This capstone project will guide you through building a Web Intelligence Solution, Intelligence agencies require secure and scalable full-stack platforms to handle classified communications, data analysis, and real-time threat monitoring. This project demonstrates a high-security, full-stack Python application with a focus on authentication, real-time web interactions, advanced API design, and scalability.
Objective: Build a secure intelligence management platform using Python that integrates authentication, real-time monitoring, and data processing with high availability and scalability.
Components
- Backend: Django for the core application, FastAPI for APIs, WebSockets for real-time events.
- Frontend: React.js with Next.js for a secure and responsive UI.
- Authentication: OAuth2 and JWT for secure user authentication.
- API Architecture: REST API with Django and GraphQL with Graphene.
- Scalability: Redis caching, RabbitMQ message queues, and load balancing with Nginx.
- Containerization & Serverless: Docker for deployment, AWS Lambda & API Gateway for serverless functions.
Step 1: Setting Up the Development Environment
1.1 Creating the Virtual Environment
python3 -m venv secure-intel-env
cd secure-intel-env
source bin/activate # On Windows use .\Scripts\activate
pip install django fastapi graphene uvicorn djangorestframework channels redis celery jwt
1.2 Project Structure
secure_intel_platform/
├── backend/
│ ├── manage.py # Django project manager
│ ├── config/ # Django settings and configurations
│ ├── users/ # Authentication & authorization
│ ├── intelligence/ # Core app for threat intelligence
│ ├── api/ # FastAPI-based API service
│ ├── ws/ # WebSockets for real-time alerts
│ ├── tasks/ # Celery task handling
│ ├── static/
│ └── templates/
├── frontend/ # React & Next.js frontend
├── docker-compose.yml # Containerized deployment
├── requirements.txt # Dependencies
└── README.md
Step 2: Backend Implementation
2.1 Django Authentication with JWT
users/views.py
from rest_framework.views import APIView
from rest_framework.response import Response
from rest_framework.permissions import IsAuthenticated
import jwt, datetime
from django.conf import settings
from django.contrib.auth.models import User
from django.contrib.auth import authenticate
class LoginView(APIView):
def post(self, request):
username = request.data.get('username')
password = request.data.get('password')
user = authenticate(username=username, password=password)
if user:
payload = {
'id': user.id,
'exp': datetime.datetime.utcnow() + datetime.timedelta(hours=12),
'iat': datetime.datetime.utcnow()
}
token = jwt.encode(payload, settings.SECRET_KEY, algorithm='HS256')
return Response({'token': token})
return Response({'error': 'Invalid credentials'}, status=400)
2.2 FastAPI REST API for Intelligence Reports
api/main.py
from fastapi import FastAPI, Depends
from pydantic import BaseModel
from sqlalchemy import create_engine, Column, String, Integer
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import sessionmaker
app = FastAPI()
Base = declarative_base()
engine = create_engine("sqlite:///intel.db")
SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)
def get_db():
db = SessionLocal()
try:
yield db
finally:
db.close()
class Report(Base):
__tablename__ = 'reports'
id = Column(Integer, primary_key=True, index=True)
title = Column(String, index=True)
content = Column(String)
Base.metadata.create_all(bind=engine)
class ReportSchema(BaseModel):
title: str
content: str
@app.post("/report/")
def create_report(report: ReportSchema, db=Depends(get_db)):
new_report = Report(title=report.title, content=report.content)
db.add(new_report)
db.commit()
return {"message": "Report created successfully"}
2.3 WebSockets for Real-Time Intelligence Alerts
ws/consumers.py
import asyncio
from channels.generic.websocket import AsyncWebsocketConsumer
import json
class AlertConsumer(AsyncWebsocketConsumer):
async def connect(self):
await self.accept()
await self.send(text_data=json.dumps({"message": "Connection established."}))
async def receive(self, text_data):
alert = json.loads(text_data)
await self.send(text_data=json.dumps({"alert": alert}))
Step 3: Frontend Implementation
Using Next.js for Secure UI
Installation:
npx create-next-app frontend
cd frontend
npm install axios jwt-decode
Authentication with JWT: frontend/utils/auth.js
import axios from 'axios';
export async function login(username, password) {
const response = await axios.post('/api/login/', { username, password });
if (response.data.token) {
localStorage.setItem('token', response.data.token);
}
return response.data;
}
Step 4: Deploying with Docker and Serverless
4.1 Dockerizing the Application
Dockerfile
FROM python:3.9
WORKDIR /app
COPY . /app
RUN pip install -r requirements.txt
CMD ["gunicorn", "config.wsgi:application", "--bind", "0.0.0.0:8000"]
docker-compose.yml
version: '3'
services:
web:
build: .
ports:
- "8000:8000"
depends_on:
- redis
redis:
image: "redis:alpine"
4.2 Deploying Serverless Functions on AWS Lambda
handler.py
import json
def lambda_handler(event, context):
return {
'statusCode': 200,
'body': json.dumps({'message': 'Serverless Function Active'})
}
This full-stack secure intelligence platform integrates Django, FastAPI, WebSockets, GraphQL, and containerized & serverless architectures to create a scalable, high-performance, real-time intelligence management system.
Some improvements that we can consider in the future are:
- Implement AI-based anomaly detection for real-time threat intelligence.
- Integrate blockchain for immutable intelligence logs.
This project sets a strong foundation for secure intelligence agency applications by leveraging Python’s best practices, cutting-edge web technologies, and DevOps principles.