Teaching cybersecurity through games: a cloud-based approach
Bibliographic record
Abstract
Incorporating information security into the undergraduate curriculum seems to be a topic of growing interest to CCSC-NW attendees. In addition, it is receiving increased attention nationally in the proposed ACM/IEEE CS2013 Curricula Guidelines [1]. This area will be one of the new core requirements. The goal of this workshop is to provide faculty who have little experience in this area with some of our most recent tools and resources that would facilitate their incorporating this knowledge area into their curriculum. It builds on previous similar workshops in this area. In this tutorial, we will describe the use of cloud-based environments for developing and disseminating hands-on security exercises. We present one security game that we have developed on Amazon's AWS cloud environment and an exercise that was developed on The RAVE. Participants will learn about the framework we have developed for providing instructors with competitive, interactive exercises through the system EDURange[2]. EDURange is a new framework for creating exercises and games in a variety of environments including remotely hosted web services, i.e. cloud computing. They will learn about these exercises from two viewpoints. As players, they will learn about network security. As instructors, they will learn how to use these security exercises in the classroom, and they will learn about the scenario description language, which they can use to create games for their classes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".