metadata
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
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 - EMBER-trained variant
- malware-detector-demo - Interactive demo Space