rbprof 0.1.4

cybersecurity framework designed for behavioral profiling and analysis of ransomware.

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.

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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.

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Installation

You can install the framework using pip:

pip install rbprof

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Usage

1. Basic Setup

Import the framework and initialize the components:

from ransomware_framework import CybersecurityFramework

# Initialize the framework
framework = CybersecurityFramework()

# Run the framework
framework.run()

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2. Customizing Monitoring

Add custom data sources or monitoring tools:

from ransomware_framework 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()

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3. Adding Detection Rules

Define custom threat detection rules:

from ransomware_framework 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()

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4. Analyzing Behavior

Extend the behavioral analysis engine:

from ransomware_framework import BehavioralEngine

# Create a custom behavioral engine
class CustomBehavioralEngine(BehavioralEngine):
    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()

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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

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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.

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License

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

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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.

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Support

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

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Acknowledgments

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