PhishGuard 🛡️

A Flask-based web application for real-time phishing URL detection.
PhishGuard analyzes suspicious URLs using a combination of heuristic rules and machine learning models, helping users quickly identify potential phishing attacks.
It features a dark-themed responsive UI, history tracking with a database, and confidence scoring for predictions.

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📖 Overview

Phishing websites are one of the most common cybersecurity threats, tricking users into revealing sensitive information.
PhishGuard provides a simple yet powerful way to check suspicious URLs in real time.

• Hybrid Engine → combines heuristic checks (e.g., suspicious keywords, domains) with machine learning.
• Ease of Use → paste a URL, get results instantly in a clean, dark-themed interface.
• Transparency → shows confidence scores and maintains a history of past analyses.

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🚀 Features

• Real-time phishing URL detection
• Hybrid detection engine: heuristic + machine learning
• Dark-themed, responsive web UI
• Stores analysis history in SQLite/PostgreSQL
• Confidence scores for classification
• REST API endpoints

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🛠️ Technologies Used

• Backend: Flask, SQLAlchemy, scikit-learn
• Frontend: HTML5, Bootstrap 5, JavaScript
• Database: SQLite (local), PostgreSQL (Render)
• Deployment: Render (Gunicorn)

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⚙️ Setup (Local)

# Clone the repo
git clone https://github.com/SyedaUlfath/PhishGuard.git
cd PhishGuard

# Create virtual environment
python -m venv venv
source venv/bin/activate  # Linux/Mac
venv\Scripts\activate   # Windows

# Install dependencies
pip install -r requirements.txt

# Run the app
python main.py

App runs at: http://127.0.0.1:5000

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🌍 Deployment (Render)

1. Push code to GitHub
2. Create new Web Service on Render
3. Set:
   • Build Command: pip install -r requirements.txt
   • Start Command: gunicorn app:app
4. Add environment variables:
   • SESSION_SECRET → a secure random string
   • DATABASE_URL → provided by Render (PostgreSQL)
5. Deploy 🎉

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📸 Screenshots

Homepage & URL Scanner

Detection results & Analytics dashboard

Recent analysis history & Threat statistics

Security recommendations & Disclaimer notice

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📜 License

This project is licensed under the MIT License © 2025 Syeda Ulfath Qousain.