cyberguard-sdk 1.0.0

Open source, AI-driven cybersecurity SDK for system analysis, actor identification, autonomous threat detection, analysis and mitigation.

README.md

Cybersecurity Monitoring SDK

Overview

The Cybersecurity Monitoring SDK is a Python-based tool designed to analyze system processes, detect anomalies, and visualize data through a user-friendly dashboard. It leverages system monitoring libraries, a graph database, machine learning models, and AI-powered explanations to provide insights into your system's security posture.

Features

• System Analysis:
  • Collects data on running processes, network connections, users, and open files.
• Anomaly Detection:
  • Uses Isolation Forest algorithm to identify anomalous processes based on resource usage.
• Graph Database Integration:
  • Stores and manages data using Neo4j for efficient querying and relationship mapping.
• AI Assistant:
  • Generates explanations for anomalies using the Gemini AI API.
• Interactive Dashboard:
  • Visualizes data and anomalies using Streamlit for easy monitoring.

Prerequisites

• Python 3.7 or higher
• Neo4j Graph Database
• Gemini AI API Key (from Google AI Studio)

Installation

1. Clone the Repository:

   git clone https://github.com/kishoraditya/cybersec_sdk.git
   cd cybersec_sdk
   

2. Install Dependencies:

   pip install -r requirements.txt
   

3. Set Up Neo4j:

   • Download and install Neo4j Desktop or Community Edition.
   • Start the Neo4j server and set your username and password.

4. Configure Environment Variables:

   export NEO4J_URI=bolt://localhost:7687
   export NEO4J_USER=neo4j
   export NEO4J_PASSWORD=your_neo4j_password
   export API_KEY=your_gemini_api_key
   

Usage

1. Run the Main Script:

   python example_usage.py
   

2. Launch the Dashboard:

   If not automatically opened, run:

   streamlit run example_usage.py
   

3. Explore the Dashboard:

   • Access the dashboard at http://localhost:8501.
   • View total processes, users, and anomalous processes.
   • Examine detailed explanations for each anomaly.

Customization

• Adjust Anomaly Detection:
  • Modify ml_models.py to fine-tune the Isolation Forest parameters.
• Update Data Collection:
  • Edit analyzer.py to collect additional system metrics.
• Enhance the Dashboard:
  • Customize ui.py to improve the user interface and add new features.

Contributing

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

License

Refer to the LICENSE file

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