Situational States Influence on Team Workload Demands in Cyber Defense Exercise

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Abstract

Cyber operations are increasingly automated processes that can occur at computational speed with the intent of reducing, or denying time for good decision making or time to ground communication between human agents. There is a lack of performance measures and metrics in cyber operation settings. One potential setting describing human performance could be emotional stability under stress. Measures of higher individual affective variability indicate more emotional adaptability and allows for measuring individuals as dynamic systems. Previous research in other security-sensitive high-stake situations has shown that individuals with less emotional adaptability display maladaptive behaviors while individuals with more emotional adaptability can adapt more efficiently to changing situations, show more confidence in their own abilities and skills, and display better performance. We hypothesized that measurements of affective variability during a cyber defense exercise will be associated with team workload demands. Data was collected from 13 cadets during the Norwegian Defence Cyber Academy’s annual Cyber Defense Exercise. Three indicators of individual affective variability were measured daily with the Self-Assessment Manikin and compared to scores on the Team Workload Questionnaire. We found that affective variability was negatively associated with team workload demands. Participants with higher affective variability, as measured by the Self-Assessment Manikin, will impose less workload demands on the team, which can lead to better outcomes. This is the first study to assess how individual emotional adaptability affects team dynamics in a cyber defense setting. Future research should include variable measurements as they may have better explanatory power for performance measurements.

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APA

Ask, T. F., Sütterlin, S., Knox, B. J., & Lugo, R. G. (2021). Situational States Influence on Team Workload Demands in Cyber Defense Exercise. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 13096 LNCS, pp. 3–20). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-90328-2_1

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