Toward a Cybersecurity Policy Model: Israel National Cyber Bureau Case Study
Bibliographic record
Abstract
Recent revelations about the United States National Security Agency's ("NSA") clandestine electronic surveillance projects raised a public debate worldwide concerning the legality of government non-compliance with democratic principles. 1 From a national perspective, developing a comprehensive cybersecurity policy is challenging for two reasons.First, cybersecurity is largely shrouded with secrecy and over-classification.Second, the traditional major stakeholders in the field are national defense and intelligence agencies.This excessive secrecy within the newly established Israeli National Cyber Bureau and elsewhere is already burdensome in current policy initiatives.2 Not surprisingly, the original attempts to regulate cybersecurity for the private sector started with, and are still predominantly restricted to, technological standard setting and governmental-industry cooperation.To date, four such private sector endeavors are prevalent.These include the highly popular International Organization for Standardization's ("ISO") ISO 27001, 3 and ISO 27002 4 -two cybersecurity standards offering 1 A key example is the PRISM project.PRISM gathers Internet communications derived from demands made to Internet companies such as Yahoo! Inc.It does so under Section 702 of the FISA Amendments Act of 2008 in order to yield any data that counters court-approved search terms.See
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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".