Using Machine Learning to Provide Students with Rapid Feedback during Hands-on Cybersecurity Exercises

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This project aims to serve the national interest by using machine learning to provide students with timely feedback while completing hands-on cybersecurity exercises. A cyber informed citizenry is a vital part of our national defense strategy. Cybercrime is becoming increasingly sophisticated, and the systems and devices that need security are becoming ever more complex and interconnected. These issues highlight the need to develop programs that enable students to quickly obtain the fundamental skills and knowledge considered essential by cybersecurity experts. Hands-on cybersecurity exercises are known to provide students with basic cybersecurity knowledge, skills, and abilities. To be effective these exercises need to provide students with rapid feedback to prevent them from getting stuck and frustrated. The goal of this project is to use machine learning to monitor students as they work through hands-on cybersecurity exercises and automatically identify when they are getting stuck and frustrated. The students will then be given suggestions to help them to successfully complete the exercise. This project plans to use reinforcement learning to create, test, and deploy a semi-automated rapid hint system. The project intends to develop tools to collect hints directly from student-teacher interactions, which will then be used to teach the system which hints to apply and when. The system will interact with both the teacher and the student by suggesting hints as the system becomes more proficient. The hint system will be integrated into the EDURange platform but will be compatible with other cyberrange platforms such as DeterLab and KYPO. The PI team intends to offer workshops for faculty on how to use EDURange and the hint system. The hint system will collect data that will be analyzed to determine the efficacy of the tool, and to develop new hints and strategies for helping students. The NSF IUSE: EHR Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.