API Security Monitoring System - Startup Checklist

Your complete API Security Monitoring System is ready to run!

Pre-Startup Verification

Backend: FastAPI application with ML models
Frontend: Next.js dashboard with real-time updates
Database: PostgreSQL schema and migrations
Models: CNN + BiLSTM demo models with feature extraction
Documentation: Complete guides and API documentation
Configuration: Environment templates and examples

Startup Instructions

Step 1: Initialize the System

Run this command once to set up models:

python3 scripts/init.py

Expected output:

✓ Created models directory
✓ Demo models created successfully
✓ Created .env.local template
✓ Project initialization complete!

Step 2: Install Dependencies

# Install frontend dependencies
npm install

# Install backend dependencies (in scripts directory)
pip install -r scripts/requirements.txt

Step 3: Start the Backend

Open Terminal 1:

python3 scripts/run_backend.py

Wait for this message:

INFO:     Uvicorn running on http://0.0.0.0:8000
INFO:     Application startup complete

Backend is now running at:

API: http://localhost:8000
Docs: http://localhost:8000/docs
Health: http://localhost:8000/api/health

Step 4: Start the Frontend

Open Terminal 2:

npm run dev

Wait for this message:

▲ Next.js 16.x.x
  - Local:        http://localhost:3000

Frontend is now running at:

Dashboard: http://localhost:3000

Accessing the System

Main Dashboard

Open your browser and go to: http://localhost:3000

You'll see:

Real-time threat statistics
Threat distribution chart
Recent alerts
Live metrics

API Documentation

Go to: http://localhost:8000/docs

Interactive API documentation with try-it-out functionality

Test a Request

curl -X POST http://localhost:3000/api/proxy/api/analyze \
  -H "Content-Type: application/json" \
  -d '{
    "method": "POST",
    "path": "/api/admin/config",
    "headers": {"User-Agent": "Suspicious-Bot"},
    "body": "'; DROP TABLE users; --",
    "response": {"status_code": 403, "response_time_ms": 150}
  }'

Response shows threat analysis with CNN, BiLSTM, and combined scores.

What You Can Do Now

1. Monitor Real-time Threats

View dashboard with live statistics
See threat distribution by severity level
Watch model comparison charts

2. Analyze Requests

Send individual requests for analysis
Batch analyze multiple requests
Get detailed threat reports

3. View Logs & Alerts

Browse complete request history
Filter by threat level, IP, path
Export logs to CSV

4. Manage Alerts

View active security alerts
See detailed threat information
Acknowledge threats

5. Access Analytics

View 24-hour threat trends
See model performance metrics
Analyze detection patterns

Quick Demo Scenarios

Scenario 1: SQL Injection Attempt

curl -X POST http://localhost:3000/api/proxy/api/analyze \
  -H "Content-Type: application/json" \
  -d '{
    "method": "POST",
    "path": "/api/users/login",
    "body": "username=admin'\''OR'\''1'\''='\''1&password=test",
    "response": {"status_code": 401, "response_time_ms": 200}
  }'

Expected: HIGH or CRITICAL threat

Scenario 2: Brute Force Pattern

# Send 3 failed login attempts
for i in {1..3}; do
  curl -X POST http://localhost:3000/api/proxy/api/analyze \
    -H "Content-Type: application/json" \
    -d "{
      \"method\": \"POST\",
      \"path\": \"/api/auth/login\",
      \"body\": \"attempt=$i\",
      \"response\": {\"status_code\": 401}
    }"
done

BiLSTM should detect temporal pattern.

Scenario 3: Normal Traffic

curl -X POST http://localhost:3000/api/proxy/api/analyze \
  -H "Content-Type: application/json" \
  -d '{
    "method": "GET",
    "path": "/api/public/data",
    "response": {"status_code": 200, "response_time_ms": 45}
  }'

Expected: LOW threat (normal traffic)

Navigation Guide

Dashboard (/)

Overview of all threats
Real-time statistics
Recent alerts

Analytics (/analytics)

Threat trends over time
Model performance comparison
Detection rate analysis

Logs (/logs)

Complete request history
Advanced filtering
Export functionality

Alerts (/alerts)

Active threats only
Detailed information
Action recommendations

System Health

Check system status any time:

# Health check
curl http://localhost:8000/api/health

Expected response:

{
  "status": "healthy",
  "version": "1.0.0",
  "models_loaded": true,
  "database_connected": true
}

Troubleshooting

Backend won't start

# Check if port 8000 is available
lsof -i :8000

# Or use a different port (modify scripts/run_backend.py)

Frontend won't start

# Clear cache and reinstall
rm -rf node_modules .next
npm install
npm run dev

Can't connect frontend to backend

# Verify backend is running
curl http://localhost:8000/api/health

# Check .env.local has correct FASTAPI_URL
cat .env.local

Models not loading

# Regenerate models
python3 scripts/create_models.py

Features Available

Machine Learning

Dashboard

API

Developer Tools

Next Steps

Explore the Dashboard: Navigate through all pages to understand the system
Send Test Requests: Use curl or the API docs to test threat detection
Review Logs: Check security logs and understand threat classifications
Read Documentation:
- README.md for complete reference
- QUICKSTART.md for examples
- DEPLOYMENT.md for production setup
Plan Integration: Prepare to integrate with your API

Production Deployment

When ready to deploy:

See DEPLOYMENT.md for detailed instructions
Choose deployment option (Vercel + Heroku, AWS, Docker, etc.)
Set up PostgreSQL for persistent storage
Configure environment variables
Deploy frontend and backend
Set up monitoring and alerts

Support Resources

API Docs: http://localhost:8000/docs
README: Full project documentation
QUICKSTART: Examples and quick reference
DEPLOYMENT: Production setup guide
PROJECT_SUMMARY: Architecture and overview

You're All Set!

Your API Security Monitoring System is ready to use. The dashboard is running at:

http://localhost:3000

Start monitoring your API security now!

Questions? Check the documentation files:

README.md - Complete reference
QUICKSTART.md - Examples
DEPLOYMENT.md - Production setup
PROJECT_SUMMARY.md - Architecture

Issues?

Check logs in terminal windows
Verify all services are running
Ensure ports 3000 and 8000 are available
Check .env.local configuration

Ready to scale? See DEPLOYMENT.md for production deployment options.

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