Add model file + comprehensive model card

README.md

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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

model.pkl

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+oid sha256:f16366728343c7c6fb09fc89e34ab6fa9a19cf0ab740cfdd3f767649cde86f8a
+size 17082089