An exploration of the current state of information assurance education
Bibliographic record
Abstract
Information Assurance and computer security are serious worldwide concerns of governments, industry, and academia. Computer security is one of the three new focal areas of the ACM/IEEE's Computer Science Curriculum update in 2008. This ACM/IEEE report describes, as the first of its three recent trends, "the emergence of security as a major area of concern." The importance of Information Assurance and Information Assurance education is not limited to the United States. Other nations, including the United Kingdom, Australia, New Zealand, Canada, and other members from NATO countries and the EU, have inquired as to how they may be able to establish Information Assurance education programs in their own country. The goal of this document is to explore the space of various existing Information Assurance educational standards and guidelines, and how they may serve as a basis for helping to define the field of Information Assurance. It was necessary for this working group to study what has been done for other areas of computing. For example, computer science (CS 2008 and associate-degree CS 2009), information technology (IT 2008), and software engineering (SE 2004), all have available curricular guidelines. In its exploration of existing government, industry, and academic Information Assurance guidelines and standards, as well as in its discovery of what guidance is being provided for other areas of computing, the working group has developed this paper as a foundation, or a starting point, for creating an appropriate set of guidelines for Information Assurance education. In researching the space of existing guidelines and standards, several challenges and opportunities to Information Assurance education were discovered. These are briefly described and discussed, and some next steps suggested.
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 | 0.034 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.019 | 0.028 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".