Behavioral-Based Classification and Identification of Ransomware Variants Using Machine Learning
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
Due to the changing behavior of ransomware, traditional classification and detection techniques do not accurately detect new variants of ransomware. Attackers use polymorphic and metamorphic techniques to avoid detection of signature-based systems. We use machine learning classification to identify modified variants of ransomware based on their behavior. To conduct our study, we used behavioral reports of 150 ransomware samples from 10 different ransomware families. Our data-set includes some of the newest ransomware samples available, providing an evaluation of the classification accuracy of machine learning algorithms on the current evolving status of ransomware. An iterative approach is used to identify optimum behavioral attributes used to achieve best classification accuracy. Two main parts of this study are identification of the behavioral attributes which can be used for optimal classification accuracy and classification of ransomware using machine learning algorithms. We have evaluated classification accuracy of three machine learning classification algorithms.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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