Cyber Risk Prediction and Management Using Random Forest-Based Risk Scoring Models
Bibliographic record
Abstract
The increasing relevance and number of sophisticated cyber threats are creating the need for the development of intelligent and robust systems for risk prediction and management. The paper introduces an elaborate framework for cyber risk assessment and mitigation through Random Forest risk scoring models. In their research, the authors used a Random Forest classifier to train the model with past cyber incidents' data and system attributes. This approach enabled the model to effectively recognize and predict cyber risks. The system applies risk scores to the various system parts, which greatly simplifies the identification of vulnerabilities. Feature selection is executed to point out the attributes of the most influence on cyber risk and make the model more transparent and efficient. The system also introduces a dynamic risk management module to adapt to changing threat landscapes by keeping the model up to date with new data. Our experimental results demonstrate robust performance with 94.8% accuracy, 92.3% precision, and 93.7% recall, proving the system's effectiveness and reliability in real-world cybersecurity environments. The paper presents as follows: one of the priority points is the inclusion in the machine-learning-based scoring method for the quantification of cyber risks, a feature-optimized Random Forest classifier for risk prediction and a feedback-based mechanism of risk model updating. This framework grants organizations a mechanism for the early detection of risks, cybersecurity decision-making, and finally, the safety of the data.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".