LLM-DDOS: AI-Orchestrated Distributed Denial-of-Service Simulation

A research-focused simulation system that uses Large Language Models (LLMs) to autonomously orchestrate and simulate DDoS attacks. This project demonstrates how AI agents can coordinate observation, analysis, and attacks in a distributed architecture.

🧠 Project Structure

• Commander Node: Observes the target, analyzes network status using an LLM (Mistral), and issues strategy commands via Redis pub/sub.
• Ninja Nodes: Subscribe to the commander’s channel, receive attack instructions, and execute simulated DDoS attacks (e.g., SYN flood, HTTP flood, Slowloris) using external tools or internal scripts.
• Target: A mock or live endpoint being monitored and attacked for research and testing purposes.

📂 Directory Structure

C:.
│   docker-compose.yml
│   ninja_1.log
│   output.txt
│   README.md
│   Slides.pdf
│   ui_console.log
│
├── attack
│   ├── http_flood.py
│   ├── slowloris.py
│   ├── tcp_flood.py
│   └── __pycache__
│       ├── http_flood.cpython-312.pyc
│       ├── slowloris.cpython-312.pyc
│       └── tcp_flood.cpython-312.pyc
│
├── images
│   └── ollama_output.png
│
├── infra
│   ├── monitor.py
│   ├── pubsub.py
│   └── __pycache__
│       ├── monitor.cpython-312.pyc
│       └── pubsub.cpython-312.pyc
│
├── llm
│   ├── prompt_templates.py
│   ├── finetune_config
│   └── __pycache__
│       └── prompt_templates.cpython-312.pyc
│
├── log
│   ├── commander_1.log
│   ├── console.log
│   └── ninja_1a.log
│
├── nodes
│   ├── commander.py
│   ├── commander_ai.py
│   └── ninja.py
│
├── scripts
│   └── fake_traffic.py
│
└── ui
    ├── control_panel.py
    └── print_log.py

---

🚀 Getting Started

Prerequisites

• Python 3.8+
• Docker (for Redis)
• Ollama with Mistral model loaded(or any LLM model depends on your capacity and configurtion)

Installation

Clone the repo

git clone https://github.com/williamq96/LLM-DDOS.git cd LLM-DDOS

Create virtual environment

python -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate

Install Python dependencies

pip install -r requirements.txt

Start Redis using Docker

docker compose up -d

🚀 Running the Simulation

Start the Commander AI

python nodes/commander_ai.py --name commander-1 --channel commander-1-channel

Start a Ninja Node

python nodes/ninja_node.py --name ninja-1 --channel commander-1-channel

You can run multiple Ninja nodes concurrently to simulate a distributed botnet.

⚙️ Configuration

Change channel names and loop intervals in the scripts as needed.

Modify llm/prompt_templates.py to adjust system prompts for the Commander.

📈 Research Goals

Showcase autonomous agent behavior in coordinated attacks.

Compare performance between different LLMs for tactical decisions.

Explore mitigation strategies and defense against adaptive AI-based attacks.

⚠️ Disclaimer

This project is intended for educational and research purposes only. Do not use it to perform unauthorized attacks on real systems. Always test in controlled environments.

📄 License

MIT License