Polymorphic Hard Drive Security Protocol (PHDSP)

Polymorphic Hard Drive Security Protocol (PHDSP) is a cutting-edge AI-based hard drive protection system. Inspired by advanced encryption mechanisms, it continuously monitors your hard drive for unauthorized access and modifications, dynamically rewrites its own code to thwart attacks, and ensures system integrity through self-healing mechanisms.

Features

• Continuous Monitoring: Constantly monitor your directory for any unauthorized changes.
• Advanced Anomaly Detection: Detect unauthorized access and file modifications using sophisticated algorithms.
• Dynamic Self-Modification: Adapt and rewrite its code to stay ahead of threats.
• System Recovery: Restore the system to a known good state in case of an attack.
• Encryption: Secure your backup files with robust encryption.
• Future-Proof: Designed for future enhancements like machine learning-based anomaly detection, behavior analysis, and dynamic code loading.

Installation

$ pip install PHDSP

Usage

1. Initialize and run WinterShield:

from phdsp.main import HardDriveProtector


protector = HardDriveProtector(
    directory="/path/to/directory",
    backup_path="/path/to/backup",
    threshold=5,
    check_interval=10,
)
protector.monitor_directory()

2. Configuration:
   • directory: The path to the directory you want to monitor.
   • backup_path: The path to the directory where backup files are stored.
   • threshold: The number of changes before an anomaly is detected.
   • check_interval: The interval (in seconds) between checks.

How It Works

1. Initialization: The HardDriveProtector class is initialized with the directory to monitor, backup path, anomaly detection threshold, and check interval.
2. Monitoring: The monitor_directory method continuously monitors the specified directory for any file changes.
3. Anomaly Detection: If changes exceed the threshold, an anomaly is detected, triggering code rewriting and system recovery.
4. Self-Modification: The rewrite_code method replaces the current script with a backup version, ensuring the protection code is up-to-date and uncompromised.
5. System Recovery: The recover_system method restores the system to a known good state using an encrypted backup file.

Future Enhancements

• Machine Learning for Anomaly Detection: Implementing ML models to detect more sophisticated anomalies.
• Behavior Analysis: Analyzing user and system behavior to detect unusual activities.
• Code Obfuscation: Making the code harder to understand using obfuscation techniques.
• Dynamic Code Loading: Fetching code dynamically from secure remote sources to stay updated.
• Authentication and Access Control: Implementing user authentication and access control mechanisms.

Contributing

Contributions are welcome! Please submit a pull request or open an issue to discuss your ideas.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• Inspired by advanced encryption mechanisms from the movie Captain America: The Winter Soldier.