Session: Seventh SC Workshop on Best Practices for HPC Training and Education

DeapSECURE Computational Training for Cybersecurity Student: Improvements, Mid-Stage Evaluation and Lessons Learned

Wednesday, November 11, 2020

10:15 AM – 10:30 AM ET

Channel: Track 6

Author/Presenter(s)

WP

Wirawan Purwanto

Old Dominion University, United States of America
YH

Yuming He

Old Dominion University, United States of America
JO

Jewel Ossom

Old Dominion University, United States of America
QZ

Qiao Zhang

Old Dominion University, United States of America
LZ

Liuwan Zhu

Old Dominion University, United States of America
KA

Karina Arcaute

Old Dominion University, United States of America
MS

Masha Sosonkina

Old Dominion University, United States of America
HW

Hongyi Wu

Old Dominion University, United States of America

DeapSECURE is a non-degree computational training program that provides solid high-performance computing (HPC) and big-data foundations for cybersecurity students. DeapSECURE consists of six modules covering a broad spectrum of topics such as HPC platforms, big-data analytics, machine learning, privacy-preserving methods and parallel programming. In the second year of this program to improve learning experience, we implemented a number of changes, such as grouping modules into two broad categories, the "big-data" and "HPC"; creating a single cybersecurity storyline across the modules and introducing post-workshop (optional) "hackshops". Two major goals of these changes are to effectively engage students to maintain high interest and attendance in such a non-degree program, and to increase knowledge and skill acquisition. To assess the program and in particular the changes made in the second year, we evaluated and compared the execution and outcomes of the training in Year 1 and Year 2. The assessment data shows that the implemented changes have partially achieved our goals, while simultaneously providing indications where we can further improve. The development of a fully on-line training mode is planned for the next year, along with a reproducibility pilot study to broaden the subject domain from cybersecurity to other areas, such as computations with sensitive data.