Visualizing Portable Executable Headers for Ransomware Detection: A Deep Learning-Based Approach
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
In recent years, the rapid evolution of ransomware has led to the development of numerous techniques designed to evade traditional malware detection methods. To address this issue, a novel approach is proposed in this study, leveraging machine learning to encode critical information from Portable Executable (PE) headers into visual representations of ransomware samples. The proposed method selects highly impactful features for data sample classification and encodes them as images based on predefined color rules. A deep learning model named peIRCECon (PE Header-Image-based Ransomware Classification Ensemble with Concatenating) is also developed by integrating prominent architectures, such as VGG16 and ResNet50, and incorporating the concatenating method to enhance ransomware detection and classification performance. Experimental results using self-collected datasets demonstrate the efficacy of this approach, achieving high accuracy of 99.85% in distinguishing between ransomware and benign samples. This promising approach holds the potential to significantly improve the effectiveness of ransomware detection and classification, thereby contributing to more robust cybersecurity defense systems. 
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 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