RooR 0.0.4

A project to analyze the behavior of AI models in post-quantum cybersecurity.

RooR: AI Behavior Analysis in Post-Quantum Cybersecurity

Overview

RooR is a Python R&D package designed to analyze the behavior of AI models in the context of post-quantum cybersecurity. It provides tools to:

• Simulate post-quantum cryptographic attacks.
• Analyze system logs for anomalies.
• Evaluate the resilience of AI models against emerging threats.

This package is intended for research and development purposes in cybersecurity and AI resilience.

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Key Features

• AI Behavioral Analysis: Detect anomalies in logs using advanced machine learning techniques.
• Post-Quantum Attack Simulation: Simulate lattice-based and code-based cryptographic attacks.
• Resilience Evaluation: Assess AI model performance under post-quantum threat scenarios.
• Report Generation: Export results in JSON and CSV formats for easy integration and analysis.

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Installation

Install RooR using pip:

# Clone the repository
git clone https://github.com/eurocybersecurite/RooR.git
cd RooR

# Install the package
pip install .

Note: For development purposes, you can also install in editable mode:

pip install -e .

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Running Tests

To run the automated tests for this project, activate the virtual environment and use the unittest module:

# Activate the virtual environment
source venv/bin/activate

# Run tests
python3 -m unittest discover tests

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Usage Examples

Here’s a quick example demonstrating the main functionalities:

from RooR import behavioral_analysis, attack_simulation, report_generation
import pandas as pd

# --- 1. Simulate a post-quantum attack ---
target_model = "Quantum-Resistant AI Model"
attack_result = attack_simulation.simulate_lattice_based_attack(target_model)
report_generation.generate_json_report(attack_result, "attack_report.json")

# --- 2. Analyze logs for anomalies ---
log_data = {
    'timestamp': ['2023-10-27 10:00:00', '2023-10-27 10:05:00', '2023-10-27 10:10:00'],
    'event_type': ['login', 'logout', 'login']
}
pd.DataFrame(log_data).to_csv('dummy_logs.csv', index=False)

anomalies = behavioral_analysis.analyze_logs('dummy_logs.csv')
report_generation.generate_csv_report(anomalies, "anomalies_report.csv")

# --- 3. Evaluate model resilience ---
predictions = [1, 0, 1, 1, 0]
ground_truth = [1, 0, 1, 0, 1]

resilience = behavioral_analysis.evaluate_post_quantum_resilience(predictions, ground_truth)
report_generation.generate_json_report(resilience, "resilience_report.json")

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Contributing

Contributions are welcome!

• Open an issue to report bugs or request features.
• Submit a pull request for improvements.

Please follow standard Python packaging and testing practices.

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License

This project is licensed under the MIT License.

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Developer Information

• Name: ABDESSEMED Mohamed
• Email: [e-mail]