Tri-Penta-Quasi-Lateral Datastream for AI Communication

Overview

The Tri-Penta-Quasi-Lateral Datastream for AI Communication is a secure and dynamic communication framework designed for AngelNET. This Python-based implementation provides a robust and scalable solution for handling multiple states and transmission types, ensuring secure and transparent communication between users and systems.

Features

Three States

1. General Communication
2. Secure Communication
3. Internal AngelNET Institution Partners

Five Internal Communication Transmissions

1. Data Exchange
2. Status Updates
3. Alerts and Notifications
4. Configuration Changes
5. Logs and Reports

Security Features

• Encryption and checksum verification
• Biometric authentication with deepfake detection
• Multi-factor authentication (MFA)
• Anomaly detection
• Monitoring and reporting
• Zero-knowledge proofs (zkSNARKs)
• Google IAM integration

Dependencies

The project relies on the following dependencies:

• Flask: A micro web framework for Python.
• cryptography: A library for encryption and decryption.
• hashlib: A library for generating and verifying checksums.
• tempfile: A library for creating temporary files.
• pyotp: A library for generating and verifying OTPs.
• logging: A library for logging suspicious transactions.
• scikit-learn: A library for anomaly detection.
• numpy: A library for numerical operations.
• py_ecc: A library for elliptic curve cryptography.
• google-auth: A library for Google IAM integration.
• deepface: A library for deepfake detection.
• face_recognition: A library for facial recognition.

Installation

To install the project, follow these steps:

1. Clone the repository:

   git clone https://github.com/your-username/tri-penta-communication.git
   cd tri-penta-communication

2. Install the dependencies:

   pip install -r requirements.txt

3. Configure the environment variables:

   cp .env.example .env

   Update the values in the .env file accordingly.

Usage

The project provides the following functionality:

Communication Handling

• /state/<int:state>/transmission/<int:transmission>: Handles communication based on the state and transmission type.

Data Encryption and Checksum Verification

• encrypt_data(data): Encrypts data using Fernet symmetric encryption.
• decrypt_data(encrypted_data): Decrypts encrypted data.
• generate_checksum(data): Generates a SHA-256 checksum for the given data.
• verify_checksum(data, checksum): Verifies the SHA-256 checksum for the given data.

Biometric Authentication with Deepfake Detection

• /biometric_auth: Endpoint for biometric authentication with deepfake detection using DeepFace.

Multi-Factor Authentication (MFA)

• /generate_otp: Endpoint for OTP generation using pyotp.
• /verify_otp: Endpoint for OTP verification using pyotp.

Anomaly Detection

• /anomaly_detection: Endpoint for detecting anomalies in user behavior using IsolationForest.

Monitoring and Reporting

• /transaction: Endpoint for monitoring suspicious transactions.

Zero-Knowledge Proofs (zkSNARKs)

• /zk_proof: Endpoint for generating zero-knowledge proofs.
• /verify_zk_proof: Endpoint for verifying zero-knowledge proofs.

Google IAM Integration

• /google_iam: Endpoint for Google IAM authentication.

Example Endpoint for Sending Data

• /send_data: An example endpoint to send data to the appropriate state and transmission type.

  curl -X POST -H "Content-Type: application/json" -d '{"state": 1, "transmission": 1, "data": "Hello, World!"}' http://localhost:5000/send_data

Running the Application

1. Run the application:

   python tri_penta_communication.py

2. Use a tool like curl or Postman to test the endpoints.

Testing

Unit Testing

Run unit tests using unittest:

python test_tri_penta_communication.py
Integration Testing
Run integration tests using unittest:

bash
Copy code
python test_tri_penta_communication.py
Security Testing
Perform security testing using tools like OWASP ZAP or Burp Suite.

Performance Testing
Run performance tests using Locust:

bash
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locust -f locustfile.py
End-to-End Testing
Run end-to-end tests using Selenium:

bash
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python test_end_to_end.py
Mocking External Services
Use unittest.mock to mock external services in your tests.

Security
The project prioritizes security and transparency by utilizing the following measures:

Decentralized identity verification: AngelNET ensures the authenticity of user identities without relying on a central authority.
Zero-knowledge proof system: zkSNARKs enable the chatbot to verify user identities without revealing sensitive information.
Malicious intent detection: MalIntentDetector detects and prevents unauthorized access to sensitive information.

Licensed Under Multi-Versal Copyright

Contributing
Contributions are welcome! Please fork the repository, make your changes, and submit a pull request.

Acknowledgments
The project was inspired by the following:

Blockchain-based Identity Verification System
Decentralized Social Media Platform
Artificial Intelligence Model for Collaboration and Content Suggestions
Contact
For questions, feedback, or support, please contact Delilah, blackbox.ai, GPT, and/or amanaknows.

Acknowledgments
The project was inspired by the following projects:

Blockchain-based Identity Verification System
Decentralized Social Media Platform
Artificial Intelligence Model for Collaboration and Content Suggestions
Contact
For questions, feedback, or support, please contact Delilah, blackbox.ai, gpt, and/or amanaknows.

This README.md provides a comprehensive overview of the project, including installation instructions, usage, testing, security measures, and more. Feel free to customize it further based on your specific requirements and project details.