SafeNav is an AI-assisted web and link analysis platform designed to evaluate the safety of URLs, websites, and application links.
It performs multi-layer analysis using static inspection, reputation checks, and machine-learning–based scoring to identify potentially malicious or unsafe links.
The project is structured as a full-stack system with a React frontend and a Python-based backend, focusing on real-world web security use cases such as phishing detection, suspicious domain analysis, and unsafe content identification.
Handles different types of links including shortened URLs, redirects, and malformed URLs.
Detects suspicious patterns such as abnormal URL length, special characters, and domain structure anomalies.
Evaluates domain age, SSL certificate validity, and known reputation signals.
Uses engineered features and machine learning models to compute a final safety score for each URL.
Designed with separable components for easy extension, testing, and experimentation.
SafeNav is organized as a full-stack application:
- frontend/ – React-based user interface
- backend/ – Python backend responsible for API handling and link analysis
- Built with React (Vite)
- Responsible for user interaction and result visualization
- Communicates with backend APIs to request URL analysis and display safety reports
- Built using Python
- Implements the core security analysis pipeline:
- URL normalization
- Lexical and structural checks
- SSL and domain inspection
- Machine learning–based scoring engine
- React
- Vite
- JavaScript
- HTML / CSS
- Python
- FastAPI (API layer)
- Scikit-learn (machine learning experimentation)
- Custom feature engineering modules
- Git & GitHub
- Visual Studio Code
- Phishing link detection
- Unsafe website analysis
- Educational research on web security and threat detection
- Full-stack development practice with a security focus
This project is actively under development.
Planned enhancements include:
- Advanced machine learning models
- Dynamic behavior analysis
- Containerized deployment (Docker)
- Extended reporting and visualization features
SafeNav can be executed in two different modes depending on the use case:
- Docker Mode – Recommended for demo, evaluation, and deployment
- Development Mode – Recommended while coding and debugging
This mode runs the frontend, backend, and database together using Docker Compose.
- Docker Desktop installed
- Docker Compose enabled
- Clone the repository:
git clone https://github.com/su7ox/SafeNav.git
cd SafeNav- Build and start all services:
docker-compose up -d- Verify running containers:
docker ps- Frontend UI: http://localhost:5173
- Backend API: http://localhost:8000
- API Documentation (Swagger): http://localhost:8000/docs
docker-compose downDocker does not automatically reflect code changes.
docker-compose build
docker-compose up -dThis mode supports hot reload and is recommended during development.
- Python 3.10 or higher
- Navigate to backend directory:
cd backend- Create virtual environment (one-time):
python -m venv venv- Activate virtual environment:
venv\Scripts\activate- Install dependencies:
pip install -r requirements.txt- Run backend server:
uvicorn app.main:app --reloadBackend will be available at:
- Node.js (LTS version recommended)
- Open a new terminal and navigate to frontend directory:
cd frontend- Install dependencies:
npm install- Start frontend development server:
npm run devFrontend will be available at:
This project is licensed under the MIT License.
su7ox
GitHub: @su7ox