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Information Visualization as a Method for Cybersecurity Education

  • Antonio González-TorresEmail author
  • Mónica Hernández-Campos
  • Jeferson González-Gómez
  • Vetria L. Byrd
  • Paul Parsons
Chapter
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Abstract

Cybersecurity education is challenging due to the complexity and abstract nature of concepts and data. In this context, information visualization is one method for teaching cybersecurity in a concrete and engaging manner. In this chapter we present a review of some cognitive principles and instructional design strategies that are relevant for cybersecurity education using information visualization. We also discuss the use of two existing cybersecurity visualization tools, Visual Knowledge Explorer and BubbleNet, to help students learn about cybersecurity.

Keywords

Information visualization Cybersecurity Cybersecurity education 

Notes

Acknowledgments

The authors wish to thank La Universidad Latinoamericana de Ciencia y Tecnología (ULACIT), Costa Rica, for its support to this research and also to Juan Alvarez Piedra, Osael Josue Jimenez Murillo, and Pablo Andrés Rodríguez Blanco for their contribution with code for VKE.

References

  1. 1.
    M. Scaife, Y. Rogers, External cognition: how do graphical representations work? Int. J. Hum. Comput. Stud. 45(2), 185–213 (1996)CrossRefGoogle Scholar
  2. 2.
    K. Sedig, P. Parsons, Interaction design for complex cognitive activities with visual representations: a pattern-based approach. AIS Trans. Hum. Comput. Interact. 5(2), 84–133 (2013)CrossRefGoogle Scholar
  3. 3.
    N.A. Giacobe, M.D. McNeese, V.F. Mancuso, D. Minotra, Capturing human cognition in cyber-security simulations with NETS, in 2013 IEEE International Conference on Intelligence and Security Informatics, (2013), pp. 284–288CrossRefGoogle Scholar
  4. 4.
    J. Klerkx, K. Verbert, E. Duval, Enhancing learning with visualization techniques, in Handbook of Research on Educational Communications and Technology, ed. by J. M. Spector, M. D. Merrill, J. Elen, M. J. Bishop, (Springer New York, New York, NY, 2014), pp. 791–807CrossRefGoogle Scholar
  5. 5.
    A. Gonzalez-Torres, V.L. Byrd, P. Parsons, VKE: a visual analytics tool for cybersecurity data, in 2019 International Conference on Security and Management (SAM’19), (2019), pp. 56–62Google Scholar
  6. 6.
    A. González-Torres, F.J. García-Peñalvo, R. Therón, A. González-Torres, F.J. García-Peñalvo, R. Therón, Human–computer interaction in evolutionary visual software analytics. Comput. Hum. Behav. 29(2), 486–495 (2013)CrossRefGoogle Scholar
  7. 7.
    A. González-Torres, F.J. García-Peñalvo, R. Therón-Sánchez, R. Colomo-Palacios, Knowledge discovery in software teams by means of evolutionary visual software analytics. Sci. Comput. Program. 121, 55–74 (2016)CrossRefGoogle Scholar
  8. 8.
    G.R. Garay, A. Tchernykh, A.Y. Drozdov, S.N. Garichev, S. Nesmachnow, M. Torres-Martinez, Visualization of VHDL-based simulations as a pedagogical tool for supporting computer science education. J. Comput. Sci. 36, 100652 (2019)CrossRefGoogle Scholar
  9. 9.
    J.C. Castro-Alonso, P. Ayres, J. Sweller, Instructional visualizations, cognitive load theory, and visuospatial processing, in Visuospatial Processing for Education in Health and Natural Sciences, (Springer International Publishing, Cham, 2019), pp. 111–143CrossRefGoogle Scholar
  10. 10.
    C. Vieira, P. Parsons, V. Byrd, Visual learning analytics of educational data: a systematic literature review and research agenda. Comput. Educ. 122, 119–135 (2018)CrossRefGoogle Scholar
  11. 11.
    R.E. Mayer, J.K. Gallini, When is an illustration worth ten thousand words? J. Educ. Psychol. 82(4), 715–726 (1990)CrossRefGoogle Scholar
  12. 12.
    I. Vekiri, What is the value of graphical displays in learning? Educ. Psychol. Rev. 14, 261–312 (2002)CrossRefGoogle Scholar
  13. 13.
    M.M. North, S.M. North, Dynamic immersive visualisation environments: enhancing pedagogical techniques. Australas. J. Inf. Syst. 23 (2019)Google Scholar
  14. 14.
    D. Schweitzer, W. Brown, Interactive visualization for the active learning classroom, in Proceedings of 38th SIGCSE Technical Symposium on Computer Science Education, (2007), pp. 208–212Google Scholar
  15. 15.
    T.L. Naps, et al., Exploring the role of visualization and engagement in computer science education, Working Group Reports on ITiCSE Innovation and Technology in Computer Science Education, 2002, pp. 131–152Google Scholar
  16. 16.
    P. Antoniou, L. Kyriakides, A dynamic integrated approach to teacher professional development: Impact and sustainability of the effects on improving teacher behaviour and student outcomes. Teach. Teach. Educ. 29, 1–12 (2013)CrossRefGoogle Scholar
  17. 17.
    R.M. Shiffrin, R.C. Atkinson, Storage and retrieval processes in long-term memory. Psychol. Rev. 76(2), 179–193 (1969)CrossRefGoogle Scholar
  18. 18.
    J. Sweller, Cognitive Load Theory, Evolutionary Educational Psychology, and Instructional Design (Springer, Cham, 2016)CrossRefGoogle Scholar
  19. 19.
    P.A. Kirschner, J. Sweller, R.E. Clark, Why minimal guidance during instruction does not work: an analysis of the failure of constructivist, discovery, problem-based, experiential, and inquiry-based teaching. Educ. Psychol. 41(2), 75–86 (2006)CrossRefGoogle Scholar
  20. 20.
    R.E. Mayer, Should there be a three-strikes rule against pure discovery learning? The case for guided methods of instruction. Am. Psychol. 59(1), 14–19 (2004)CrossRefGoogle Scholar
  21. 21.
    C.M. Reigeluth, B.J. Beatty, R.D. Myers, Instructional-Design Theories and Models, Volume IV: The Learner-Centered Paradigm of Education (Routledge, New York, 2016)CrossRefGoogle Scholar
  22. 22.
    L.W. Anderson, D.R. Krathwohl, et al., A Taxonomy for Learning, Teaching, and Assessing. Abridged Edition (Allyn and Bacon, Boston, MA, 2001)Google Scholar
  23. 23.
    B.S. Bloom et al., Taxonomy of Educational Objectives. Vol. 1: Cognitive Domain (McKay, New York, 1956), pp. 20–24Google Scholar
  24. 24.
    T. Mahmood, U. Afzal, Security analytics: big data analytics for cybersecurity: a review of trends, techniques and tools, in 2013 2nd National Conference on Information Assurance (NCIA), (2013), pp. 129–134CrossRefGoogle Scholar
  25. 25.
    E. Glatz, S. Mavromatidis, B. Ager, X. Dimitropoulos, Visualizing big network traffic data using frequent pattern mining and hypergraphs. Computing 96(1), 27–38 (2014)CrossRefGoogle Scholar
  26. 26.
    H. Shiravi, A. Shiravi, A.A. Ghorbani, A survey of visualization systems for network security. IEEE Trans. Vis. Comput. Graph. 18(8), 1313–1329 (2012)CrossRefGoogle Scholar
  27. 27.
    A. González Torres, F.J. García-Peñalvo, R. Therón-Sánchez, How evolutionary visual software analytics supports knowledge discovery. J. Inf. Sci. Eng. 29(1), 17–34 (2013)Google Scholar
  28. 28.
    J.J. van Wijk, The value of visualization. Vis. Conf. IEEE 0, 11 (2005)Google Scholar
  29. 29.
    B. Shneiderman, The eyes have it: a task by data type taxonomy for information visualizations, in Proceedings 1996 IEEE Symposium on Visual Languages, (1996), pp. 336–343CrossRefGoogle Scholar
  30. 30.
    S.K. Card, J. Mackinlay, B. Shneiderman, Readings in Information Visualization: Using Vision to Think (Morgan Kaufman, San Francisco, CA, 1999)Google Scholar
  31. 31.
    E.H. Chi, A taxonomy of visualization techniques using the data state reference model, in Proceedings of the IEEE Symposium on Information Vizualization 2000, 2000, p. 69Google Scholar
  32. 32.
    N. Marz, J. Warren, Big Data: Principles and Best Practices of Scalable Realtime Data Systems, 1st edn. (Manning Publications Co, Greenwich, CT, 2015)Google Scholar
  33. 33.
    A. Fasale, N. Kumar, YARN Essentials (Packt Publishing, Birmingham, 2015)Google Scholar
  34. 34.
    A.G. Psaltis, Streaming Data: Understanding the Real-Time Pipeline (Manning Publications Company, Shelter Island, NY, 2017)Google Scholar
  35. 35.
    L. Antova et al., Datometry hyper-Q: bridging the gap between real-time and historical analytics, in Proceedings of the 2016 International Conference on Management of Data, (2016), pp. 1405–1416Google Scholar
  36. 36.
    J. Román, The Hadoop Ecosystem Table, 2017Google Scholar
  37. 37.
    S. McKenna, D. Staheli, C. Fulcher, M. Meyer, Bubblenet: a cyber security dashboard for visualizing patterns. Comput. Graph. Forum 35(3), 281–290 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Antonio González-Torres
    • 1
    • 2
    Email author
  • Mónica Hernández-Campos
    • 1
  • Jeferson González-Gómez
    • 1
  • Vetria L. Byrd
    • 3
  • Paul Parsons
    • 3
  1. 1.Costa Rica Institute of TechnologyCartagoCosta Rica
  2. 2.ULACITSan JoséCosta Rica
  3. 3.Purdue UniversityWest LafayetteUSA

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