Ransomware Framework

A cybersecurity framework designed for behavioral profiling and analysis of ransomware. This tool helps security researchers and analysts understand ransomware behavior, detect anomalies, and develop mitigation strategies in a controlled environment.

Features

• Behavioral Profiling: Monitor and analyze ransomware actions, such as file system changes, network activity, and process manipulation.
• Threat Detection: Detect suspicious activities using customizable rules and patterns.
• Alerting System: Generate alerts for detected threats and anomalies.
• Modular Design: Easily extendable with custom monitoring and analysis modules.
• Safe Execution: Execute ransomware samples in a controlled, isolated environment.

Installation

You can install the framework using pip:

pip install rbprof

Usage

1. Basic Setup

Import the framework and initialize the components:

from rbprof import CybersecurityFramework

# Initialize the framework
framework = CybersecurityFramework()

# Run the framework
framework.run()

2. Customizing Monitoring

Add custom data sources or monitoring tools:

from rbprof import Monitor, DataSource

# Create a custom data source
class CustomDataSource(DataSource):
    def get_data(self):
        return [
            {"timestamp": time.time(), "user": "admin", "action": "login"},
            {"timestamp": time.time(), "user": "attacker", "action": "brute_force"},
        ]

# Initialize the framework with a custom data source
custom_data_source = CustomDataSource()
monitor = Monitor(custom_data_source)
framework = CybersecurityFramework(monitor=monitor)
framework.run()

3. Adding Detection Rules

Define custom threat detection rules:

from rbprof import Detector

# Create a custom detector
class CustomDetector(Detector):
    def __init__(self):
        super().__init__()
        self.threat_rules.append(
            {"action": "unauthorized_access", "description": "Unauthorized access detected"}
        )

# Initialize the framework with a custom detector
detector = CustomDetector()
framework = CybersecurityFramework(detector=detector)
framework.run()

4. Analyzing Behavior

Extend the behavioral analysis engine:

from rbprof import BehaviorEngine

# Create a custom behavioral engine
class CustomBehavioralEngine(BehaviorEngine):
    def analyze_behavior(self, data):
        anomalies = []
        for entry in data:
            if entry.get("action") == "suspicious_action":
                anomalies.append(entry)
        return anomalies

# Initialize the framework with a custom behavioral engine
behavioral_engine = CustomBehavioralEngine()
framework = CybersecurityFramework(behavioral_engine=behavioral_engine)
framework.run()

Example Output

When you run the framework, it will log detected anomalies and threats:

2023-10-10 12:00:00 - INFO - Data collected for analysis.
2023-10-10 12:00:01 - INFO - Behavioral anomalies detected: [{'user': 'attacker', 'action': 'brute_force'}]
2023-10-10 12:00:02 - WARNING - ALERT: Threat detected: Potential brute force attack

Contributing

Contributions are welcome! If you'd like to contribute, please follow these steps:

1. Fork the repository.
2. Create a new branch for your feature or bugfix.
3. Submit a pull request.

License

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

Disclaimer

This framework is intended for educational and research purposes only. Do not use it for malicious activities. Always ensure you have proper authorization before analyzing ransomware or other malware.

Support

If you encounter any issues or have questions, please open an issue on the GitHub repository.

Acknowledgments

• Inspired by the need for better ransomware analysis tools.
• Built by Elton Tanaka Mukarati.