Mental Model Mapping Method for Cybersecurity
Loading...
Files
Collections
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 Imaging Research Center (IRC)
UMBC Information Systems Department
UMBC Office for the Vice President of Research & Creative Achievement (ORCA)
Load more UMBC Center for Space Sciences and Technology (CSST) / Center for Research and Exploration in Space Sciences & Technology II (CRSST II)
UMBC Faculty Collection
UMBC Imaging Research Center (IRC)
UMBC Information Systems Department
UMBC Office for the Vice President of Research & Creative Achievement (ORCA)
Author/Creator
Date
2020-07-10
Type of Work
Department
Program
Citation of Original Publication
Kullman K., Buchanan L., Komlodi A., Engel D. (2020) Mental Model Mapping Method for Cybersecurity. In: Moallem A. (eds) HCI for Cybersecurity, Privacy and Trust. HCII 2020. Lecture Notes in Computer Science, vol 12210. Springer, Cham. https://doi.org/10.1007/978-3-030-50309-3_30
Rights
This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
Access to this item will begin on 07/10/2021
Access to this item will begin on 07/10/2021
Subjects
Abstract
Visualizations can enhance the efficiency of Cyber Defense Analysts, Cyber Defense Incident Responders and Network Operations Specialists (Subject Matter Experts, SME) by providing contextual information for various cybersecurity-related datasets and data sources. We propose that customized, stereoscopic 3D visualizations, aligned with SMEs internalized representations of their data, may enhance their capability to understand the state of their systems in ways that flat displays with either text, 2D or 3D visualizations cannot afford. For these visualizations to be useful and efficient, we need to align these to SMEs internalized understanding of their data. In this paper we propose a method for interviewing SMEs to extract their implicit and explicit understanding of the data that they work with, to create useful, interactive, stereoscopically perceivable visualizations that would assist them with their tasks.