An Adaptive Wideband Delphi Method to Study State Cyber-Defence Requirements
Bibliographic record
Abstract
Background — Edward Snowden’s revelations of the extensive global communications surveillance activities of foreign intelligence services have led countries such as Indonesia to take concrete steps to enhance protective information security for classified data and communications.Objective — This paper develops the Wideband Delphi Method to study the Indonesian Government’s requirements for cyber-defence in response to reported secret intelligence collection by the Australian Signals Directorate (ASD). It provides a clearer understanding of the issues that influence Indonesian policymakers’ views on the mitigation of foreign surveillance.Method — We developed and conducted an Adaptive Wideband Delphi Study with senior Indonesian officials, with group discussions and individual sessions to explore how to mitigate the surveillance activities of the Five Eyes (the US-UK-Canada-Australia-New Zealand) intelligence alliance. We used the US National Security Agency (NSA) framework of the three elements of Defence in Depth (people, operations, and technology), in combination with governance and legal remedies, as an analytical framework. Results — We identified twenty-five mitigation controls to deal with the priority concerns of policymakers, which were divided into the five defences in depth elements.Conclusion — We discuss the key requirements for protection against foreign surveillance to be taken into account in state cyber-defence frameworks, and suggest effective mitigation controls for safeguarding and protecting states’ national interests.
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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 | 0.049 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".