Machine learning / URL intelligence

Check a link before you trust it.

Phishing Website Detection analyzes URL signals with a trained Random Forest model and turns a complex classification workflow into a clear, practical scan.

View on GitHub Explore the workflow

Why phishing detection matters

Trust can be engineered into a page. URL evidence gives users and systems another signal before sensitive information is shared.

Phishing websites imitate trusted organizations to persuade people to reveal passwords, account details, or other private information.

Financial services and online platforms process a large volume of real-time transactions, making them attractive targets for fraud and social engineering. Attackers continuously change their techniques, create convincing replicas, and distribute them through emails, messages, and misleading links.

This project explores how measurable URL characteristics can help distinguish legitimate websites from potentially phishing websites. It is designed for learning, experimentation, and responsible security research.

Signals behind the prediction

The model works with a compact numeric representation of URL behavior and domain information.

URL structure

Length, redirects, subdomains, IP addresses, and prefix or suffix patterns.

Trust indicators

HTTPS tokens, shortening services, DNS records, and statistical reputation signals.

Domain history

Domain age and registration information that can reveal suspicious patterns.

From URL to result

The same documented workflow powers the Streamlit interface and the training notebooks.

Enter a URL

Provide a complete website URL in the Streamlit scanner.

Extract signals

Convert URL and domain characteristics into numeric features.

Run the model

Pass the feature vector to the saved Random Forest classifier.

Inspect the result

Review the Legitimate or Phishing classification and extracted values.

Explore the project

The repository keeps the application, training material, and source datasets easy to inspect.