Multi-Agent AI System for Cybersecurity Defense

Project Overview

This project aims to develop a comprehensive and advanced multi-agent AI system for cybersecurity defense. The system leverages open source large language models (LLMs), multi-agent frameworks, advanced red teaming AI, open source cybersecurity AI models, open source cybersecurity datasets, and other relevant technologies to defend against AI-powered cyberattacks.

Objectives

• Integrate open source LLMs like GPT-3 or GPT-4 with multi-agent frameworks such as OpenAI Gym or Ray RLlib.
• Develop specialized agents for threat detection, incident response, and vulnerability assessment.
• Implement real-time communication protocols between agents.
• Create an advanced red teaming AI to simulate sophisticated cyberattacks.
• Utilize open source cybersecurity datasets and models for training and evaluation.
• Develop a data pipeline for continuous updates with new threat intelligence and cybersecurity data.

Key Components

1. Multi-Agent AI System

   • Integration of OpenAI Gym and GPT-3.
   • Specialized agents for different cybersecurity tasks.
   • Real-time communication protocols using ZeroMQ or RabbitMQ.

2. Advanced Red Teaming AI

   • Simulation of sophisticated cyberattacks using open source cybersecurity AI models.
   • Continuous learning and adaptation through a feedback loop.

3. Data Pipeline

   • Integration of real-time data feeds from open source threat intelligence platforms.
   • Continuous updates with new threat intelligence and cybersecurity data.

Installation and Setup

1. Clone the repository:

   git clone https://github.com/githubnext/workspace-blank.git
   cd workspace-blank

2. Create a virtual environment and activate it:

   python3 -m venv venv
   source venv/bin/activate

3. Install the required dependencies:

   pip install -r requirements.txt

4. Set up the necessary API keys and authentication for GPT-3.

Usage

1. Initialize the multi-agent AI system:

   python src/main.py

2. The system will start the agents for threat detection, incident response, and vulnerability assessment.

3. The advanced red teaming AI will simulate cyberattacks to test the system's resilience.

4. The data pipeline will continuously update the system with new threat intelligence and cybersecurity data.

Examples

Threat Detection Agent

The threat detection agent uses GPT-3 to analyze network traffic and identify potential threats. It communicates with other agents in real-time to share information and coordinate actions.

Incident Response Agent

The incident response agent uses GPT-3 to respond to detected threats. It takes appropriate actions to mitigate the impact of the threats and communicates with other agents to ensure a coordinated response.

Vulnerability Assessment Agent

The vulnerability assessment agent uses GPT-3 to identify and assess vulnerabilities in the system. It provides recommendations for mitigating the vulnerabilities and communicates with other agents to ensure a comprehensive defense strategy.

Contributing

Contributions are welcome! Please read the contributing guidelines for more information.

License

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