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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. |