---
language: en
license: mit
library_name: scikit-learn
tags:
- malware-detection
- cybersecurity
- machine-learning
- random-forest
- classification
pipeline_tag: tabular-classification
---

# Malware Detection Model (Enhanced Baseline)

## 🧠 Overview
This repository contains a machine learning-based malware detection model designed to classify files as **benign or malicious** based on extracted numerical features.

The current version focuses on validating the **end-to-end pipeline**, including:
- Feature preprocessing
- Model training
- Serialization
- Deployment via Hugging Face Hub

---

## ⚙️ Model Architecture
- Algorithm: **Random Forest Classifier**
- Number of trees: **300**
- Max depth: **15**
- Input features: **50 engineered features**

The model is optimized for:
- Fast inference
- Robustness to noisy inputs
- Scalability for larger datasets

---

## 📊 Features Used
The model operates on numerical features derived from file characteristics, such as:

- File entropy (randomness of bytes)
- File size
- Section count
- Import/export table size
- Byte distribution statistics
- Header metadata patterns

> Note: Current version uses simulated data to validate architecture. Integration with real-world datasets (e.g., EMBER) is planned.

---

## 🧪 Usage

```python
import joblib
import numpy as np

bundle = joblib.load("model.pkl")

model = bundle["model"]
scaler = bundle["scaler"]

sample = np.random.rand(1, 50)
sample_scaled = scaler.transform(sample)

prediction = model.predict(sample_scaled)

print("Malicious" if prediction[0] == 1 else "Benign")