Phishing Website Detection System

A real-time web application that detects phishing websites using machine learning, enhancing online security and user safety.

Comment

Inspiration

Phishing attacks are one of the most widespread and dangerous cybersecurity threats. I was inspired to build this system after seeing how easily people — especially those without technical knowledge — fall victim to malicious links sent via emails, messages, or fake websites. I wanted to create a solution that uses machine learning to detect phishing websites and help protect users online.

What it does

The Phishing Website Detection System allows users to enter a URL and receive an instant prediction on whether it's legitimate or phishing. It uses feature extraction techniques and a trained ML model to analyze the URL and deliver accurate results via a simple web interface.

How we built it

  • Data Collection: Used an open-source dataset of phishing and legitimate URLs.
  • Feature Extraction: Extracted features such as URL length, use of special characters (like @, -), presence of HTTPS, number of subdomains, etc.
  • Model Training: Built and trained machine learning models (e.g., Random Forest) using Python and Scikit-learn.
  • Backend: Implemented with Flask to serve predictions in real-time.
  • Frontend: Built using HTML, CSS, and basic JS for a user-friendly interface.
  • Integration: Connected frontend and backend to enable seamless interaction.

Challenges we ran into

  • Finding a high-quality, well-balanced dataset.
  • Selecting meaningful features to improve model performance.
  • Ensuring the model was fast enough to provide real-time results.
  • Handling compatibility issues during backend and frontend integration.

Accomplishments that we're proud of

  • Created a fully working phishing detection web app from scratch.
  • Achieved good model accuracy with relevant URL-based features.
  • Learned full-stack development and applied machine learning practically.
  • Built a project that addresses a real-world security issue.

What we learned

  • The importance of data cleaning and feature engineering in ML.
  • Building and evaluating classification models with Scikit-learn.
  • How to deploy ML models using Flask.
  • Integrating backend ML logic into a clean and responsive web app.

What's next for Phishing Website Detection System

  • Enhance detection with content-based features (e.g., presence of forms or suspicious text).
  • Integrate browser extension support for real-time phishing protection.
  • Use advanced ML techniques like ensemble models or neural networks.
  • Maintain a live database of detected phishing sites.
  • Add multi-language support and improve UI/UX.

Built With

Share this project:

Updates