The literature intends to investigate why cybersecurity data visualizations are so powerful and how data variables may be used to improve future visualizations in cybersecurity. Surveys are used to collect the raw data from the selected participants, who are experienced cybersecurity experts, especially those working in their respective firms’ cybersecurity data visualization departments. Statistical analyses (Logistic Regression) test the relationship between the settled independent and dependent variables. Selecting participants randomly reduces biases regarding one’s viewpoint or set of experiences, thus increasing the accuracy of collected data. In summary, the appraisal potentially expands visual analytics approaches for better cyber threat identification and management. With improved cyber security, optimizing data visualization procedures is enlightened in the information technological arena. Organizations may improve their cyber defense capabilities by understanding the aspects contributing to good cybersecurity data visualizations.
Andrade, R. O., & Yoo, S. G. (2019). Cognitive security: A comprehensive study of cognitive science in cybersecurity. Journal of Information Security and Applications, 48, 102352.
Augusto-Gonzalez, J., Collen, A., Evangelatos, S., Anagnostopoulos, M., Sp
Sampling Strategy and Population
The quantitative research approach is adopted to determine what characteristics affect the efficacy of cybersecurity data visualizations. The data is gathered from at least a hundred cybersecurity specialists working on cybersecurity data visualization in their respective organizations. The study will use online surveys designed using the Microsoft Online Forms platform (https://forms.office.com/) to collect top-quality and reliable first-hand raw data for the research. The surveys are designed to allow the respondents to categorize their organization in terms of success in VIzSec adoption (Successful or Unsuccessful).
The survey questions are derived from established instruments measuring TOE factors in prior technology adoption studies. Some modifications were made to tailor the items specifically to VizSec adoption. The leverages validated measures while adapting them to the current research context. The respondent will also rate how various variables under the TOE Framework affect an organization’s successful adoption of VizSec. Therefore, online surveys will be used by engaging at least a hundred cybersecurity experts working in various organizations’ cybersecurity data visualization sections to obtain the raw data for literature analysis.
Variable Definition
The research hypotheses guided the selection of study variables for the VizSec case. The research was designed to assess the impact of various Technology-Organization-Environment Framework components on the successful adoption of VizSec in an organization. Therefore, these TOE Framework factors will act as independent variables in the technological exploration, while the successful adoption of VizSec is its dependent variable. The table below lists the independent variables and their corresponding TOE Framework element. In conclusion, the paper’s variables were derived from the research hypotheses; whereas only one dependent variable (VS) exists, nine independent variables are obtained from components under each TOE Framework element.
Table 1: Study Variables
| # | Variable | TOE Element | Coding |
| 1 | VizSec Adoption Success | 1: Unsuccessful
2: Successful |
|
| 2 | Compatibility | Technology | 1: Incompatible
2: Compatible |
| 3 | Scalability | 1-10 Likert Scale | |
| 4 | Functionality | 1-10 Likert Scale | |
| 5 | Culture and Structure Complexity | Organization | 1: Complex
2: Simple |
| 6 | Stakeholder Awareness |