Remote Technical Labs: An Innovative and Scalable Component for University Cybersecurity Program Admission

In response to the existing and predicted skills gap in cybersecurity, educational institutions establish an increased number of studies. Admission boards need to screen large numbers of applicants to identify those with the highest probability of successful completion. To address the current lack of scalable and validated admission procedures with predictive value, we present a validation of an innovative university admission process for a master level program including technical skills assessment via cloud-based virtual labs. A regression model based on data collected during admission assessment procedures is applied to predict later study performance in technical courses. The virtual labs assessing technical skills but also interview component had comparably high predictive values for study performance, indicating a complementary relationship of two distinct skill-sets. The primary conclusion of this research is that cybersecurity technical labs can be used to significantly improve the predictive value of traditional interview-based admission processes for the candidates' later success in technical courses.