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license: mit
library_name: sklearn
tags:
- malware-detection
- cybersecurity
- random-forest
- pe-files
- security
datasets:
- dev9269/darkweb-slang-dictionary
---
# Malware Detector - Random Forest
A Random Forest classifier for PE (Portable Executable) malware detection, trained on the EMBER dataset.
## Model Details
- **Model Type**: Random Forest Classifier (scikit-learn)
- **Training Data**: EMBER dataset (features from PE file headers, sections, imports, exports, etc.)
- **Framework**: scikit-learn (LightGBM backend)
- **Input**: Extracted PE feature vector (2381 dimensions)
- **Output**: Malicious / Benign classification with confidence score
## Usage
```python
import joblib
import numpy as np
model = joblib.load("model.pkl")
features = np.load("sample_features.npy") # 2381-dim feature vector
prediction = model.predict([features])[0]
confidence = model.predict_proba([features])[0]
print(f"Malicious: {bool(prediction)}")
print(f"Confidence: {max(confidence):.2%}")
```
## Performance
| Metric | Score |
|--------|-------|
| Accuracy | ~96% |
| Precision | ~0.95 |
| Recall | ~0.94 |
| F1 Score | ~0.94 |
## Related Models
- [ember-malware-rf](https://huggingface.co/dev9269/ember-malware-rf) - EMBER-trained variant
- [malware-detector-demo](https://huggingface.co/spaces/dev9269/malware-detector-demo) - Interactive demo Space
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