, no. 8, pp. 5028–5037, Aug. 2021. doi: 10.1109/TITS.2020.2970610
    [89]
    H. M. Song, J. Woo, and H. K. Kim, “In-vehicle network intrusion detection using deep convolutional neural network,” Vehicular Commun., vol. 21, p. 100198, Jan. 2020.
    [90]
    F. van Wyk, Y. Y. Wang, A. Khojandi, and N. Masoud, “Real-time sensor anomaly detection and identification in automated vehicles,” IEEE Trans. Intell. Transp. Syst., vol. 21, no. 3, pp. 1264–1276, Mar. 2020. doi: 10.1109/TITS.2019.2906038
    [91]
    C. Ieracitano, A. Adeel, F. C. Morabito, and A. Hussain, “A novel statistical analysis and autoencoder driven intelligent intrusion detection approach,” Neurocomputing, vol. 387, pp. 51–62, Apr. 2020. doi: 10.1016/j.neucom.2019.11.016
    [92]
    J. M. Vidal, M. A. S. Monge, and S. M. M. Monterrubio, “EsPADA: Enhanced payload analyzer for malware detection robust against adversarial threats,” Future Gener. Comput. Syst., vol. 104, pp. 159–173, Mar. 2020. doi: 10.1016/j.future.2019.10.022
    [93]
    K. Vieira, F. L. Koch, J. B. M. Sobral, C. B. Westphall, and J. L. de Souza Leão, “Autonomic intrusion detection and response using big data,” IEEE Syst. J., vol. 14, no. 2, pp. 1984–1991, Jun. 2020. doi: 10.1109/JSYST.2019.2945555
    [94]
    R. B. Benisha and S. R. Ratna, “Detection of data integrity attacks by constructing an effective intrusion detection system,” J. Ambient Intell. Human. Comput., vol. 11, no. 11, pp. 5233–5244, Mar. 2020. doi: 10.1007/s12652-020-01850-1
    [95]
    H. P. Zhang, L. L. Huang, C. Q. Wu, and Z. B. Li, “An effective convolutional neural network based on SMOTE and Gaussian mixture model for intrusion detection in imbalanced dataset,” Comput. Netw., vol. 177, p. 107315, Aug. 2020.
    [96]
    C. Liu, J. Yang, and J. Q. Wu, “Web intrusion detection system combined with feature analysis and SVM optimization,” EURASIP J. Wirel. Commun. Netw., vol. 2020, no. 1, p. 33, Feb. 2020.
    [97]
    M. Zhou, L. S. Han, H. W. Lu, and C. Fu, “Distributed collaborative intrusion detection system for vehicular Ad Hoc networks based on invariant,” Comput. Netw., vol. 172, p. 107174, May 2020.
    [98]
    X. K. Li, W. Chen, Q. R. Zhang, and L. F. Wu, “Building auto-encoder intrusion detection system based on random forest feature selection,” Comput. Secur., vol. 95, p. 101851, Aug. 2020.
    [99]
    M. A. Ferrag, L. Maglaras, A. Ahmim, M. Derdour, and H. Janicke, “RDTIDS: Rules and decision tree-based intrusion detection system for internet-of-things networks,” Future Internet, vol. 12, no. 3, p. 44, Mar. 2020.
    [100]
    How to choose an activation function for deep learning [Online]. https://wmk-it.net/technology/how-to-choose-an-activation-function-for-deep-learning-1627113168, Accessed on: Mar. 13, 2021.
    [101]
    M. Almiani, A. AbuGhazleh, A. Al-Rahayfeh, S. Atiewi, and A. Razaque, “Deep recurrent neural network for IoT intrusion detection system,” Simul. Modell. Pract. Theory, vol. 101, p. 102031, May 2020.
    [102]
    D. M. Li, L. B. Deng, B. B. Gupta, H. X. Wang, and C. Choi, “A novel CNN based security guaranteed image watermarking generation scenario for smart city applications,” Inf. Sci., vol. 479, pp. 432–447, Apr. 2019. doi: 10.1016/j.ins.2018.02.060
    [103]
    S. A. Aljawarneh and R. Vangipuram, “GARUDA: Gaussian dissimilarity measure for feature representation and anomaly detection in internet of things,” J. Supercomput., vol. 76, no. 6, pp. 4376–4413, Jun. 2020. doi: 10.1007/s11227-018-2397-3
    [104]
    F. Jiang, Y. S. Fu, B. B. Gupta, Y. S. Liang, S. Rho, F. Lou, F. Z. Meng, and Z. H. Tian, “Deep learning based multi-channel intelligent attack detection for data security,” IEEE Trans. Sustain. Comput., vol. 5, no. 2, pp. 204–212, Apr.–Jun. 2020. doi: 10.1109/TSUSC.2018.2793284
    [105]
    W. Wang, Y. Y. Shang, Y. Z. He, Y. D. Li, and J. Q. Liu, “Botmark: Automated botnet detection with hybrid analysis of flow-based and graph-based traffic behaviors,” Inf. Sci., vol. 511, pp. 284–296, Feb. 2020. doi: 10.1016/j.ins.2019.09.024
    [106]
    M. Shafiq, Z. H. Tian, A. K. Bashir, X. J. Du, and M. Guizani, “CorrAUC: A malicious bot-IoT traffic detection method in IoT network using machine-learning techniques,” IEEE Internet Things J., vol. 8, no. 5, pp. 3242–3254, Mar. 2020.
    [107]
    A. Al Shorman, H. Faris, and I. Aljarah, “Unsupervised intelligent system based on one class support vector machine and grey wolf optimization for IoT botnet detection,” J. Ambient Intell. Human. Comput., vol. 11, no. 7, pp. 2809–2825, Jul. 2019.
    [108]
    M. M. Hassan, A. Gumaei, A. Alsanad, M. Alrubaian, and G. Fortino, “A hybrid deep learning model for efficient intrusion detection in big data environment,” Inf. Sci., vol. 513, pp. 386–396, Mar. 2020. doi: 10.1016/j.ins.2019.10.069
    [109]
    S. Murali and A. Jamalipour, “A lightweight intrusion detection for sybil attack under mobile RPL in the internet of things,” IEEE Internet Things J., vol. 7, no. 1, pp. 379–388, Jan. 2020. doi: 10.1109/JIOT.2019.2948149
    [110]
    M. Lopez-Martin, B. Carro, and A. Sanchez-Esguevillas, “Application of deep reinforcement learning to intrusion detection for supervised problems,” Expert Syst. Appl., vol. 141, p. 112963, Mar. 2020.
    [111]
    N. Kumar, V. Poonia, B. B. Gupta, and M. K. Goyal, “A novel framework for risk assessment and resilience of critical infrastructure towards climate change,” Technol. Forecast. Soc. Change, vol. 165, p. 120532, Apr. 2021.
    [112]
    W. Liang, K. C. Li, J. Long, X. Y. Kui, and A. Y. Zomaya, “An industrial network intrusion detection algorithm based on multifeature data clustering optimization model,” IEEE Trans. Ind. Inf., vol. 16, no. 3, pp. 2063–2071, Mar. 2020. doi: 10.1109/TII.2019.2946791
    [113]
    S. T. Park, G. Z. Li, and J. C. Hong, “A study on smart factory-based ambient intelligence context-aware intrusion detection system using machine learning,” J. Ambient Intell. Human. Comput., vol. 11, no. 4, pp. 1405–1412, Apr. 2020. doi: 10.1007/s12652-018-0998-6
    [114]
    F. Farivar, M. S. Haghighi, A. Jolfaei, and M. Alazab, “Artificial intelligence for detection, estimation, and compensation of malicious attacks in nonlinear cyber-physical systems and industrial IoT,” IEEE Trans. Ind. Inf., vol. 16, no. 4, pp. 2716–2725, Apr. 2020. doi: 10.1109/TII.2019.2956474
    [115]
    J. P. Liu, W. X. Zhang, T. Y. Ma, Z. H. Tang, Y. F. Xie, W. H. Gui, and J. P. Niyoyita, “Toward security monitoring of industrial cyber-physical systems via hierarchically distributed intrusion detection,” Expert Syst. Appl., vol. 158, p. 113578, Nov. 2020.
    [116]
    Y. Hu, H. Li, T. H. Luan, A. Yang, L. M. Sun, Z. L. Wang, and R. Wang, “Detecting stealthy attacks on industrial control systems using a permutation entropy-based method,” Future Gener. Comput. Syst., vol. 108, pp. 1230–1240, Jul. 2020. doi: 10.1016/j.future.2018.07.027
    [117]
    K. L. Miao, X. F. Shi, and W. A. Zhang, “Attack signal estimation for intrusion detection in industrial control system,” Comput. Secur., vol. 96, p. 101926, Sep. 2020.
    [118]
    M. N. Kurt, O. Ogundijo, C. Li, and X. D. Wang, “Online cyber-attack detection in smart grid: A reinforcement learning approach,” IEEE Trans. Smart Grid, vol. 10, no. 5, pp. 5174–5185, Sept. 2019. doi: 10.1109/TSG.2018.2878570
    [119]
    A. Patel, H. Alhussian, J. M. Pedersen, B. Bounabat, J. C. Júnior, and S. Katsikas, “A nifty collaborative intrusion detection and prevention architecture for smart grid ecosystems,” Comput. Secur., vol. 64, pp. 92–109, Jan. 2017. doi: 10.1016/j.cose.2016.07.002
    [120]
    L. Haghnegahdar and Y. Wang, “A whale optimization algorithm-trained artificial neural network for smart grid cyber intrusion detection,” Neural Comput. Appl., vol. 32, no. 13, pp. 9427–9441, Jul. 2020. doi: 10.1007/s00521-019-04453-w
    [121]
    M. Zhong, Y. J. Zhou, and G. Chen, “Sequential model based intrusion detection system for IoT servers using deep learning methods,” Sensors, vol. 21, no. 4, p. 1113, Feb. 2021.
    [122]
    S. Potluri and C. Diedrich, “Accelerated deep neural networks for enhanced intrusion detection system,” in Proc. IEEE 21st Int. Conf. Emerging Technologies and Factory Automation, Berlin, Germany, 2016, pp. 1−8.
    [123]
    W. C. Lin, S. W. Ke, and C. F. Tsai, “CANN: An intrusion detection system based on combining cluster centers and nearest neighbors,” Knowl.-Based Syst., vol. 78, pp. 13–21, Apr. 2015. doi: 10.1016/j.knosys.2015.01.009
    [124]
    N. Gao, L. Gao, Q. L. Gao, and H. Wang, “An intrusion detection model based on deep belief networks,” in Proc. Second Int. Conf. Advanced Cloud and Big Data, Huangshan, China, 2014, pp. 247−252.
    [125]
    A. Tesfahun and D. L. Bhaskari, “Intrusion detection using random forests classifier with SMOTE and feature reduction,” in Proc. Int. Conf. Cloud & Ubiquitous Computing & Emerging Technologies, Pune, India, 2013, pp. 127−132.
    [126]
    Y. H. Li, J. B. Xia, S. L. Zhang, J. K. Yan, X. C. Ai, and K. B. Dai, “An efficient intrusion detection system based on support vector machines and gradually feature removal method,” Expert Syst. Appl., vol. 39, no. 1, pp. 424–430, Jan. 2012. doi: 10.1016/j.eswa.2011.07.032
    [127]
    M. Lezoche, J. E. Hernandez, M. del Mar Eva Alemany Díaz, H. Panetto, and J. Kacprzyk, “Agri-food 4.0: A survey of the supply chains and technologies for the future agriculture,” Comput. Ind., vol. 117, p. 103187, May 2020.
    [128]
    N. Moustafa and J. Slay, “UNSW-NB15: A comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set),” in Proc. Military Communications and Information Systems Conf., Canberra, Australia, 2015, pp. 1−6.
    [129]
    M. Tavallaee, E. Bagheri, W. Lu, and A. A. Ghorbani, “A detailed analysis of the KDD CUP 99 data set,” in Proc. IEEE Symp. Computational Intelligence for Security and Defense Applications, Ottawa, Canada, 2009, pp. 1−6.
    [130]
    A. Shiravi, H. Shiravi, M. Tavallaee, and A. A. Ghorbani, “Toward developing a systematic approach to generate benchmark datasets for intrusion detection,” Comput. Secur., vol. 31, no. 3, pp. 357–374, May 2012. doi: 10.1016/j.cose.2011.12.012
    [131]
    Isot cloud intrusion dataset [Online]. https://www.uvic.ca/engineering/ece/isot/datasets/index.php, Accessed on: May 30, 2020.
    [132]
    HTTP DATASET CSIC 2010 [Online]. https://www.isi.csic.es/dataset/, Accessed on: May 30, 2020.
    [133]
    Kyoto 2006 dataset [Online]. http://www.takakura.com/Kyoto_data/, Accessed on: May 30, 2020.
    [134]
    I. Sharafaldin, A. H. Lashkari, and A. A. Ghorbani, “Toward generating a new intrusion detection dataset and intrusion traffic characterization,” in Proc. 4th Int. Conf. Information Systems Security and Privac, Funchal, Portugal, 2018, pp. 108−116.
    [135]
    Industrial control system (ICS) cyber attack datasets [Online]. https://sites.google.com/a/uah.edu/tommy-morris-uah/ics-data-sets, Accessed on: May 30, 2020.
    [136]
    AWID dataset [Online]. http://icsdweb.aegean.gr/awid/, Accessed on: May 30, 2020.
    [137]
    N. Koroniotis, N. Moustafa, E. Sitnikova, and B. Turnbull, “Towards the development of realistic Botnet dataset in the internet of things for network forensic analytics: Bot-IoT dataset,” Future Gener. Comput. Syst., vol. 100, pp. 779–796, Nov. 2019. doi: 10.1016/j.future.2019.05.041
    [138]
    TON_IOT datasets [Online]. https://ieee-dataport.org/documents/toniot-datasets, Mar. 3, 2021.
    [139]
    M. S. Elsayed, N. A. Le-Khac, and A. D. Jurcut, “InSDN: A novel SDN intrusion dataset,” IEEE Access, vol. 8, pp. 165263–165284, Sept. 2020. doi: 10.1109/ACCESS.2020.3022633
    [140]
    A. Alsaedi, N. Moustafa, Z. Tari, A. Mahmood, and A. Anwar, “TON_IoT telemetry dataset: A new generation dataset of IoT and IIoT for data-driven intrusion detection systems,” IEEE Access, vol. 8, pp. 165130–165150, Sept. 2020. doi: 10.1109/ACCESS.2020.3022862
    [141]
    M. S. Elsayed, N. A. Le-Khac, S. Dev, and A. D. Jurcut, “Network anomaly detection using LSTM based autoencoder,” in Proc. 16th ACM Symp. QoS and Security for Wireless and Mobile Networks, Alicante, Spain, 2020, pp. 37−45.
    [142]
    S. J. Stolfo, W. Fan, W. Lee, A. Prodromidis, and P. K. Chan, “Cost-based modeling for fraud and intrusion detection: Results from the JAM project,” in Proc. DARPA Information Survivability Conf. and Expo., Hilton Head, USA, 2000, pp. 130−144.
    [143]
    R. P. Lippmann, D. J. Fried, I. Graf, J. W. Haines, K. R. Kendall, D. McClung, D. Weber, S. E. Webster, D. Wyschogrod, R. K. Cunningham, and M. A. Zissman, “Evaluating intrusion detection systems: The 1998 DARPA off-line intrusion detection evaluation,” in Proc. DARPA Information Survivability Conf. and Expo., Los Alamitos, USA, 2000, pp. 12−26.
    [144]
    M. A. Ambusaidi, X. J. He, P. Nanda, and Z. Y. Tan, “Building an intrusion detection system using a filter-based feature selection algorithm,” IEEE Trans. Comput., vol. 65, no. 10, pp. 2986–2998, Oct. 2016. doi: 10.1109/TC.2016.2519914
    [145]
    F. Sadikin, T. van Deursen, and S. Kumar, “A ZigBee intrusion detection system for IoT using secure and efficient data collection,” Internet Things, vol. 12, p. 100306, Dec. 2020.
    [146]
    J. D. Ren, J. W. Guo, W. Qian, H. Yuan, X. B. Hao, and J. J. Hu, “Building an effective intrusion detection system by using hybrid data optimization based on machine learning algorithms,” Secur. Commun. Netw., vol. 2019, p. 7130868, Jun. 2019.
    [147]
    I. A. Khan, D. C. Pi, Z. U. Khan, Y. Hussain, and A. Nawaz, “HML-IDS: A hybrid-multilevel anomaly prediction approach for intrusion detection in SCADA systems,” IEEE Access, vol. 7, pp. 89507–89521, Jul. 2019. doi: 10.1109/ACCESS.2019.2925838
    [148]
    M. F. Elrawy, A. I. Awad, and H. F. A. Hamed, “Intrusion detection systems for IoT-based smart environments: A survey,” J. Cloud Comput., vol. 7, no. 1, p. 21, Dec. 2018.
    [149]
    Tensorflow [Online]. https://www.tensorflow.org, Jan. 5, 2021.
    [150]
    The microsoft cognitive toolkit [Online]. https://docs.microsoft.com/en-us/cognitive-toolkit/, Accessed on: Jan. 5, 2021.
    [151]
    Apache SINGA [Online]. https://singa.apache.org/, Accessed on: Jan. 5, 2021.
    [152]
    Caffe [Online]. https://caffe.berkeleyvision.org/, Jan. 5, 2021.
    [153]
    Eclipse deeplearning4j [Online]. https://deeplearning4j.org/, Accessed on: Jan. 5, 2021.
    [154]
    PyTorch [Online]. https://pytorch.org/, Accessed on: Jan. 5, 2021.
    [155]
    Keras [Online]. https://keras.io/, Accessed on: Jan. 5, 2021.
    [156]
    Apache MXNet [Online]. https://mxnet.apache.org/, Accessed on: Jan. 5, 2021.
    [157]
    Theano [Online]. https://pypi.org/project/Theano/, Accessed on: Jan. 5, 2021.
    [158]
    S. AlZu’bi, B. Hawashin, M. Mujahed, Y. Jararweh, and B. B. Gupta, “An efficient employment of internet of multimedia things in smart and future agriculture,” Multim. Tools Appl., vol. 78, no. 20, pp. 29581–29605, Feb. 2019. doi: 10.1007/s11042-019-7367-0
    [159]
    X. M. Huang, D. D. Ye, R. Yu, and L. Shu, “Securing parked vehicle assisted fog computing with blockchain and optimal smart contract design,” IEEE/CAA J. Autom. Sinica, vol. 7, no. 2, pp. 426–441, Mar. 2020. doi: 10.1109/JAS.2020.1003039
    [160]
    P. Y. Zhang and M. C. Zhou, “Security and trust in blockchains: Architecture, key technologies, and open issues,” IEEE Trans. Comput. Soc. Syst., vol. 7, no. 3, pp. 790–801, Jun. 2020. doi: 10.1109/TCSS.2020.2990103
    [161]
    K. Arulkumaran, M. P. Deisenroth, M. Brundage, and A. A. Bharath, “Deep reinforcement learning: A brief survey,” IEEE Signal Process. Mag., vol. 34, no. 6, pp. 26–38, Nov. 2017. doi: 10.1109/MSP.2017.2743240
    [162]
    T. Liu, B. Tian, Y. F. Ai, and F. Y. Wang, “Parallel reinforcement learning-based energy efficiency improvement for a cyber-physical system,” IEEE/CAA J. Autom. Sinica, vol. 7, no. 2, pp. 617–626, Mar. 2020. doi: 10.1109/JAS.2020.1003072
    [163]
    J. Qiu, Z. H. Tian, C. L. Du, Q. Zuo, S. Su, and B. X. Fang, “A survey on access control in the age of internet of things,” IEEE Internet Things J., vol. 7, no. 6, pp. 4682–4696, Jun. 2020. doi: 10.1109/JIOT.2020.2969326
    [164]
    D. N. Cheng, H. P. Zhang, F. Xia, S. G. Li, Y. Q. Zhang, “The scalability for parallel machine learning training algorithm: Dataset matters,” arXiv preprint arXiv: 1910.11510, 2019.
    [165]
    L. Muñoz-González, B. Pfitzner, M. Russo, J. Carnerero-Cano, and E. C. Lupu, “Poisoning attacks with generative adversarial nets,” arXiv preprint arXiv: 1906.07773, 2019.
    [166]
    A. Gouveia and M. Correia, “Towards quantum-enhanced machine learning for network intrusion detection,” in Proc. IEEE 19th Int. Symp. Network Computing and Applications, Cambridge, USA, 2020, pp. 1−8.
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    Highlights

    • 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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