The Development of Cybersecurity Information Sharing Framework for National Critical Information Infrastructure in Indonesia
Bibliographic record
Abstract
The increase of cyber attacks in the Critical Information Infrastructure (CII) requires every organization to collaborate through Cybersecurity Information Sharing (CIS). To support the implementation of the CIS, governance support is needed in the form of a framework that can be used as a reference. This study focuses on developing a CIS framework for the CII sector in Indonesia which consists of three main outputs, namely the proposed ecosystem, the proposed framework and the recommendations for the implementation of the framework. The proposed framework is based on standards including ISO/IEC 27032, NIST SP 800-150 and ENISA ISAC in a Box, based on best practices for implementing CIS and best practices for implementation in other countries including the United States, Australia, United Kingdom, Singapore and Canada. To validate, the expert judgment method was used to obtain suggestions for improvement. The expert judgment method was also carried out quantitatively to measure interrater reliability between experts using Fleiss Kappa Statistics. The measurement results show a kappa value of 0.938, which means that the proposed framework implementation recommendation gets an agreement from the experts at the almost perfect agreement level.
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 | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".