🌐 Data Obfuscation and Cybersecurity System

📖 Overview

This project implements an integrated Data Obfuscation and Cybersecurity System. The system combines data pseudonymization, tokenization, cryptographic encryption, and AI-based anomaly detection into a unified workflow to protect sensitive data and detect potential cyber threats.

✨ Features

🔒 Data Obfuscation

• Pseudonymization: Replaces sensitive information with unique identifiers (UUIDs).
• Tokenization: Encrypts pseudonymized data for additional security.

🛡️ Cryptographic Algorithms

• AES Encryption: Encrypts obfuscated data using AES symmetric encryption.
• RSA Encryption: Secures keys and provides asymmetric encryption for data exchange.

🤖 AI-Based Anomaly Detection

• Isolation Forest: Detects anomalies in data using machine learning.
• Custom Features: Simulated feature extraction for anomaly detection.

🔗 Integrated Workflow

• Reads sensitive data, applies obfuscation, and encrypts it.
• Detects anomalies in encrypted/obfuscated data using an AI model.
• Decrypts and deobfuscates the data to restore the original content.

📂 Workflow

1. Input Data: A file containing sensitive information.
2. Obfuscation:
   • Pseudonymization of sensitive text.
   • Tokenization with encryption.
3. Encryption: AES encrypts the obfuscated data.
4. AI Analysis: Detects anomalies in the obfuscated/encrypted data.
5. Decryption and Deobfuscation:
   • Decrypts the AES-encrypted data.
   • Restores original data through detokenization and reverse pseudonymization.

📁 File Structure

|-- cryptographic_algorithms.py  # Main script for integration
|-- sample.txt                   # Sample input file
|-- decrypted_sample.txt         # Output after decryption and deobfuscation
|-- aes_key.key                  # AES encryption key
|-- private_key.pem              # RSA private key (password-protected)
|-- public_key.pem               # RSA public key

🛠️ Requirements

• Python 3.8+
• Libraries:
  • cryptography
  • pandas
  • scikit-learn

Install dependencies:

pip install cryptography pandas scikit-learn

🚀 Usage

1. Clone the repository:

   git clone https://github.com/yourusername/Artificial-Intelligence-Project.git
   cd Artificial-Intelligence-Project

2. Run the main script:

   python cryptographic_algorithms.py

3. Verify the output:

   • Check sample.txt and decrypted_sample.txt to confirm the workflow.

🔍 Key Features in Detail

🔒 Pseudonymization

• Maps original data to UUIDs for anonymity.
• Example:

  Original: Name: John Doe
  Pseudonymized: c992ffc7-aff9-4eec-a797-5ea1adabd0e1
  

🔑 Tokenization

• Encrypts pseudonymized data for security.
• Example:

  Token: gAAAAABng...
  

🛡️ AES Encryption

• Uses a 256-bit AES key for data encryption.
• Securely stores the encrypted file as sample.txt.enc.

🔐 RSA Key Management

• Password-protected RSA private key stored as private_key.pem.
• Public key stored as public_key.pem.

🤖 Anomaly Detection

• AI model predicts whether data is normal or anomalous.
• Example prediction:

  AI Prediction: Normal
  

✅ Tests and Validation

• The system has been tested with:
  • Sample text data (sample.txt).
  • Anomaly detection using a simulated dataset.
• Output files are validated to match original content.

🌟 Future Enhancements

• Support for additional file formats (e.g., JSON, CSV).
• Extend AI model to real network traffic datasets.
• Add a GUI or web interface for ease of use.

👥 Contributors

• Nico M -
• Geri R -

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Thank you for exploring the Data Obfuscation and Cybersecurity System! 🎉