PhishGuard

PhishGuard is a Streamlit-based phishing detection app that helps users assess whether an Email, SMS, or URL may be suspicious. It is designed as a trust-first cybersecurity demo that combines machine learning prediction with clearer verdicts, cleaner UI, and safer user guidance.

Live Demo

Live App

Preview

Why this project exists

Phishing attacks continue to target users through fake emails, scam messages, and malicious links. PhishGuard was built to provide a simple and beginner-friendly way to review suspicious content and get a quick risk assessment through an accessible web interface.

This V2 rebuild improves the earlier prototype by focusing on:

• better validation and safer input handling
• clearer trust messaging and result readability
• improved project structure and maintainability
• a cleaner, more professional Streamlit interface
• stronger portfolio/demo presentation

Features

• Email phishing analysis
• SMS scam/phishing analysis
• URL risk analysis
• Trust-focused result presentation
• Validation for empty or invalid input
• Cleaner visual hierarchy and user flow
• Streamlit-based deployable web interface

Screenshots

Home Dashboard

Email Analysis

SMS Analysis

URL Analysis

Trust-Focused Result Card

Tech Stack

• Python
• Streamlit
• scikit-learn
• Pickle / trained model file
• Git & GitHub
• Streamlit deployment

Project Structure

PhishGuard/
├── app.py
├── train.py
├── phishguard_model.pkl
├── requirements.txt
├── README.md
├── .gitignore
├── .streamlit/
│   └── config.toml
└── screenshots/
    ├── home-dashboard.png
    ├── email-analysis.png
    ├── sms-analysis.png
    ├── url-analysis.png
    └── trust-result-card.png

How It Works

1. Open the app and choose the relevant input type: Email, SMS, or URL.
2. Paste the suspicious content into the appropriate field.
3. The app validates the input before processing.
4. The model and supporting logic analyze the content.
5. PhishGuard returns a verdict with a clearer presentation of the result.

Installation

Clone the repository:

git clone https://github.com/aryanParth-afk/PhishGuard.git
cd PhishGuard

Install dependencies:

pip install -r requirements.txt

Run the app locally:

streamlit run app.py

Usage

• Use the Email tab to check suspicious email text.
• Use the SMS tab to inspect scam-like messages.
• Use the URL tab to evaluate whether a link may be risky.
• Review the verdict and explanation carefully before deciding whether content is safe.

V2 Improvements

This version focuses on turning the original project into a cleaner and more portfolio-ready application.

Phase 1

• improved validation behavior
• stronger warning and trust messaging
• safer handling of weak or empty input

Phase 2

• cleaner structure and maintainability improvements
• better separation of concerns
• improved code organization for future updates

Phase 3

• professional Streamlit UI redesign
• improved layout hierarchy and readability
• better sidebar and header presentation
• stronger trust-first result display

Example Use Cases

• Reviewing a suspicious email before clicking a link
• Checking whether an SMS message looks scam-like
• Evaluating an unfamiliar URL before opening it
• Demonstrating a student cybersecurity project in interviews, showcases, or college reviews

Limitations

• This project is intended as an educational and demo-focused cybersecurity application.
• The output should be treated as a supportive signal, not a final security decision.
• Real-world phishing detection can require more context, larger datasets, and broader threat intelligence.

Future Improvements

• richer phishing indicator explanations
• confidence visualization improvements
• expanded dataset and retraining workflow
• report export or downloadable summary
• browser extension or API version
• improved logging and analytics

Author

Aryan Parth

• GitHub: aryanParth-afk

License

Add a license here if you want the project to be openly reusable, for example MIT.