const httpsServer = https.createServer({
  key: fs.readFileSync(path.join(__dirname, '..', 'peace.data.key')),
  cert: fs.readFileSync(path.join(__dirname, '..', 'peace.data.crt')),
}, app);

httpsServer.listen(443, 'api.peace.data', () => {
  console.log(`HTTPS Server running on port https://api.peace.data`);
});

// Send session cookie only over HTTPS
app.use(session({
  secret: sessionSecret,
  name: 'SessionID',
  resave: false,
  saveUninitialized: true,
  cookie: {
    secure: debug ? false : true,
    httpOnly: true,
  }
}));

// For https://api.peace.data:443/
app.use(cors({
  origin: 'https://peace.data:443/',
  optionsSuccessStatus: 204
}));

app.use(rateLimit({
  windowMs: 15 * 60 * 1000, // 15 minutes
  max: 100 // limit each IP to 100 requests per windowMs
}));

A public key and a private key will be used to encrypt and decrypt the JWT by the authentication server and application server.

If the application server registered with the asymmetric encryption, The public key will be given to the application server to use as the decryption key while authentication server will use the private key for the encryption.

data=base64urlEncode(header)+”.”+base64urlEncode(payload);

hashedData=hash(data,secret);

signature=base64urlEncode(hashedData);

Then we encode the Header, Payload and the signature separately in base 64 and combine them with “.”.

Header:-eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9
Payload:-eyJ1c2VySWQiOiJiMDhmODZhZi0zNWRhLTQ4ZjItOGZhYi1jZWYzOTA0NjYwYmQifQ
Signature:- -xN_h82PHVTCMA9vdoHrcZxH-x5mb11y1537t3rGzcM

A complete JWT will be look like this

To identify how JWT works lets consider there is a authentication server, Application server and a user.

1. DBSCAN - Density-based spatial clustering of applications with noise (DBSCAN) is a data clustering algorithm. It is a density-based clustering non-parametric algorithm: given a set of points in some space, it groups together points that are closely packed together (points with many nearby neighbors), marking as outliers points that lie alone in low-density regions (whose nearest neighbors are too far away).

2. Random forests or random decision forests are an ensemble learning method for classification, regression and other tasks that operate by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes (classification) or mean/average prediction (regression) of the individual trees.

Frontend: (Port 3000)

cd client
npm install
npm run dev

Backend Node.js: (Port 3001)

cd node
npm install
npm run dev

Backend Python: (Port 3002)

cd server
python3 -m http.server 3002

• Node.js 12.18.4 https://nodejs.org/en/download/
• Python 3.x https://www.python.org/downloads/