Advertisement

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

  • Sanaz NakhodchiEmail author
  • Ali Dehghantanha
  • Hadis Karimipour
Chapter
  • 22 Downloads

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.

References

  1. 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. 25–51Google Scholar
  2. 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. 1–4Google Scholar
  3. 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)CrossRefGoogle Scholar
  4. 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. 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)CrossRefGoogle Scholar
  6. 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)CrossRefGoogle Scholar
  7. 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)CrossRefGoogle Scholar
  8. 8.
    M. Brown, Smart farming—automated and connected agriculture (2018)Google Scholar
  9. 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. 108–112Google Scholar
  10. 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)CrossRefGoogle Scholar
  11. 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. 120–124Google Scholar
  12. 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. 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. 1–4Google Scholar
  14. 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. 442–447Google Scholar
  15. 15.
    M.M. Jahn et al., Cyber risk and security implications in smart agriculture and food systems (2019)Google Scholar
  16. 16.
    Z. Zorz, FBI warns farming industry about equipment hacks, data breaches (2016)Google Scholar
  17. 17.
    G. Information, APT28 under the scope – a journey into exfiltrating intelligence (2015)Google Scholar
  18. 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)CrossRefGoogle Scholar
  19. 19.
    N. Trantham, A. Garcia, Reputation dynamics in networks: Application to cyber security of wind farms. Syst. Eng. 18, 339–348 (2015)CrossRefGoogle Scholar
  20. 20.
    H. Chi, S. Welch, E. Vasserman, E. Kalaimannan, A framework of cybersecurity approaches in precision agriculture (2017)Google Scholar
  21. 21.
    C.L. Borgman, Communication and Collaboration Scholarlv Communication and Bibliometrics. Annu. Rev. Inf. Sci. Technol. 36(1), 2–72 (2002)CrossRefGoogle Scholar
  22. 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)CrossRefGoogle Scholar
  23. 23.
    F. Madani, ‘Technology Mining’ bibliometrics analysis: applying network analysis and cluster analysis. Scientometrics 105, 323–335 (2015)CrossRefGoogle Scholar
  24. 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)MathSciNetCrossRefGoogle Scholar
  25. 25.
    I. Danvila-del-Valle, C. Estévez-Mendoza, F.J. Lara, Human resources training: a bibliometric analysis. J. Bus. Res 101, 627–636 (2019)CrossRefGoogle Scholar
  26. 26.
    A.M. Palacios-Marqués et al., Worldwide scientific production in obstetrics: a bibliometric analysis. Ir. J. Med. Sci. 188, 913–919 (2019)CrossRefGoogle Scholar
  27. 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)CrossRefGoogle Scholar
  28. 28.
    J. Mingers, L. Leydesdorff, A review of theory and practice in scientometrics. Eur. J. Oper. Res. 246(1), 1–19 (2015)CrossRefGoogle Scholar
  29. 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)CrossRefGoogle Scholar
  30. 30.
    S. Wolfert, L. Ge, C. Verdouw, M.J. Bogaardt, Big data in smart farming – a review. Agric. Syst. 153, 69–80 (2017)CrossRefGoogle Scholar
  31. 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)CrossRefGoogle Scholar
  32. 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)CrossRefGoogle Scholar
  33. 33.
    E. Ahmed et al., The role of big data analytics in Internet of Things. Comput. Netw. 129, 459–471 (2017)CrossRefGoogle Scholar
  34. 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)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.School of Computer ScienceUniversity of GuelphGuelphCanada
  2. 2.School of EngineeringUniversity of GuelphGuelphCanada

Personalised recommendations