Detecting Ransomware Before It Bites: A Hybrid Model Approach for Early Ransomware Detection
Bibliographic record
Abstract
Ransomware attacks are a growing threat to organizations worldwide, with sensitive data encrypted and held hostage for ransom. Although traditional ransomware detection methods, such as signature-based and heuristic detection, are effective to some extent, they struggle to identify ransomware before encryption begins due to evasion techniques like code obfuscation and polymorphism. This work aims to address this gap by developing a detection method that can identify ransomware early by analyzing static and dynamic features. The system tests ransomware samples using static analysis of Portable Executable (PE) files without execution and dynamic analysis in a sandbox environment to capture pre-encryption behaviors and features. A set of state-of-the-art machine learning algorithms is employed to classify ransomware activity based on these behavioral patterns. The goal is to identify ransomware activity before it executes encryption. By establishing a framework for early ransomware detection, this study provides a pathway for scalable detection systems that can adapt to evolving ransomware threats.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".