CyberTraining:CIC: DeapSECURE: A Data-Enabled Advanced Training Program for Cyber Security Research and Education

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As the volume and sophistication of cyber-attacks grow, cybersecurity researchers, engineers and practitioners heavily rely on advanced cyberinfrastructure (CI) techniques such as big data, machine learning, and parallel programming, as well as advanced CI platforms, e.g., cloud and high-performance computing to assess cyber risks, identify and mitigate threats, and achieve defense in depth. However, advanced CI techniques have not been widely introduced in undergraduate and graduate cybersecurity curricula. This lack creates a hurdle for many senior undergraduates and early-stage graduate cybersecurity students who are keen to conduct cutting-edge cybersecurity research and/or participate in advanced industrial cybersecurity projects. This project introduces a unique Data-Enabled Advanced Training Program for Cyber Security Research and Education (DeapSECURE), aimed to prepare undergraduate and graduate students with advanced CI techniques and teach them to use CI resources, tools, and services to succeed in cutting-edge cybersecurity research and industrial cybersecurity projects. The project responds to the urgent need for well-prepared cybersecurity workforce in the Hampton Roads metropolitan region, the Commonwealth of Virginia, and the Nation. It, thus, serves the national interest, as stated by NSF's mission: to promote the progress of science; to advance the national health, prosperity and welfare; or to secure the national defense.This project develops six new CI training modules which emphasize the practical use of the advanced CI techniques, especially the tools that implement them, in the context of cybersecurity research. Each training module includes three sections: (1) an overview presented by an invited cybersecurity faculty about his/her research, concluding with a research problem that heavily depends on CI techniques; (2) an introduction of corresponding CI skills, tools and platforms; (3) a hands-on lab session where students will apply the CI techniques to solve the research problem formerly introduced by the cybersecurity faculty. The modules will be delivered via two distinct means: monthly workshops and summer institutes. Six monthly workshops are conducted during academic year, primarily targeting students enrolled at Old Dominion University (ODU). The summer institutes present these six modules to students from local community colleges, Research Experiences for Undergraduates program at ODU, and other Virginia universities; they also include special activities such as field trips, open house for K-12 students, Cyber Night events, cybersecurity career panels, and student competitions. Complementing the workshops and summer institutes, an online continuous learning community is created, which includes a virtual computer lab and a student forum, as a place for students to continue their learning engagement after the face-to-face sessions. Archived workshop materials, as well as additional learning materials are also posted on this online platform as open educational resources, to be made available to the cybersecurity research and education communities. The open-source style development of the learning modules facilitates a wide-range of adoption, adaptations, and contributions in an efficient manner. The project leverages existing and new partnerships to ensure broad participation, and accordingly broaden the adoption of advanced CI techniques in the cybersecurity community. The project employs a rigorous assessment and evaluation plan rooted in diverse metrics of success to improve the curricula and demonstrate its effectiveness. The metrics, which are based on the students' outcomes and exit surveys, are assessed by an independent evaluator. The adoption of the learning modules outside of the training program is also considered as a metric of success.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.