Generative Cybersecurity

🚀 Generative Cybersecurity

An innovative AI-powered learning assistant for solving cybersecurity challenges, leveraging advanced AI models like White Rabbit with Retrieval-Augmented Generation (RAG) and Hypothetical Document Embeddings (HyDE) for accurate, context-aware responses.

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📖 Table of Contents

• About the Project
• System Architecture
• Prerequisites
• Setup Instructions
• Usage
• Directory Structure
• Acknowledgments

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📝 About the Project

The Generative Cybersecurity Application is designed to enhance learning and problem-solving in cybersecurity using generative AI models. It offers:

• Context-aware assistance for cybersecurity challenges.
• Ethical educational support using authorized materials like OverTheWire Bandit wargame series.
• Integration of AI-driven query enhancement and retrieval-based learning.

Key Features

• Query Enhancement: Improves user queries using LLM-based processing.
• Generative AI Responses: Provides accurate answers using White Rabbit.
• Vector Database Integration: Retrieves relevant cybersecurity documentation.
• User-Friendly Frontend: A Streamlit UI for an intuitive experience.
• Educational Focus: Covers fundamental Linux/Bash commands, SSH connectivity, file analysis, and basic cryptography.

Built With

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🏗 System Architecture

The system is built on a modular architecture, comprising:

1. Generative Module API:
   • Backend for AI model inference and FastAPI endpoints.
2. Streamlit Module API:
   • Frontend providing an interactive user interface.

System Diagram: Copy and paste the code from SystemDiagram.md into Mermaid Live Editor to visualize the architecture.

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✅ Prerequisites

Ensure the following are installed on your system:

• Docker Engine (19.03.0+)
• Docker Compose (2.0+)
• NVIDIA GPU with CUDA support
• NVIDIA Container Toolkit (nvidia-docker2)

🛠 Installation Instructions

Installing Docker

Step 1: Uninstall Conflicting Packages

Run the following command to remove any previously installed Docker-related packages that might conflict:

for pkg in docker.io docker-doc docker-compose docker-compose-v2 podman-docker containerd runc; do sudo apt-get remove $pkg; done

Step 2: Set Up Docker's apt Repository

1. Update the apt package index:

   sudo apt-get update

2. Install required packages:

   sudo apt-get install ca-certificates curl

3. Create the /etc/apt/keyrings directory:

   sudo install -m 0755 -d /etc/apt/keyrings

4. Download and add Docker’s GPG key:

   sudo curl -fsSL https://download.docker.com/linux/ubuntu/gpg -o /etc/apt/keyrings/docker.asc
   sudo chmod a+r /etc/apt/keyrings/docker.asc

5. Add the Docker repository:

   echo "deb [arch=$(dpkg --print-architecture) signed-by=/etc/apt/keyrings/docker.asc] https://download.docker.com/linux/ubuntu $(. /etc/os-release && echo "$VERSION_CODENAME") stable" | sudo tee /etc/apt/sources.list.d/docker.list > /dev/null

6. Update the package index:

   sudo apt-get update

Step 3: Install Docker Packages

Install Docker Engine, CLI, and associated plugins:

sudo apt-get install docker-ce docker-ce-cli containerd.io docker-buildx-plugin docker-compose-plugin

Step 4: Verify the Installation

Run the following command to verify that Docker is installed correctly:

sudo docker run hello-world

Step 5: Enable Non-Root Docker Usage

1. Add your user to the Docker group:

   sudo groupadd docker
   sudo usermod -aG docker $USER

2. Apply the changes:

   newgrp docker

3. Verify that you can run Docker commands without sudo:

   docker run hello-world

Step 6: Enable Docker on Boot

To start Docker and containerd on system boot:

sudo systemctl enable docker.service
sudo systemctl enable containerd.service

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Installing NVIDIA Container Toolkit

Step 1: Add NVIDIA’s Package Repository

Run the following command to add NVIDIA's GPG key and repository:

curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg && curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list | sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list

Step 2: Enable the Experimental Components

Edit the repository file to enable experimental components:

sudo sed -i -e '/experimental/ s/^#//g' /etc/apt/sources.list.d/nvidia-container-toolkit.list

Step 3: Update Package Index

sudo apt-get update

Step 4: Install NVIDIA Container Toolkit

sudo apt-get install -y nvidia-container-toolkit

Step 5: Configure the Runtime

Set NVIDIA as the default runtime for Docker:

sudo nvidia-ctk runtime configure --runtime=docker
sudo systemctl restart docker

Step 6: Verify the Installation

1. Check that the NVIDIA Container Toolkit is installed:

   dpkg -l | grep nvidia-container-toolkit

   Expected Output:

   ii  nvidia-container-toolkit                   1.16.2-1                                amd64        NVIDIA Container toolkit
   ii  nvidia-container-toolkit-base              1.16.2-1                                amd64        NVIDIA Container Toolkit Base
   

2. Verify NVIDIA runtime is available to Docker:

   sudo docker info | grep Runtimes

   Expected Output:

   Runtimes: io.containerd.runc.v2 nvidia runc
   

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⚙️ Setup Instructions

1. Clone the Repository

git clone https://github.com/iamamiramine/generative-cybersecurity.git
cd generative-cybersecurity

2. Install Docker and NVIDIA Toolkit

Refer to this section in the README for the installation commands.

3. Create Docker Network

docker network create generative-cybersecurity-network

4. Prepare Directory Structure

Ensure the following folders exist:

• models/ - Stores AI models.
• data/ - Stores application data (only .txt files are supported).

5. Download the Model

Use the following command to download the required model:

curl -X POST http://localhost:7575/download_llm -H "Content-Type: application/json" -d '{"model_name": "WhiteRabbitNeo/WhiteRabbitNeo-2.5-Qwen-2.5-Coder-7B"}'

Alternatively, use Swagger UI at http://localhost:7575/docs#/.

6. Build and Run the Application

docker compose build
docker compose up

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🚀 Usage

Accessing the Application

• Streamlit Frontend: http://localhost:7500
• Generative API Swagger UI: http://localhost:7575/docs

FastAPI Endpoints

1. Without Context:
   • POST /load_model
   • POST /load_pipeline
   • POST /load_chain
   • POST /generate
2. With Context:
   • POST /load_model
   • POST /load_pipeline
   • POST /load_docs
   • POST /load_ensemble_retriever_from_docs
   • POST /load_chain
   • POST /generate

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📂 Directory Structure

generative-cybersecurity/
├── models/                    # Contains AI models
├── data/                      # Contains application data
├── shared/                    # Shared resources
│   ├── assets/                # Images and other assets
│   └── config/                # Configuration files
├── generative-module-api/     # Backend service
└── streamlit-module-api/      # Frontend service

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🛠 Streamlit UI Workflow

1. Load the model.
2. Load the pipeline.
3. Load the documents.
4. Load the ensemble retriever.
5. Load the chain.
6. Generate text.

The UI dynamically reloads components like the pipeline and chain when parameters or context change.

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Contributing

Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again!

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

Top contributors:

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💡 Acknowledgments

Special thanks to:

• The White Rabbit open-source community for their LLM contributions.
• The OverTheWire community for providing ethical cybersecurity challenges.
• Open-source tools and libraries that made this project possible.

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