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T3-CIDERS is a train-the-trainer program to increase the adoption of advanced cyberinfrastructure (CI) and data skills into the fabric of research and education in cybersecurity and cyber-related disciplines. T3-CIDERS trains faculty, researchers, and students as “future trainers” (FTs) with hands-on technical and instructional skills to enable more people to effectively leverage CI in cybersecurity. The program includes a series of technical pre-training modules, a weeklong summer institute, ongoing learning engagements conducted over an academic year; it culminates with the FTs conducting locally tailored CI-infused training events at their respective home institutions. Ultimately, T3-CIDERS aims to build a “CI+cybersecurity” community of practice as the cohort continues to practice and teach CI skills in their teaching and research activities. This paper describes the vision and implementation of T3-CIDERS with the first cohort starting in year 2024. Based on the lessons learned through the in-person cohorts, a fully online program will be developed to expand the reach of T3-CIDERS to a broader audience. T3-CIDERS responds to the need to close the CI and data skill gap to meet the increasing challenges in securing the digital world.
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DeapSECURE: Empowering Students for Data- and Compute-Intensive Research in Cybersecurity through Training
As the volume and sophistication of cyber-attacks grow, cybersecurity researchers, engineers and practitioners rely on advanced cyberinfrastructure (CI) techniques like big data and machine learning, as well as advanced CI platforms, e.g., cloud and high-performance computing (HPC) to assess cyber risks, identify and mitigate threats, and achieve defense in depth. There is a training gap where current cybersecurity curricula at many universities do not introduce advanced CI techniques to future cybersecurity workforce. At Old Dominion University (ODU), we are bridging this gap through an innovative training program named DeapSECURE (Data-Enabled Advanced Training Program for Cyber Security Research and Education). We developed six non-degree training modules to expose cybersecurity students to advanced CI platforms and techniques rooted in big data, machine learning, neural networks, and high-performance programming. Each workshop includes a lecture providing the motivation and context for a CI technique, which is then examined during a hands-on session. The modules are delivered through (1) monthly workshops for ODU students, and (2) summer institutes for students from other universities and Research Experiences for Undergraduates participants. Future plan for the training program includes an online continuous learning community as an extension to the workshops, and all learning materials available as open educational resources, which will facilitate widespread adoption, adaptations, and contributions. The project leverages existing partnerships to ensure broad participation and adoption of advanced CI techniques in the cybersecurity community. We employ a rigorous evaluation plan rooted in diverse metrics of success to improve the curriculum and demonstrate its effectiveness.
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- Award ID(s):
- 1829771
- PAR ID:
- 10111062
- Date Published:
- Journal Name:
- Proceedings of ACM Practical Experience in Advanced Research Computing Conference (PEARC’19)
- Page Range / eLocation ID:
- 1 to 8
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3332186.3332247
