Towards a Comprehensive Cybersecurity Information Sharing Framework
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it