deviceprintx

🚀 Advanced device fingerprinting library for fraud detection, bot detection, and security analysis.

📦 Installation

npm install deviceprintx

⚡ Usage

import { generateFingerprintPayload } from "deviceprintx";

// Method 1: Using async/await
const run = async () => {
  const data = await generateFingerprintPayload();
  console.log(data);
};

run();

// Method 2: Using Promise (.then)
generateFingerprintPayload()
  .then((data) => {
    console.log(data);
  })
  .catch((error) => {
    console.error(error);
  });

🧠 Features

• 🔐 Unique deviceId & visitorId generation
• 🤖 Bot detection & probability scoring
• 🕵️ Incognito / private mode detection
• 🖥️ Hardware fingerprinting (CPU, Memory, GPU)
• 🎨 Canvas & Audio fingerprinting
• 🌐 Browser & OS detection
• ⚠️ Security risk analysis with confidence score
• 🎭 Spoof / emulator detection

📊 Example Output

{
  "visitorId": "3f02c252630490303e8d2c5d003103b03a3e28bf64c181265fd915c0c40863a9",
  "deviceId": "d74694981fa4bde81871a4b3694175f8dc8d3b840c1da680ef00540cf67e1902",
  "confidenceScore": 90,
  "status": "TRUSTED",
  "botDetected": false,
  "deviceType": "Desktop"
}

📦 API

generateFingerprintPayload()

Returns a detailed fingerprint object.

type FingerprintResponse = {
  visitorId: string;
  deviceId: string;
  requestId: string;

  confidenceScore: number;
  status: "TRUSTED" | "NEUTRAL" | "FLAGGED" | "ERROR";

  botDetected: boolean;
  botProbability: number;
  botReasons: string[];

  deviceType: string;
  browserName: string;
  browserVersion: string;
  osName: string;
  osVersion: string;

  incognitoResult: object;

  hardware: {
    cpuCores: number;
    deviceMemory: number;
    platform: string;
    gpuVendor: string;
    gpuRenderer: string;
    screenResolution: string;
    touchPoints: number;
  };

  timestamp: string;
};

⚠️ Important Notes

• Works only in browser environment
• Uses async APIs → requires await or .then()
• Some signals may vary based on browser privacy settings
• Incognito detection accuracy depends on browser limitations

🔐 Use Cases

• Fraud detection systems
• Multi-account prevention
• Device tracking & identification
• Risk scoring engines
• Security analytics

🚀 Roadmap

• TypeScript support
• Server-side validation helpers
• Advanced bot detection improvements
• Fingerprint stability enhancements

👨‍💻 Author

Dipesh Gothi

📄 License

MIT