Towards a Comprehensive Cybersecurity Information Sharing Framework
Bibliographic record
Abstract
In today's digital age, cybersecurity has become a critical concern for nations around the world. With South Africa facing a significant cybersecurity challenge, ranking as the most targeted country on the African continent. The number and sophistication of cyber-attacks such as ransomware attacks, data breaches, phishing and pharming attacks have been steadily rising in recent years with the public sector and financial institutions being highly prone to these attacks. As cyber threats grow in sophistication and frequency, the need for robust defences and proactive measures is of high importance. Information sharing helps organizations and governments to analyse and understand existing cyber-attack trends and use the intelligence gathered to prevent future cyber-attacks, this helps to improve their overall security posture. It is evident from several scholars that organizations that share cybersecurity information have a high probability of reducing cyber-attacks within their environments. Most scholars agrees that, generally, information sharing, and collaboration may greatly reduce cybersecurity risk while ensuring resilience. But confusion and controversy remain around the following particulars such as: Who should share information? What should be shared? When should it be shared? What is the quality and utility of what is shared? How should it be shared? Why is it being shared? What can be done with the information? This paper therefore seeks to analyse the existing Cybersecurity information sharing frameworks, highlight the gaps and propose a comprehensive framework. Firstly, the paper formulates metrics that are used to evaluate the various identified frameworks, then compare and contract them. We then formulate a comprehensive information sharing framework building from the identified gaps. The proposed framework will then be adopted and used by various stakeholders, such as cybersecurity organizations, government bodies, and security experts who intend to share cybersecurity information.
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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 | 0.038 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.015 | 0.012 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.021 | 0.037 |
| Open science | 0.006 | 0.015 |
| Research integrity | 0.005 | 0.006 |
| 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".