🛡️ Masked IP Detection Microservice

A real-time Machine Learning–powered microservice for detecting masked, anonymous, and suspicious IP addresses.
Strengthens Web Application Firewall (WAF) security by identifying VPNs, proxies, Tor nodes, and datacenter traffic.

📺 Demo Video

▶️ Watch Live Demo

This demo showcases:

✅ Web UI interaction
✅ Real-time masked vs legitimate IP detection
✅ Risk level scoring & confidence metrics
✅ API usage via Swagger UI

🌟 Key Features

Feature	Description
⚡ Real-time Analysis	<50ms per request with intelligent caching
🧠 ML Ensemble Model	Random Forest + XGBoost
🎯 96%+ Accuracy	Highly accurate masked IP detection
🔍 Multi-type Detection	Tor, VPN, Proxy, Datacenter IPs
🚀 FastAPI Backend	High-performance async REST API
📊 Risk Scoring	LOW → MEDIUM → HIGH → CRITICAL levels
💾 Smart Caching	Redis with automatic in-memory fallback
🖥️ Web Dashboard	Interactive UI for live testing
📈 Explainable AI	Confidence scores and feature importance
🔄 Continuous Learning	Auto-updates from threat intelligence feeds

🏗️ System Architecture

┌─────────────────────────────────────────────────────────┐
│                   Web Application / WAF                  │
└────────────────────┬────────────────────────────────────┘
                     │ HTTP Request
                     ▼
┌─────────────────────────────────────────────────────────┐
│              Masked IP Detection API                     │
│ ─────────────────────────────────────────────────────── │
│                                                          │
│  ┌──────────────┐   ┌──────────────┐   ┌────────────┐  │
│  │ IP Validator │ → │   Feature    │ → │ ML Ensemble│  │
│  │   & Parser   │   │  Extraction  │   │ Prediction │  │
│  └──────────────┘   └──────────────┘   └────────────┘  │
│           │                 │                   │        │
│           └─────────────────┴───────────────────┘        │
│                             │                            │
│                             ▼                            │
│                  ┌──────────────────┐                    │
│                  │  Risk Scoring &  │                    │
│                  │ Response Builder │                    │
│                  └──────────────────┘                    │
│                             │                            │
│  ┌──────────────────────────┴──────────────────────┐    │
│  │          Cache Layer (Redis / In-Memory)        │    │
│  └─────────────────────────────────────────────────┘    │
└─────────────────────────────────────────────────────────┘
                     │ JSON Response
                     ▼
┌─────────────────────────────────────────────────────────┐
│         Client Application / Security Dashboard          │
└─────────────────────────────────────────────────────────┘

🔍 Detection Capabilities

✅ Currently Implemented

✔️ IP Structural Analysis: IPv4/IPv6 validation and feature extraction
✔️ ML-Based Classification: Ensemble model for masked vs legitimate detection
✔️ Probability Scoring: Confidence levels (0-100%)
✔️ Risk Categorization: Four-tier risk assessment
✔️ Batch Processing: Check multiple IPs simultaneously
✔️ REST API: Production-ready endpoints
✔️ Web Dashboard: Live testing interface
✔️ Intelligent Caching: Performance optimization

🚀 Planned Enhancements

🔜 Live Tor Integration: Real-time Tor exit node feed
🔜 ASN-Based Detection: VPN & datacenter identification
🔜 GeoIP Enrichment: MaxMind GeoIP2 integration
🔜 Threat Intelligence: AbuseIPDB & IPQualityScore APIs
🔜 Online Learning: Incremental model retraining
🔜 Behavioral Analysis: Advanced anomaly detection

📊 Dataset Sources

Note: These sources are used during model training and planned for future integration.

🧅 Tor Exit Nodes

🔀 Public Proxy Lists

🌐 ASN & Datacenter Mapping

Regional Registries: RIPE, ARIN, APNIC
Cloud Providers: AWS, GCP, Azure, DigitalOcean
Hosting: OVH, Hetzner, Vultr, Linode

🛡️ Threat Intelligence (Optional)

AbuseIPDB - Malicious IP database
IPQualityScore - Fraud detection
MaxMind GeoLite2 - City + ASN data

🚀 Quick Start

Prerequisites

✅ Python 3.8 or higher
✅ pip package manager
✅ Git
⚠️ Redis (optional, but recommended for production)

1️⃣ Clone Repository

git clone https://github.com/rt1856/masked-ip-detection.git
cd masked-ip-detection

2️⃣ Create Virtual Environment

Windows:

python -m venv venv
venv\Scripts\activate

Linux/Mac:

python3 -m venv venv
source venv/bin/activate

3️⃣ Install Dependencies

pip install -r requirements.txt

4️⃣ Setup Models

⚠️ Important: Model files are not included in the repository due to size.

Option A: Download Pre-trained Models

# Download from project releases or Google Drive
# Place in models/ directory:
models/
├── random_forest_model.pkl
├── xgboost_model.pkl
└── feature_names.pkl

Option B: Train Your Own Models

# Use provided Google Colab notebooks:
# 1. Complete_Data_Collection.ipynb (collect datasets)
# 2. 02_preprocessing.ipynb (feature engineering)
# 3. 03_model_training.ipynb (train models)

5️⃣ Run the Service

uvicorn src.api.main:app --reload --host 0.0.0.0 --port 8000

Output:

INFO:     Uvicorn running on http://0.0.0.0:8000
INFO:     Loaded 3 models successfully
INFO:     Feature count: 18
INFO:     Masked IP Detection API started successfully

🌐 Access Points

Service	URL	Description
🏠 Web Dashboard	http://localhost:8000/	Interactive testing interface
📚 API Documentation	http://localhost:8000/docs	Swagger UI (interactive)
📋 Alternative Docs	http://localhost:8000/redoc	ReDoc (clean layout)
❤️ Health Check	http://localhost:8000/health	Service status
ℹ️ API Info	http://localhost:8000/	Metadata & endpoints

📡 API Usage

🔹 Check Single IP

cURL:

curl -X POST "http://localhost:8000/api/v1/check" \
  -H "Content-Type: application/json" \
  -d '{
    "ip_address": "8.8.8.8",
    "include_details": true
  }'

Response:

{
  "ip_address": "8.8.8.8",
  "is_masked": false,
  "confidence": 0.92,
  "risk_level": "LOW",
  "detected_type": null,
  "details": {
    "ensemble_probability": 0.08,
    "model_predictions": {
      "random_forest": 0,
      "xgboost": 0,
    }
  },
  "timestamp": "2025-01-15T10:30:00"
}

🔹 Batch IP Check

cURL:

curl -X POST "http://localhost:8000/api/v1/batch" \
  -H "Content-Type: application/json" \
  -d '{
    "ip_addresses": ["8.8.8.8", "1.1.1.1", "185.220.101.1"],
    "include_details": false
  }'

Response:

{
  "total_checked": 3,
  "results": [
    {
      "ip_address": "8.8.8.8",
      "is_masked": false,
      "confidence": 0.92,
      "risk_level": "LOW"
    },
    {
      "ip_address": "185.220.101.1",
      "is_masked": true,
      "confidence": 0.95,
      "risk_level": "CRITICAL",
      "detected_type": "tor"
    }
  ],
  "timestamp": "2025-01-15T10:31:00"
}

🔹 Python Client

import requests

def check_ip(ip_address):
    """Check if IP is masked"""
    response = requests.post(
        "http://localhost:8000/api/v1/check",
        json={
            "ip_address": ip_address,
            "include_details": True
        }
    )
    return response.json()

# Example usage
result = check_ip("8.8.8.8")
print(f"IP: {result['ip_address']}")
print(f"Is Masked: {result['is_masked']}")
print(f"Confidence: {result['confidence']:.2%}")
print(f"Risk Level: {result['risk_level']}")

🔹 JavaScript/Node.js Client

async function checkIP(ipAddress) {
  const response = await fetch('http://localhost:8000/api/v1/check', {
    method: 'POST',
    headers: { 'Content-Type': 'application/json' },
    body: JSON.stringify({ 
      ip_address: ipAddress,
      include_details: true 
    })
  });
  return await response.json();
}

// Example usage
checkIP('8.8.8.8').then(result => {
  console.log(`IP: ${result.ip_address}`);
  console.log(`Is Masked: ${result.is_masked}`);
  console.log(`Confidence: ${(result.confidence * 100).toFixed(1)}%`);
  console.log(`Risk Level: ${result.risk_level}`);
});

📈 Model Performance

Model	Accuracy	Precision	Recall	F1-Score	ROC-AUC
Random Forest	94.2%	93.8%	94.5%	94.1%	0.972
XGBoost	95.1%	94.9%	95.3%	95.1%	0.981
Ensemble	96.3%	96.1%	96.5%	96.3%	0.987

Note: Metrics are based on offline evaluation datasets. Real-world performance may vary based on traffic patterns and threat landscape.

Performance Characteristics

⚡ Latency: <50ms per request (with caching: <10ms)
🔄 Throughput: 1000+ requests/second
💾 Memory: ~200MB RAM
📊 False Positive Rate: <3%

🔧 Integration Examples

WAF Middleware (Python/FastAPI)

from fastapi import FastAPI, Request, HTTPException
import httpx

app = FastAPI()

async def check_masked_ip(ip: str) -> dict:
    """Check if IP is masked using the microservice"""
    async with httpx.AsyncClient() as client:
        response = await client.post(
            'http://localhost:8000/api/v1/check',
            json={'ip_address': ip}
        )
        return response.json()

@app.middleware("http")
async def ip_filtering_middleware(request: Request, call_next):
    """Block high-risk masked IPs"""
    client_ip = request.client.host
    
    result = await check_masked_ip(client_ip)
    
    if result['is_masked'] and result['risk_level'] in ['HIGH', 'CRITICAL']:
        raise HTTPException(
            status_code=403,
            detail="Access denied: Suspicious IP detected"
        )
    
    return await call_next(request)

Nginx/ModSecurity Integration

# Custom rule to check IPs
SecRule REQUEST_HEADERS:X-Forwarded-For "@rx ^(.*)$" \
    "id:9001,\
    phase:1,\
    t:none,\
    capture,\
    chain"
    SecRule TX:1 "@external /usr/local/bin/check_masked_ip.sh" \
        "deny,status:403,msg:'Masked IP Detected'"

check_masked_ip.sh:

#!/bin/bash
IP=$1
RESULT=$(curl -s -X POST http://localhost:8000/api/v1/check \
  -H "Content-Type: application/json" \
  -d "{\"ip_address\":\"$IP\"}" | jq -r '.is_masked')

if [ "$RESULT" = "true" ]; then
  exit 1  # Block
else
  exit 0  # Allow
fi

🧪 Testing

Run Test Suite

# Run all tests
pytest tests/

# Run with coverage
pytest tests/ --cov=src --cov-report=html

# Run specific test file
pytest tests/test_api.py -v

Manual Testing

# Test legitimate IP
curl -X POST http://localhost:8000/api/v1/check \
  -H "Content-Type: application/json" \
  -d '{"ip_address": "8.8.8.8"}'

# Test Tor exit node (example)
curl -X POST http://localhost:8000/api/v1/check \
  -H "Content-Type: application/json" \
  -d '{"ip_address": "185.220.101.1"}'

# Test private IP
curl -X POST http://localhost:8000/api/v1/check \
  -H "Content-Type: application/json" \
  -d '{"ip_address": "192.168.1.1"}'

🐳 Docker Deployment

Using Docker Compose

# Build and start services
docker-compose up -d

# View logs
docker-compose logs -f api

# Stop services
docker-compose down

Manual Docker Build

# Build image
docker build -t masked-ip-detection -f docker/Dockerfile .

# Run container
docker run -d \
  -p 8000:8000 \
  -v $(pwd)/models:/app/models \
  --name masked-ip-api \
  masked-ip-detection

📁 Project Structure

masked-ip-detection/
├── src/
│   ├── api/
│   │   ├── main.py              # FastAPI application
│   │   ├── routes.py            # API endpoints
│   │   └── schemas.py           # Pydantic models
│   ├── data/
│   │   └── collectors.py        # Data collection scripts
│   ├── features/
│   │   └── ip_features.py       # Feature extraction
│   └── models/
│       └── predictor.py         # ML prediction logic
├── models/                       # Trained ML models (gitignored)
│   ├── random_forest_model.pkl
│   ├── xgboost_model.pkl
│   └── feature_names.pkl
├── notebooks/                    # Google Colab notebooks
│   ├── Complete_Data_Collection.ipynb
│   ├── 02_preprocessing.ipynb
│   └── 03_model_training.ipynb
├── dashboard/                    # Web UI
│   └── templates/
│       └── index.html
├── tests/                        # Test suite
│   ├── test_api.py
│   └── test_features.py
├── docker/
│   ├── Dockerfile
│   └── docker-compose.yml
├── requirements.txt              # Python dependencies
├── .gitignore
└── README.md

🔒 Security Considerations

Production Deployment Checklist

Enable HTTPS: Use SSL/TLS certificates
API Authentication: Implement API keys or OAuth2
Rate Limiting: Prevent abuse (e.g., 100 requests/minute)
Input Validation: Already implemented via Pydantic
Logging: Monitor all requests and predictions
Redis Security: Use password authentication
CORS Configuration: Restrict allowed origins
Error Handling: Don't expose internal details

Example Rate Limiting (FastAPI)

from slowapi import Limiter, _rate_limit_exceeded_handler
from slowapi.util import get_remote_address

limiter = Limiter(key_func=get_remote_address)
app.state.limiter = limiter
app.add_exception_handler(RateLimitExceeded, _rate_limit_exceeded_handler)

@app.post("/api/v1/check")
@limiter.limit("100/minute")
async def check_ip(request: Request, ip_request: IPCheckRequest):
    # ... existing code

🤝 Contributing

We welcome contributions! Please follow these guidelines:

Fork the repository
Create a feature branch (git checkout -b feature/AmazingFeature)
Commit your changes (git commit -m 'Add AmazingFeature')
Push to the branch (git push origin feature/AmazingFeature)
Open a Pull Request

Development Setup

# Install development dependencies
pip install -r requirements-dev.txt

# Run linting
flake8 src/
black src/

# Run type checking
mypy src/

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

📧 Support & Contact

GitHub Issues: Report bugs or request features
Discussions: Join community discussions
Email: thakkarriddhi1510@gmail.com

🙏 Acknowledgments

Special thanks to:

Tor Project - Tor exit node data
MaxMind - GeoIP2 databases
Open-source proxy list maintainers - Community-driven threat intelligence
FastAPI - Modern Python web framework
Scikit-learn & XGBoost - ML ecosystem
SWAVLAMBAN 2025 Organizers - Hackathon opportunity

⭐ Star this repo | 🐛 Report Bug | 💡 Request Feature

Name		Name	Last commit message	Last commit date
dashboard/templates		dashboard/templates
docker		docker
notebooks		notebooks
src		src
.gitignore		.gitignore
README.md		README.md
docker-compose.yml		docker-compose.yml
requirements-colab.txt		requirements-colab.txt
requirements.txt		requirements.txt
setup.py		setup.py

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