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:
- Operating System: Windows 10 or later, macOS Mojave or later
- Python: Version 3.6 or higher must be installed on your computer
- Minimum RAM: 4 GB
- Disk Space: At least 200 MB of free space
- Offline Operation: Detect phishing sites without needing internet access.
- Hybrid Model: Utilizes both CNN and LSTM for improved accuracy.
- User-Friendly Interface: Simple to use, even for beginners.
- Comprehensive Analysis: Classifies URLs as legitimate or malicious with high accuracy.
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.
- Locate the Downloaded File: Navigate to the folder where you downloaded the file.
- Extract (if necessary): If the file is zipped, right-click and extract it.
- Run the Application:
- For Windows, double-click the
.exefile to launch the application. - For macOS, right-click and select "Open" to run the
.app.
- For Windows, double-click the
- Input URLs: You will see a text box where you can enter the URL you want to check.
- Analyze: Click the "Analyze" button. The tool will process the input and provide you with a result.
- Results Interpretation: The results will indicate whether the URL is safe or potentially harmful.
If you encounter any issues while using the application, please consider the following:
- Online Documentation: Check the README file included in your downloaded package for further instructions.
- Community Support: You can ask for help by creating an issue in the repository’s GitHub page. The community is generally helpful and responsive.
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:
- CNN (Convolutional Neural Networks)
- Cybersecurity measures for everyday users
- Hybrid neural network approaches
- LSTM (Long Short-Term Memory) in machine learning
- Malware prevention strategies
- Offline models for practical use
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.