, Volume 121, Issue 2, pp 1189–1211 | Cite as

Investigating the applications of artificial intelligence in cyber security

  • Naveed Naeem Abbas
  • Tanveer Ahmed
  • Syed Habib Ullah Shah
  • Muhammad OmarEmail author
  • Han Woo ParkEmail author


Artificial Intelligence (AI) provides instant insights to pierce through the noise of thousands of daily security alerts. The recent literature focuses on AI’s application to cyber security but lacks visual analysis of AI applications. Structural changes have been observed in cyber security since the emergence of AI. This study promotes the development of theory about AI in cyber security, helps researchers establish research directions, and provides a reference that enterprises and governments can use to plan AI applications in the cyber security industry. Many countries, institutions and authors are densely connected through collaboration and citation networks. Artificial neural networks, an AI technique, gave birth to today’s research on cloud cyber security. Many research hotspots such as those on face recognition and deep neural networks for speech recognition may create future hotspots on emerging technology, such as on artificial intelligence systems for security. This study visualizes the structural changes, hotspots and emerging trends in AI studies. Five evaluation factors are used to judge the hotspots and trends of this domain and a heat map is used to identify the areas of the world that are generating research on AI applications in cyber security. This study is the first to provide an overall perspective of hotspots and trends in the research on AI in the cyber security domain.


Artificial intelligence Cyber security Scientometric Visualization Emerging trend Research hotspot 



I wish to acknowledge someone who means a lot to me, my father (Mr. Irshad Hussain), for showing faith in me and giving me the liberty to make my own choices. I salute you for the selfless love, care, pain and sacrifice you offered to me in order to shape my life.


  1. Aghion, P., Jones, B. F., & Jones, C. I. (2017). Artificial intelligence and economic growth. NBER Working Paper Series. Scholar
  2. Byres, E. (2004). The myths and facts behind cyber security risks for industrial control systems. Proceedings of the VDE Kongress.
  3. Chen, C. (2006). CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature. Journal of the American Society for Information Science and Technology. Scholar
  4. Chen, C. (2016). How to use CiteSpace. British Columbia, Canada: Lean Publishing. Retrieved from
  5. Chen, C., Dubin, R., & Kim, M. C. (2014). Orphan drugs and rare diseases: A scientometric review (2000–2014). Expert Opinion on Orphan Drugs, 2(7), 709–724. Scholar
  6. Chen, C., & Leydesdorff, L. (2013). Patterns of connections and movements in dual-map overlays: A new method of publication portfolio analysis. Journal of the American Society for Information Science and Technology. Retrieved from
  7. Chen, H., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS Quarterly, 36(4), 1165–1188. Scholar
  8. Dilek, S., Cakır, H., & Aydın, M. (2015). Applications of artificial intelligence techniques to combating cyber crimes: A review. International Journal of Artificial Intelligence & Applications, 6(1), 21–39. Scholar
  9. Gautam, P. (2019). A bibliometric approach for department-level disciplinary analysis and science mapping of research output using multiple classification schemes. Journal of Contemporary Eastern Asia, 18(1), 7–29. Scholar
  10. Göztepe, K. (2012). Designing fuzzy rule based expert system for cyber security. International Journal of Information Security Science, 1(1), 13–19.Google Scholar
  11. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009). The WEKA data mining software: An update. ACM SIGKDD Explorations Newsletter, 11. Retrieved from
  12. Hengstler, M., Enkel, E., & Duelli, S. (2016). Technological forecasting & social change applied artificial intelligence and trust—The case of autonomous vehicles and medical assistance devices. Technological Forecasting and Social Change, 105, 105–120. Scholar
  13. Holmberg, K., & Park, H. W. (2018). An altmetric investigation of the online visibility of South Korea-based scientific journals. Scientometrics, 117(1), 603–613.CrossRefGoogle Scholar
  14. Imran, M., Castillo, C., Lucas, J., Meier, P., & Vieweg, S. (2014). Aidr. In Proceedings of the 23rd international conference on world wide webWWW’14 companion, (April) (pp. 159–162).
  15. Jan, N., & Ludo, V. E. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics. Scholar
  16. Jha, S., & Topol, E. J. (2016). Adapting to artificial intelligence: Radiologists and pathologists as information specialists. JAMA Journal of the American Medical Association, 316(22), 2353–2354. Scholar
  17. Jin, Y., & Li, X. (2018). Visualizing the hotspots and emerging trends of multimedia big data through scientometrics. Multimedia Tools and Applications. Scholar
  18. Kim, H. J., Jeong, Y. K., & Song, M. (2016). Content- and proximity-based author co-citation analysis using citation sentences. Journal of Informetrics, 10(4), 954–966. Scholar
  19. Li, S., & Sun, Y. (2013). The application of weighted co‐occurring keywords time gram in academic research temporal sequence discovery. Proceedings of the American Society for Information Science and Technology, 50(1), 1–10. Scholar
  20. Li, J., Xu, W. W., Wang, F., Chen, S., & Sun, J. (2018). Examining China’s internet policies through a bibliometric approach. Journal of Contemporary Eastern Asia, 17(2), 237–253. Scholar
  21. Litman, T. (2014). Autonomous vehicle implementation predictions implications for transport planning. Transportation Research Board Annual Meeting, 42(January), 36–42. Scholar
  22. Liu, S., Chen, C., Ding, K., Wang, B., Xu, K., & Lin, Y. (2014). Literature retrieval based on citation context. Scientometrics, 101(2), 1293–1307. Scholar
  23. Loebbecke, C., & Picot, A. (2015). Reflections on societal and business model transformation arising from digitization and big data analytics: A research agenda. Journal of Strategic Information Systems, 24(3), 149–157. Scholar
  24. Machine, P., & Tools, L. (n.d.). Datamining. Practical machine learning tools and technicals with java implementations. Google Scholar
  25. Malav, A., Kadam, K., & Kamat, P. (2017). Prediction of heart disease using K-means and artificial neural network as hybrid approach to improve accuracy. International Journal of Engineering and Technology, 9(4), 3081–3085. Scholar
  26. Ofli, F., Meier, P., Imran, M., Castillo, C., Tuia, D., Rey, N., et al. (2016). Combining human computing and machine learning to make sense of big (aerial) data for disaster response. Big Data, 4(1), 47–59. Scholar
  27. Omar, M., Mehmood, A., Choi, G. S., & Park, H. W. (2017). Global mapping of artificial intelligence in Google and Google Scholar. Scientometrics, 113(3), 1269–1305. Scholar
  28. Pak Chung, W., Chen, C., Gorg, C., Shneiderman, B., Stasko, J., & Thomas, J. (2011). Graph analytics-lessons learned and challenges ahead. IEEE Computer Graphics and Applications, 31(5), 18–29. Scholar
  29. Pannu, A. (2015). Artificial intelligence and its application in different areas. Certified International Journal of Engineering and Innovative Technology, 4(10), 79–84. Scholar
  30. Park, H. J., & Park, H. W. (2018). Two-side face of knowledge building using scientometric analysis. Quality & Quantity, 52(6), 2815–2836.CrossRefGoogle Scholar
  31. Park, H. C., Youn, J. M., & Park, H. W. (2018). Global mapping of scientific information exchange using altmetric data. Quality & Quantity, 53(2), 935–955.CrossRefGoogle Scholar
  32. Parkes, D. C., & Wellman, M. P. (2015). Economic reasoning and artificial intelligence. Science, 349(6245), 267–272. Scholar
  33. Ramchurn, S. D., Huynh, T. D., Wu, F., Ikuno, Y., Flann, J., Moreau, L., et al. (2016). A disaster response system based on human-agent collectives. Journal of Artificial Intelligence Research, 57, 661–708. Scholar
  34. Saridakis, G., Benson, V., Ezingeard, J., & Tennakoon, H. (2015). Technological forecasting & social change individual information security, user behaviour and cyber victimisation: An empirical study of social networking users. Technological Forecasting and Social Change. Scholar
  35. Small, H., & Greenlee, E. (1980). Citation context analysis of a co-citation cluster: Recombinant-DNA. Scientometrics, 2(4), 277–301. Scholar
  36. Su, H. N., & Lee, P. C. (2010). Mapping knowledge structure by keyword co-occurrence: A first look at journal papers in Technology Foresight. Scientometrics, 85(1), 65–79. Scholar
  37. Wang, F. Y., Zheng, N. N., Cao, D., Martinez, C. M., Li, L., & Liu, T. (2017). Parallel driving in CPSS: A unified approach for transport automation and vehicle intelligence. IEEE/CAA Journal of Automatica Sinica, 4(4), 577–587. Scholar
  38. Zhou, Z. H., & Jiang, Y. (2003). Medical diagnosis with C4.5 Rule preceded by artificial neural network ensemble. IEEE Transactions on Information Technology in Biomedicine, 7(1), 37–42. Scholar

Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2019

Authors and Affiliations

  1. 1.Department of Computer Science and ITThe Islamia University of BahawalpurBahawalpurPakistan
  2. 2.Dera Ghazi KhanPakistan
  3. 3.Department of Computer ScienceCOMSATS UniversityIslamabadPakistan
  4. 4.Dera Ghazi KhanPakistan
  5. 5.Department of Media and Communication, Interdisciplinary Program of Digital Convergence BusinessYeungNam UniversityGyeongsan-siSouth Korea

Personalised recommendations