https://github.com/muhammadparkar/ciphery
A simple Streamlit app that analyses the algorithm used in a given ciphered database.
https://github.com/muhammadparkar/ciphery
aes-encryption cybersecurity encryption-decryption pyhton
Last synced: over 1 year ago
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A simple Streamlit app that analyses the algorithm used in a given ciphered database.
- Host: GitHub
- URL: https://github.com/muhammadparkar/ciphery
- Owner: muhammadparkar
- License: apache-2.0
- Created: 2024-09-04T14:01:14.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-09-22T17:49:41.000Z (almost 2 years ago)
- Last Synced: 2025-02-03T23:41:13.059Z (over 1 year ago)
- Topics: aes-encryption, cybersecurity, encryption-decryption, pyhton
- Language: Python
- Homepage: https://ciphery.streamlit.app/
- Size: 155 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Codeowners: .github/CODEOWNERS
Awesome Lists containing this project
README
# 📄 Ciphery: Cipher Algorithm Analyzer
**Ciphery**, a Streamlit-based web application designed to analyze and identify the encryption algorithm used in a given ciphered database. This tool combines cutting-edge machine learning techniques to support various cryptographic algorithms, making it a powerful asset for cybersecurity professionals and researchers.
[](https://document-question-answering-template.streamlit.app/)
## Features
- **AES Encryption**: Supports Advanced Encryption Standard (AES) analysis.
- **Encryption & Decryption**: Analyze encrypted data and predict the cryptographic algorithm used.
- **Machine Learning Integration**: Leverages **SVM** and **Neural Networks** to classify encryption techniques.
- **TensorFlow & Streamlit**: Built with TensorFlow for deep learning and Streamlit for an interactive, user-friendly interface.
- **Real-time Predictions**: Upload your ciphered database and get instant predictions.
## Topics Covered
- **Cybersecurity**: Focus on cryptographic algorithms and encryption methods.
- **Python**: App built with Python, utilizing libraries like TensorFlow, Keras, and OpenCV.
- **AES Encryption**: Analyze and decode databases encrypted with AES and other algorithms.
- **Support Vector Machines (SVM)**: Machine learning-based classification.
- **Neural Networks**: Deep learning models to enhance prediction accuracy.
- **Streamlit**: A fast and flexible way to build the app interface.
## How to Use
1. **Visit** [ciphery.streamlit.app](https://ciphery.streamlit.app).
2. **Upload** your ciphered database or input encrypted text.
3. **Analyze**: Let the app predict the encryption algorithm used (e.g., AES, RSA).
4. **Results**: Instantly view the identified encryption algorithm and relevant details.
## Technologies
- **Python**: Core programming language.
- **TensorFlow & Keras**: Machine learning and deep learning libraries.
- **Streamlit**: For building interactive web apps.
- **SVM**: Support Vector Machines for classification.
- **OpenCV**: For any potential image-related processing.
## How to run it on your own machine
1. Install the requirements
```
$ pip install -r requirements.txt
```
2. Run the app
```
$ streamlit run streamlit_app.py
```
## Contribute
If you're interested in contributing to this project, feel free to submit a pull request or raise an issue.
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