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Privacy and Security in Smart and Precision Farming: A Bibliometric Analysis

Abstract

By using IoT in agriculture which is used for remote monitoring and automation bring a new concern about security and privacy, due to facing huge scale of data in its environment. Most studies aim to present novel solutions for providing a framework or an application to protect data and prevent data breach. However, in spite of many articles to support research activities, there is still no publication of bibliometric report that considers the research trends. This paper aims to providing comprehensive assess about security and privacy in smart farming researches and fill in the gap. All publications of ISI Web of Science database are considered which was about 150 between 2008 and 2018. By using bibliometric analysis, the number of publications along with the number of citations discusses. This paper also presents analysis by focusing on countries and continents, research areas, authors, institutions, terms and keywords.

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References

  1. S. Grooby, T. Dargahi, A. Dehghantanha, A bibliometric analysis of authentication and access control in IoT devices, in Handbook of Big Data and IoT security (Springer International Publishing, Cham, 2019), pp. 25–51

    Google Scholar 

  2. A. Azmoodeh, A. Dehghantanha, K.-K.R. Choo, Big data and internet of things security and forensics: challenges and opportunities, in Handbook of Big Data and IoT Security (Springer International Publishing, Cham, 2019), pp. 1–4

    Google Scholar 

  3. M.M. Najafabadi, F. Villanustre, T.M. Khoshgoftaar, N. Seliya, R. Wald, E. Muharemagic, Deep learning applications and challenges in big data analytics. J. Big Data 2, 1 (2015)

    CrossRef  Google Scholar 

  4. S. Mohammadi, H. Mirvaziri, M. Ghazizadeh-Ahsaee, H. Karimipour, Cyber intrusion detection by combined feature selection algorithm. J. Inf. Secur. Appl. 44, 80–88 (2019)

    Google Scholar 

  5. E.M. Dovom, A. Azmoodeh, A. Dehghantanha, D.E. Newton, R.M. Parizi, H. Karimipour, Fuzzy pattern tree for edge malware detection and categorization in IoT. J. Syst. Archit. 97, 1–7 (2019)

    CrossRef  Google Scholar 

  6. H.H. Pajouh, R. Javidan, R. Khayami, A. Dehghantanha, K.K.R. Choo, A two-layer dimension reduction and two-tier classification model for anomaly-based intrusion detection in IoT backbone networks. IEEE Trans. Emerg. Top. Comput. 7(2), 314–323 (2019)

    CrossRef  Google Scholar 

  7. A. Azmoodeh, A. Dehghantanha, K.-K.R. Choo, Robust malware detection for internet of (Battlefield) things devices using deep eigenspace learning. IEEE Trans. Sustain. Comput. 4(1), 88–95 (Feb. 2018)

    CrossRef  Google Scholar 

  8. M. Brown, Smart farming—automated and connected agriculture (2018)

    Google Scholar 

  9. J. Sakhnini, H. Karimipour, A. Dehghantanha, Smart grid cyber attacks detection using supervised learning and heuristic feature selection, in 2019 IEEE 7th International Conference on Smart Energy Grid Engineering (SEGE) (IEEE, 2019), pp. 108–112

    Google Scholar 

  10. A. Azmoodeh, A. Dehghantanha, M. Conti, K.K.R. Choo, Detecting crypto-ransomware in IoT networks based on energy consumption footprint. J. Ambient. Intell. Humaniz. Comput. 9(4), 1141–1152 (2018)

    CrossRef  Google Scholar 

  11. M.R. Begli, F. Derakhshan, H. Karimipour, A layered intrusion detection system for critical infrastructure using machine learning, in 2019 IEEE 7th International Conference on Smart Energy Grid Engineering (SEGE) (IEEE, 2019), pp. 120–124

    Google Scholar 

  12. S. Geris, H. Karimipour, A feature selection-based approach for joint cyber-attack detection and state estimation, in IEEE Int. Conf. on Smart Energy Grid Engineering (SEGE) (IEEE, 2019)

    Google Scholar 

  13. H. Karimipour, S. Geris, A. Dehghantanha, H. Leung, Intelligent anomaly detection for large-scale smart grids, in 2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE) (IEEE, 2019), pp. 1–4

    Google Scholar 

  14. A. Kamilaris, F. Gao, F.X. Prenafeta-Boldu, M.I. Ali, Agri-IoT: a semantic framework for Internet of Things-enabled smart farming applications, in 2016 IEEE 3rd World Forum on Internet of Things, WF-IoT 2016 (IEEE, 2017), pp. 442–447

    Google Scholar 

  15. M.M. Jahn et al., Cyber risk and security implications in smart agriculture and food systems (2019)

    Google Scholar 

  16. Z. Zorz, FBI warns farming industry about equipment hacks, data breaches (2016)

    Google Scholar 

  17. G. Information, APT28 under the scope – a journey into exfiltrating intelligence (2015)

    Google Scholar 

  18. B. Reaves, T. Morris, Analysis and mitigation of vulnerabilities in short-range wireless communications for industrial control systems. Int. J. Crit. Infrastruct. Prot. 5, 154–174 (2012)

    CrossRef  Google Scholar 

  19. N. Trantham, A. Garcia, Reputation dynamics in networks: Application to cyber security of wind farms. Syst. Eng. 18, 339–348 (2015)

    CrossRef  Google Scholar 

  20. H. Chi, S. Welch, E. Vasserman, E. Kalaimannan, A framework of cybersecurity approaches in precision agriculture (2017)

    Google Scholar 

  21. C.L. Borgman, Communication and Collaboration Scholarlv Communication and Bibliometrics. Annu. Rev. Inf. Sci. Technol. 36(1), 2–72 (2002)

    CrossRef  Google Scholar 

  22. P. Zhang, F. Yan, C. Du, A comprehensive analysis of energy management strategies for hybrid electric vehicles based on bibliometrics. Renew. Sust. Energ. Rev. 48, 88–104 (2015)

    CrossRef  Google Scholar 

  23. F. Madani, ‘Technology Mining’ bibliometrics analysis: applying network analysis and cluster analysis. Scientometrics 105, 323–335 (2015)

    CrossRef  Google Scholar 

  24. J. Koskinen et al., How to use bibliometric methods in evaluation of scientific research? An example from Finnish schizophrenia research. Nord. J. Psychiatry 62(2), 136–143 (2008)

    MathSciNet  CrossRef  Google Scholar 

  25. I. Danvila-del-Valle, C. Estévez-Mendoza, F.J. Lara, Human resources training: a bibliometric analysis. J. Bus. Res 101, 627–636 (2019)

    CrossRef  Google Scholar 

  26. A.M. Palacios-Marqués et al., Worldwide scientific production in obstetrics: a bibliometric analysis. Ir. J. Med. Sci. 188, 913–919 (2019)

    CrossRef  Google Scholar 

  27. É. Archambault, D. Campbell, Y. Gingras, V. Larivière, Comparing bibliometric statistics obtained from the web of science and Scopus. J. Am. Soc. Inf. Sci. Technol. 60, 1320–1326 (2009)

    CrossRef  Google Scholar 

  28. J. Mingers, L. Leydesdorff, A review of theory and practice in scientometrics. Eur. J. Oper. Res. 246(1), 1–19 (2015)

    CrossRef  Google Scholar 

  29. C. López-Illescas, F. de Moya-Anegón, H.F. Moed, Coverage and citation impact of oncological journals in the Web of Science and Scopus. J. Informetr. 2, 304–316 (2008)

    CrossRef  Google Scholar 

  30. S. Wolfert, L. Ge, C. Verdouw, M.J. Bogaardt, Big data in smart farming – a review. Agric. Syst. 153, 69–80 (2017)

    CrossRef  Google Scholar 

  31. N. Hossein Motlagh, T. Taleb, O. Arouk, Low-altitude unmanned aerial vehicles-based internet of things services: comprehensive survey and future perspectives. IEEE Internet Things J. 3(6), 899–922 (2016)

    CrossRef  Google Scholar 

  32. S. Janssen, E. Andersen, I.N. Athanasiadis, M.K. van Ittersum, A database for integrated assessment of European agricultural systems. Environ. Sci. Pol. 12(5), 573–587 (2009)

    CrossRef  Google Scholar 

  33. E. Ahmed et al., The role of big data analytics in Internet of Things. Comput. Netw. 129, 459–471 (2017)

    CrossRef  Google Scholar 

  34. H. Karimipour, A. Dehghantanha, R.M. Parizi, K.K.R. Choo, H. Leung, A deep and scalable unsupervised machine learning system for cyber-attack detection in large-scale smart grids. IEEE Access 7, 80778–80788 (2019)

    CrossRef  Google Scholar 

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Correspondence to Sanaz Nakhodchi .

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Nakhodchi, S., Dehghantanha, A., Karimipour, H. (2020). Privacy and Security in Smart and Precision Farming: A Bibliometric Analysis. In: Choo, KK., Dehghantanha, A. (eds) Handbook of Big Data Privacy. Springer, Cham. https://doi.org/10.1007/978-3-030-38557-6_14

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  • DOI: https://doi.org/10.1007/978-3-030-38557-6_14

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