Skip to content

PROGRESS.md

Latest commit

 

History

History
135 lines (106 loc) · 3.98 KB

PROGRESS.md

File metadata and controls

135 lines (106 loc) · 3.98 KB
Raw

Backend Implementation Progress

✅ Completed (Phase 1: Foundation)

Project Structure

  • Created app/ directory with proper structure
  • Set up models/, schemas/, api/ folders
  • Initialized all __init__.py files

Configuration & Database

  • ✅ app/config.py - Settings management with Pydantic
  • ✅ app/database.py - SQLAlchemy async setup
  • ✅ Using SQLite for development (easy to switch to PostgreSQL later)
  • ✅ Database connection working

SQLAlchemy Models

  • ✅ app/models/alert.py - Alert and AlertSource models
  • ✅ app/models/fl_round.py - FLRound and FLClient models
  • ✅ app/models/prediction.py - Prediction and PredictedTechnique models
  • ✅ app/models/network_data.py - NetworkData model

Pydantic Schemas

  • ✅ app/schemas/alert.py - Alert request/response schemas
  • ✅ app/schemas/fl_status.py - FL schemas
  • ✅ app/schemas/prediction.py - Prediction schemas

Database Migrations

  • ✅ Alembic initialized
  • ✅ Initial migration created and applied
  • ✅ All tables created successfully

FastAPI Application

Dependencies Installed

  • ✅ asyncpg (PostgreSQL driver)
  • ✅ aiosqlite (SQLite async driver)
  • ✅ All Poetry dependencies installed

✅ Completed (Phase 2: Repository Layer)

Repository Pattern

  • ✅ app/repositories/alert_repository.py - Alert CRUD operations
  • ✅ app/repositories/fl_repository.py - FL rounds and clients
  • ✅ app/repositories/prediction_repository.py - Predictions
  • ✅ Full filtering, pagination, and search support

Seed Data

  • ✅ scripts/seed_database.py - Populate database with mock data
    • 20 sample alerts with multiple sources
    • 4 FL rounds (3 completed, 1 in-progress)
    • 10 predictions with technique chains
  • ✅ scripts/test_repositories.py - Test repository functionality

Features Implemented

  • ✅ Alert filtering by severity, facility, status, search, time range
  • ✅ Pagination support for all endpoints
  • ✅ Alert statistics calculation
  • ✅ FL round management with client tracking
  • ✅ Prediction creation with technique relationships
  • ✅ Async database operations throughout

📝 Next Steps (Phase 3: API Endpoints)

Immediate Next Tasks

  1. Create repository layer (app/repositories/)
  2. Create seed data script (scripts/seed_database.py)
  3. Implement API endpoints:
    • app/api/alerts.py
    • app/api/fl_status.py
    • app/api/predictions.py
  4. Add WebSocket support

Testing

# Start server
poetry run uvicorn app.main:app --reload --port 8000

# Visit docs
http://localhost:8000/docs

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

Database

  • Location: ./ics_threat_detection.db (SQLite)
  • Migrations: alembic/versions/
  • To migrate: poetry run alembic upgrade head

🐳 Docker Setup

Infrastructure Services Available

  • ✅ docker-compose.yml - All services configured
  • ✅ PostgreSQL (port 5432)
  • ✅ Redis (port 6379)
  • ✅ Neo4j (ports 7474, 7687)
  • ✅ Kafka + Zookeeper (port 9092)

Quick Start Docker

# Start all services
docker-compose up -d

# Check status
docker-compose ps

# View logs
docker-compose logs -f

See DOCKER_SETUP.md for detailed instructions.

🎯 Current Status

Phase 1 Complete: Foundation is solid and ready for API implementation.

The backend is now ready to:

  • Accept API endpoint implementations
  • Store data in the database
  • Serve the React frontend
  • Be extended with WebSocket support

Current Setup:

  • ✅ Server running on http://localhost:8000
  • ✅ Using SQLite for development (can switch to PostgreSQL with Docker)
  • ✅ Docker compose ready for full infrastructure

To use PostgreSQL:

  1. docker-compose up -d postgres
  2. Update .env: DATABASE_URL=postgresql+asyncpg://ics_user:ics_password@localhost:5432/ics_threat_detection
  3. poetry run alembic upgrade head