Interactive Stereoscopically Perceivable Multidimensional Data Visualizations for Cybersecurity

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interactive_stereoscopically_perceivable_multidimensional_data_visualizations_for_cybersecurity-1.pdf (1.85 MB)

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https://www.jdst.eu/publications/interactive-stereoscopically-perceivable-multidimensional-data-visualizations

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https://doi.org/10.46713/jdst.004.03
 http://hdl.handle.net/11603/24821

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UMBC Computer Science and Electrical Engineering Department
UMBC Center for Space Sciences and Technology (CSST) / Center for Research and Exploration in Space Sciences & Technology II (CRSST II)
UMBC Faculty Collection
UMBC Office for the Vice President of Research & Creative Achievement (ORCA)

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Author/Creator ORCID

https://orcid.org/0000-0001-9480-0583
 https://orcid.org/0000-0003-2838-0140

Date

2021-12-12

Type of Work

journal articles
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Citation of Original Publication

Kullman, Kaur, and Don Engel. "Interactive Stereoscopically Perceivable Multidimensional Data Visualizations for Cybersecurity." Journal of Defence & Security Technologies 4, no. 3 (2022): 37-52.

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Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)

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Abstract

Interactive Data Visualizations (IDV) can be useful for cybersecurity subject matter experts (CSMEs) while they are exploring new data or investigating familiar datasets for anomalies, correlating events, etc. For an IDV to be useful to a CSME, interaction with that visualization should be simple and intuitive (free of additional mental tasks) and the visualization’s layout must map to a CSME’s understanding. While CSMEs may learn to interpret visualizations created by others, they should be encouraged to visualize their datasets in ways that best reflect their own ways of thinking. Developing their own visual schemes makes optimal use of both the data analysis tools and human visual cognition. In this article, we focus on a currently available interactive stereoscopically perceivable multidimensional data visualization solution, as such tools could provide CSMEs with better perception of their data compared to interpreting IDV on flat media (whether visualized as 2D or 3D structures).