1st-project/backend

URL Malicious Detection API

This FastAPI application provides an API for detecting malicious URLs using two different machine learning models.

Features

• Two independent ML models for URL analysis
• RESTful API for easy integration
• High performance with uv package management

Installation

1. Clone the repository
2. Create and activate a virtual environment using uv:

cd backend
uv venv
source .venv/bin/activate

3. Install dependencies using uv pip:

uv pip install -r requirements.txt

Running the Application

Start the FastAPI server:

uvicorn app.main:app --reload

The API will be available at http://localhost:8000

API Endpoints

GET /

Health check endpoint that confirms the API is running.

Response:

{
  "message": "URL 악성 판별기 FastAPI 서버 정상 작동 중!"
}

POST /predict

Analyzes a URL to determine if it's malicious.

Request Body:

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

Response:

{
  "url": "http://example.com",
  "model1": 0.2048,  // Lower value means less likely to be malicious
  "model2": {
    "url": "http://example.com",
    "malicious_probability": 0.1076,  // Probability of being malicious
    "is_malicious": false,  // Boolean classification result
    "threshold": 0.4034  // Classification threshold
  }
}

Models

The application uses two different models for URL analysis:

1. XGBoost Ensemble (model1): Ensemble of 4 XGBoost models
2. Neural Network (model2): TensorFlow/Keras model

Dependencies

Main dependencies:

• FastAPI
• TensorFlow
• XGBoost
• scikit-learn
• pandas
• numpy
• tldextract
• uvicorn

License

This project is licensed under the MIT License.