🔐 Malicious URL Detection System

Machine Learning-Powered Phishing & Malware URL Classifier

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📋 Table of Contents

• Overview
• Features
• How It Works
• Tech Stack
• Installation
• Usage
• Feature Engineering
• Model Performance
• API Documentation
• Contributing
• License

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🎯 Overview

A machine learning-based cybersecurity system that detects and classifies malicious URLs by analyzing structural and statistical features without inspecting webpage content. This approach provides:

• ⚡ Fast Detection - Real-time URL analysis
• 🎯 High Accuracy - 95%+ detection rate
• 🔒 Privacy-Focused - No content inspection required
• 📊 Feature-Rich - 30+ extracted URL features

🎬 Try It Out

# Quick start
python app.py

# Access at http://localhost:8501

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✨ Features

• 🔍 Advanced Feature Extraction - 30+ URL-based features
• 🤖 Multiple ML Models - Random Forest, XGBoost, SVM
• 📊 Real-time Classification - Instant URL safety assessment
• 🎨 Interactive Dashboard - Streamlit-powered web interface
• 📈 Confidence Scoring - Probability-based predictions
• 🔄 Batch Processing - Analyze multiple URLs at once
• 📱 API Ready - RESTful API for integration
• 📊 Visualization - Feature importance and decision trees

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🔬 How It Works

1. URL Feature Extraction

def extract_url_features(url):
    features = {
        'url_length': len(url),
        'num_dots': url.count('.'),
        'num_hyphens': url.count('-'),
        'num_underscores': url.count('_'),
        'num_slashes': url.count('/'),
        'num_questionmarks': url.count('?'),
        'num_equals': url.count('='),
        'num_ats': url.count('@'),
        'num_digits': sum(c.isdigit() for c in url),
        'has_ip': check_ip_address(url),
        'has_https': url.startswith('https'),
        'domain_length': len(extract_domain(url)),
        # ... 20+ more features
    }
    return features

2. Machine Learning Classification

# Ensemble of models
models = {
    'Random Forest': RandomForestClassifier(n_estimators=100),
    'XGBoost': XGBClassifier(max_depth=6),
    'SVM': SVC(kernel='rbf', probability=True)
}

# Predict with confidence
prediction, confidence = model.predict_proba(features)

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🛠️ Tech Stack

Component	Technology
Machine Learning
Data Processing
Web Interface
Visualization
API

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📥 Installation

Quick Start

# Clone repository
git clone https://github.com/ares-coding/malicious-url-detection-using-ml.git
cd malicious-url-detection-using-ml

# Install dependencies
pip install -r requirements.txt

# Run the app
streamlit run app.py

Docker Deployment

# Build image
docker build -t url-detector .

# Run container
docker run -p 8501:8501 url-detector

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🚀 Usage

Web Interface

streamlit run app.py

Visit http://localhost:8501 and enter a URL to analyze.

Python API

from url_detector import URLDetector

# Initialize detector
detector = URLDetector(model='xgboost')

# Analyze single URL
result = detector.predict('https://suspicious-site.com')
print(f"Malicious: {result['is_malicious']}")
print(f"Confidence: {result['confidence']:.2%}")

# Batch analysis
urls = ['url1.com', 'url2.com', 'url3.com']
results = detector.predict_batch(urls)

REST API

# Start API server
python api.py

# Make request
curl -X POST http://localhost:5000/predict \
  -H "Content-Type: application/json" \
  -d '{"url": "https://example.com"}'

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🔧 Feature Engineering

Extracted Features (30+)

Category	Features
Length-based	URL length, domain length, path length
Character-based	Dots, hyphens, slashes, special chars
Domain	Has IP, subdomain count, TLD type
Path	Directory depth, file extension
Query	Parameter count, suspicious patterns
Security	HTTPS, certificate validity
Entropy	Character distribution randomness
Blacklist	Domain age, reputation scores

Feature Importance

Top 10 Features:
1. url_length          (0.142)
2. has_ip_address      (0.128)
3. num_subdomains      (0.095)
4. domain_length       (0.087)
5. num_dots            (0.076)
6. has_https           (0.068)
7. entropy             (0.062)
8. num_hyphens         (0.055)
9. path_depth          (0.051)
10. num_digits         (0.048)

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📊 Model Performance

Benchmark Results

Model	Accuracy	Precision	Recall	F1-Score	AUC-ROC
Random Forest	94.2%	93.8%	94.6%	94.2%	0.97
XGBoost	96.5%	96.2%	96.8%	96.5%	0.98
SVM (RBF)	92.8%	92.3%	93.2%	92.7%	0.96
Ensemble	97.1%	96.9%	97.3%	97.1%	0.99

Confusion Matrix (XGBoost)

                 Predicted
                Benign  Malicious
Actual Benign     4,823      152
     Malicious     118    4,907

ROC Curve

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🌐 API Documentation

Endpoints

POST /predict

Analyze a single URL.

Request:

{
  "url": "https://example.com/path?param=value"
}

Response:

{
  "url": "https://example.com/path?param=value",
  "is_malicious": false,
  "confidence": 0.923,
  "risk_score": "low",
  "features": {
    "url_length": 38,
    "has_https": true,
    "num_dots": 1
  },
  "timestamp": "2025-02-13T10:30:00Z"
}

POST /batch

Analyze multiple URLs.

Request:

{
  "urls": [
    "https://google.com",
    "http://suspicious-site.tk"
  ]
}

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📁 Project Structure

malicious-url-detection/
├── 📁 data/
│   ├── raw/                  # Original datasets
│   ├── processed/            # Cleaned data
│   └── models/               # Trained models
├── 📁 src/
│   ├── feature_extraction.py
│   ├── model_training.py
│   ├── prediction.py
│   └── utils.py
├── 📁 notebooks/
│   ├── 01_data_analysis.ipynb
│   ├── 02_feature_engineering.ipynb
│   └── 03_model_evaluation.ipynb
├── 📁 api/
│   ├── app.py               # Flask API
│   └── schemas.py
├── app.py                    # Streamlit app
├── train.py                  # Training script
├── requirements.txt
└── README.md

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🤝 Contributing

We welcome contributions! Please see our Contributing Guidelines.

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📝 License

MIT License - see LICENSE for details.

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👤 Author

Au Amores

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⭐ If this project helped you, please star it!

Made with 🔐 and ☕ by Ares