🚀 Fraud Detection System (URL & QR Code Analysis)

A hybrid fraud detection system that analyzes URLs and QR codes to identify potential phishing or fraudulent links. This project leverages machine learning and rule-based analysis to classify links as safe or fraudulent, providing a confidence score and risk level.

🔥 Features

✅ URL Analysis - Detects malicious or suspicious URLs based on domain characteristics, patterns, and ML models.

✅ QR Code Analysis - Extracts and scans URLs from QR codes for potential fraud.

✅ Risk Level & Confidence Score - Displays fraud probability with a risk level (Low/Medium/High).

✅ History Tracking - Saves previously analyzed URLs and QR codes for reference.

✅ Real-Time Detection - Provides instant feedback on URLs & QR codes.

✅ Cross-Origin Support (CORS) - Allows frontend (JavaScript) to communicate with backend (Flask).

🛠️ Tech Stack

• Frontend: HTML, CSS, JavaScript
• Backend: Python (Flask)
• Machine Learning: Scikit-learn, Pandas, NumPy
• QR Code Processing: OpenCV, pyzbar, Pillow
• CORS Handling: Flask-CORS

📊 Fraud Detection Metrics Comparison

Previous Metrics:

              precision    recall  f1-score   support

           0       0.87      0.83      0.85       172
           1       0.79      0.84      0.81       128

    accuracy                           0.83       300
   macro avg       0.83      0.83      0.83       300
weighted avg       0.84      0.83      0.83       300

Updated Metrics:

              precision    recall  f1-score   support

           0       0.85      0.90      0.88       162
           1       0.88      0.82      0.85       138

    accuracy                           0.86       300
   macro avg       0.86      0.86      0.86       300
weighted avg       0.86      0.86      0.86       300

🏗️ Project Setup

Clone the Repository

git clone https://github.com/yourusername/fraud-detection.git
cd fraud-detection

Install Dependencies

pip install -r requirements.txt

⚠️ Important Prerequisite (for Windows Users)

To avoid DLL-related errors (e.g., when using pyzbar or OpenCV), please install the Microsoft Visual C++ Redistributable packages from 2008 to 2015.

🔗 Download Links:

• Visual C++ 2008 Redistributable (x64/x86): Download

• Visual C++ 2010 Redistributable (x64/x86): Download

• Visual C++ 2012 Redistributable (x64/x86): Download

• Visual C++ 2013 Redistributable (x64/x86): Download

• Visual C++ 2015-2022 Redistributable (x64): Download

• Visual C++ 2015-2022 Redistributable (x86): Download

Make sure to install both x86 and x64 versions if you are running a 64-bit operating system.

Run the Flask Server

python app.py

(Runs on http://127.0.0.1:5000/ by default)

Start the Frontend

Open index.html in a browser or use Live Server (VS Code).

📌 API Endpoints

1️⃣ Analyze URL

Endpoint: POST /analyze-url

Request:

{
  "url": "https://example.com"
}

Response:

{
  "is_fraud": true,
  "confidence": 85.6,
  "risk_level": "High",
  "risk_factors": ["Suspicious domain", "Contains unusual characters"]
}

2️⃣ Analyze QR Code

Endpoint: POST /analyze-qr

Request:

Upload an image containing a QR code

Response:

{
  "is_fraud": false,
  "confidence": 32.4,
  "risk_level": "Low"
}

Screenshhot

👨‍💻 Contributors

• Ritesh Brahmachari
• Satyabrata Panigrahi
• Tanisha Dutta