T3-CIDERS: Train-the-Trainer and Community Building to Increase Cyberinfrastructure Adoption in Cybersecurity Research and Education
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.
Authors
- Shanan Chappell Moots (ORCID: https://orcid.org/0000-0001-6540-7985)
- Mohan Yang (ORCID: https://orcid.org/0000-0003-0856-0814)
- Peng Jiang (ORCID: https://orcid.org/0000-0002-2178-6888)
- Wirawan Purwanto (ORCID: https://orcid.org/0000-0002-2124-4552)
- Masha Sosonkina (ORCID: https://orcid.org/0009-0005-0223-397X)
- Hongyi Wu (ORCID: https://orcid.org/0000-0002-6112-9313)
Institutions
- University of Nebraska at Omaha (US)
- University of Arizona (US)
- Old Dominion University (US)
- Texas A&M University (US)
Publication Details
- Published
- 2025-07-18
- DOI
- https://doi.org/10.1145/3708035.3736021
- Primary Topic
- Network Security and Intrusion Detection
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Science Foundation