DeapSECURE Computational Training for Cybersecurity: Progress Toward Widespread Community Adoption

Wirawan Purwanto, Bahador Dodge, Karina Arcaute, Masha Sosonkina, and Hongyi Wu

Volume 15, Issue 1 (March 2024), pp. 2–9

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  author={Wirawan Purwanto and Bahador Dodge and Karina Arcaute and Masha Sosonkina and Hongyi Wu},
  title={DeapSECURE Computational Training for Cybersecurity: Progress Toward Widespread Community Adoption},
  journal={The Journal of Computational Science Education},
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The Data-Enabled Advanced Computational Training Program for Cybersecurity Research and Education (DeapSECURE) is a nondegree training consisting of six modules covering a broad range of cyberinfrastructure techniques, including high performance computing, big data, machine learning and advanced cryptography, aimed at reducing the gap between current cybersecurity curricula and requirements needed for advanced research and industrial projects. Since 2020, these lesson modules have been updated and retooled to suit fully-online delivery. Hands-on activities were reformatted to accommodate self-paced learning. In this paper, we summarize the four years of the project comparing in-person and online only instruction methods as well as outlining lessons learned. The module content and hands-on materials are being released as open-source educational resources. We also indicate our future direction to scale up and increase adoption of the DeapSECURE training program to benefit cybersecurity research everywhere.