An Improved Stochastic Model for Cybersecurity Risk Assessment
Bibliographic record
Abstract
Most of the existing solutions in cybersecurity analysis has been centered on identifying threats and vulnerabilities, and also providing suitable defense mechanisms to improve the robustness of the cyberspace network. These solutions lack effective capabilities to countermeasure the effect of risks and perform long-term prediction. In this paper, an improved risk assessment model for cyberspace security that will effectively predict and mitigate the consequences of risk was developed. Real-time vulnerabilities of a selected network were scanned and analysed and the ease of vulnerability exploitability was assessed. A Risk Assessment Model was formulated using the synergy of Absorbing Markov Chain and Markov Reward Model. The model was utilized to analyse cybersecurity state of the selected network. The proposed model was simulated using R- Statistical Package, and its performance was evaluated by benchmarking with an existing model, using Reliability and Availability as metrics. The result showed that the proposed model has higher reliability and availability over the existing model. This implied that there is a significant improvement in the assessment of security situations in a cyberspace network.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".