Ransomware Detection Using Aggregated Random Forest Technique with Recent Variants
Bibliographic record
Abstract
The increasing sophistication and frequency of ransomware attacks have posed significant challenges to existing cybersecurity measures, highlighting the need for more effective detection techniques. A novel approach is presented that leverages an aggregated random forest technique to enhance the accuracy and robustness of ransomware detection. Through the integration of multiple random forests, the proposed method demonstrates superior performance in detecting a wide array of ransomware variants, including those utilizing advanced evasion tactics. The methodology includes comprehensive data collection, feature extraction, and rigorous evaluation, yielding high detection accuracy while maintaining low false positive and negative rates. Comparative analysis with other machine learning techniques, such as Support Vector Machines and Neural Networks, further underscores the efficacy of the aggregated random forest model, which also excels in detection speed and resource utilization. The implications for cybersecurity are profound, offering a scalable and efficient solution for real-time ransomware detection, thereby contributing to the resilience of security infrastructures across various sectors. Future work could explore the model's adaptability to new ransomware variants, integration with other machine learning techniques, and broader applicability to different types of malware.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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 | 0.001 |
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".