EDURange: hands-on cybersecurity exercises in the cloud
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Incorporating cybersecurity into the undergraduate curriculum is a topic of continuing interest. This topic has been included as a core knowledge area in the ACM/IEEE CS2013 Curricula Guidelines [1]. The goal of this workshop is to provide faculty who may only have little experience in cybersecurity with a framework and some of our most recent scenarios that would facilitate incorporating this topic into the Computer Science curriculum. Building on previous similar workshops [2, 3], this tutorial focuses on the design of two hands-on exercises that we have developed on Amazon's AWS cloud environment. The first exercise is a network reconnaissance exercise, and the second is about detecting malicious binaries (Elf infection). Participants will learn how to use the EDURange framework. Taking on the role of player, participants will learn about network security and malware. As the instructor, they will learn how to use these security exercises in the classroom, and how to create scenarios that they can use 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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it