GitHub RadarBlue team tool
🔍 Detect phishing websites offline using a combined CNN and LSTM model, analyzing URL features for high accuracy in classification. Primary language: Jupyter Notebook. 1 stars.
Project links:Open GitHub projectBack to radar
phishing-detection-rnn-cnn is an offline phishing detection model designed to help you identify risky websites. Using a hybrid CNN-LSTM architecture, it classifies URLs as either legitimate or potentially malicious based on patterns it has learned. This tool adds an extra layer of security to your online experience, all without requiring an internet connection.
Follow these steps to download and run the phishing-detection tool on your computer. This guide will help you through the process, even if you have little technical experience.
Before downloading, ensure your system meets these requirements:
To get started, visit the Releases page to download the latest version of phishing-detection-rnn-cnn.
Once on the release page, find the appropriate file for your operating system. Download it to your computer.
.exe file to launch the application..app.If you encounter any issues while using the application, please consider the following:
While the phishing-detection model is designed to be accurate, it is not infallible. Always use additional judgment when visiting websites, especially if they require personal information.
This tool covers various important areas in online security. Here are some relevant topics you may find useful:
Your feedback is vital for improving this tool. If you have suggestions, bug reports, or feature requests, please feel free to reach out by opening an issue in the GitHub repository. Contributions are welcome, and we appreciate any help from the community.
This project is licensed under the MIT License. You can freely use, modify, and distribute the software as long as you credit the original developers.
Don't miss the chance to enhance your online safety.