Assessing Cybersecurity Training
July 2019 • Podcast
April Galyardt, a machine learning research scientist, discusses efforts to develop a new approach to assessing the skills of the cybersecurity workforce.
“One of the biggest things is that it should help us speed up and streamline training. If we can give better feedback and faster feedback… to the trainees, they can learn a little faster. They can get up and running. One of the hardest things about training people is that contextualization, knowing when to do this and when to do that.”
Simulation environments allow people to practice skills such as setting up and defending networks. If we can record informative traces of activity in these online environments and draw accurate inferences about trainee capabilities, then we can provide evidence-based guidance on performance, assess mission readiness, optimize training schedules, and refine training modules. April Galyardt, a machine learning research scientist, discusses efforts to develop a new approach to assessing the skills of the cybersecurity workforce.
About the Speaker
Dr. April Galyardt is a statistician specializing in machine learning and Bayesian model building. Dr. Galyardt has applied machine learning to problems in cybersecurity, educational data mining, and cognitive science. Before joining the SEI, Dr. Galyardt was a professor at the University of Georgia and designed and taught the first statistical machine learning course offered by the university.