---
license: apache-2.0
language:
- en
tags:
- cybersecurity
- intrusion-detection
- phishing-detection
- vulnerability-scoring
- machine-learning
- random-forest
- xgboost
---

# 🛡️ Cybersecurity ML Models

A collection of machine learning models for cybersecurity threat detection and vulnerability assessment.

## Models Included

### 🔍 Intrusion Detection (UNSW-NB15)
Detects network intrusions and classifies attack types.
- **Algorithm:** Random Forest + XGBoost
- **Classes:** Normal, DoS, Exploits, Fuzzers, Generic, Reconnaissance, Backdoor, Analysis, Shellcode, Worms
- **Dataset:** UNSW-NB15

### 🎣 Phishing Detection
Identifies phishing URLs and malicious web content.
- **Algorithm:** Random Forest + XGBoost
- **Output:** Phishing / Legitimate

### 🛡️ Vulnerability Scoring
Predicts vulnerability severity and CVSS scores.
- **Algorithm:** Random Forest + XGBoost (classifier + regressor)
- **Output:** Severity label + numeric score

---

## 📁 Repository Structure
```
├── predictor.pkl                          # Main predictor (load this)
├── preprocessors/
│   ├── intrusion_label_encoder.pkl
│   ├── intrusion_scaler.pkl
│   ├── phishing_scaler.pkl
│   ├── severity_encoder.pkl
│   ├── vulnerability_scaler.pkl
│   ├── intrusion_feature_names.json
│   ├── phishing_feature_names.json
│   └── vulnerability_feature_names.json
└── saved_models/
    ├── intrusion_detection/
    ├── phishing_detection/
    └── vulnerability_scoring/
```

---

## 🚀 Quickstart

### Install dependencies
```bash
pip install scikit-learn xgboost joblib huggingface_hub
```

### Load the models
```python
import joblib
from huggingface_hub import hf_hub_download

# Download and load main predictor
path = hf_hub_download(repo_id="Alfeesi/cybersecurity-ml-models", filename="predictor.pkl")
predictor = joblib.load(path)
```

### Intrusion Detection
```python
import numpy as np

features = np.array([[0.5, 0, 1, 1024, 512]])  # your network features
result = predictor.predict_intrusion(features)
print(f"Attack Type: {result['attack_type']}")
```

### Phishing Detection
```python
features = np.array([[75, 1, 3, 0]])  # url_length, has_ip, num_dots, has_https
result = predictor.predict_phishing(features)
print(f"Phishing: {result}")
```

### Vulnerability Scoring
```python
features = np.array([[7.5, 0, 0]])  # cvss_base, attack_vector, complexity
result = predictor.predict_vulnerability(features)
print(f"Severity: {result['severity']} | Score: {result['score']}")
```

---

## 📊 Model Performance

| Model | Metric | Score |
|-------|--------|-------|
| Intrusion Detection | Accuracy | See evaluation/ |
| Phishing Detection | Accuracy | See evaluation/ |
| Vulnerability Scoring | RMSE | See evaluation/ |

---

## 🔗 Live Demo
https://huggingface.co/spaces/Alfeesi/cybersecurity-demo

---

## 📄 License
Apache 2.0 — free to use, modify, and distribute.