captchured-ml

Installation

npm install captchured-api

Usage

1. Use the Hosted API

You can send requests directly to the hosted API:

POST https://captchured.shashw1t.in/

See the API Documentation below for request/response format.

2. Self-host the API

You can run your own instance:

const createApp = require('captchured-api');
const app = createApp();
const port = process.env.PORT || 3000;
app.listen(port, () => {
  console.log(`Server running on http://localhost:${port}`);
});

Or, from the command line:

npx captchured-api
# or if installed globally
captchured-api

API Documentation

Endpoint

POST /

• URL: https://captchured.shashw1t.in/
• Method: POST
• Content-Type: application/json

Request Body

Send a JSON object with the following structure:

{
  "key_count": 3,
  "key_sequence": ["a", "b", "c"],
  "time_delay": [100, 120],
  "mouse_movements": [
    {
      "x": 100,
      "y": 200,
      "timeDelay": 50,
      "timestamp": 1710000000000
    }
  ],
  "mouse_clicks": [
    {
      "x": 120,
      "y": 220,
      "timestamp": 1710000000100
    }
  ],
  "total_time": 1500,
  "environment": {
    "timezone": "Asia/Kolkata",
    "language": "en-US",
    "cpu": 8,
    "browser": "Mozilla/5.0",
    "os": "Windows",
    "deviceType": "Desktop"
  }
}

Field Descriptions

• key_count: Number of keypresses.
• key_sequence: Array of keys pressed.
• time_delay: Array of delays (ms) between keypresses.
• mouse_movements: Array of mouse movement objects.
• mouse_clicks: Array of mouse click objects.
• total_time: Total time spent (ms).
• environment: Object with user environment details.

Response

Returns a JSON object:

{
  "message": "Data received and processed successfully",
  "cookies": undefined
}

When ML model integration is ready, the response will include a prediction result.

Example Usage (with fetch in frontend)

fetch('https://captchured.shashw1t.in/', {
  method: 'POST',
  headers: { 'Content-Type': 'application/json' },
  body: JSON.stringify({
    key_count: 3,
    key_sequence: ['a', 'b', 'c'],
    time_delay: [100, 120],
    mouse_movements: [],
    mouse_clicks: [],
    total_time: 1500,
    environment: {
      timezone: 'Asia/Kolkata',
      language: 'en-US',
      cpu: 8,
      browser: 'Mozilla/5.0',
      os: 'Windows',
      deviceType: 'Desktop'
    }
  })
})
  .then(res => res.json())
  .then(data => console.log(data));

Frontend Integration Example

To use the API from your own frontend, send a POST request to the / endpoint with the required data.
You can use fetch, axios, or any HTTP client in your frontend code.

Example using fetch

fetch('https://captchured.shashw1t.in/', {
  method: 'POST',
  headers: { 'Content-Type': 'application/json' },
  body: JSON.stringify({
    key_count: 3,
    key_sequence: ['a', 'b', 'c'],
    time_delay: [100, 120],
    mouse_movements: [
      { x: 100, y: 200, timeDelay: 50, timestamp: 1710000000000 }
    ],
    mouse_clicks: [
      { x: 120, y: 220, timestamp: 1710000000100 }
    ],
    total_time: 1500,
    environment: {
      timezone: 'Asia/Kolkata',
      language: 'en-US',
      cpu: 8,
      browser: 'Mozilla/5.0',
      os: 'Windows',
      deviceType: 'Desktop'
    }
  })
})
  .then(res => res.json())
  .then(data => {
    // Handle the prediction result or response
    console.log(data);
    // Example: alert(data.predictionResult);
  })
  .catch(error => {
    console.error('Error:', error);
  });

How users will do it

1. Collect user behavior data in your frontend (keypresses, mouse movements, etc.).
2. Format the data as shown above.
3. Send a POST request to https://captchured.shashw1t.in/.
4. Handle the response in your frontend (e.g., display prediction to the user).

License

MIT