🛡️ Cyber RL Defense System (Simulation)

Overview

Key Features

• 🔥 Reinforcement Learning-based cyber defense
• 🧪 Simulated attack environment (honeypot)
• 📊 Real-time attack visualization dashboard
• 🤖 AI decision-making using PyTorch
• 🐳 Dockerized deployment
• 🌐 Frontend + Backend architecture

Tech Stack

• AI/ML: PyTorch
• Backend: Python (FastAPI/Flask)
• Frontend: HTML, CSS, JavaScript
• Deployment: Docker, Hugging Face
• Logging: Custom attack logs

How It Works

1. Environment simulates cyber attacks
2. RL Agent observes state
3. Agent takes action (block, allow, monitor)
4. Rewards are assigned based on defense success
5. Model improves over time

Run Locally

frontend/index.html

Double Click To index.html

Run with Docker

docker build -t cyber-rl .
docker run -p 5000:5000 cyber-rl

Screenshots

Use Case

• Cybersecurity research
• AI-based intrusion detection
• Ethical hacking simulation
• Autonomous defense systems

Hackathon Submission

📜 License

Folder Structure

cyber-rl-insane/
│
├── 📁 backend/
│   ├── app.py
│   ├── rl_agent.py
│   ├── environment.py
│   ├── utils.py
│   └── requirements.txt
│
├── 📁 frontend/
│   ├── index.html
│   ├── app.js
│   ├── styles.css
│   └── assets/
│
├── 📁 models/
│   └── trained_model.pt
│
├── 📁 logs/
│   └── attacks.log
│
├── 📁 screenshots/
│   ├── dashboard.png
│   ├── training.png
│   └── results.png
│
├── 📁 docs/
│   ├── architecture.md
│   └── api.md
│
├── Dockerfile
├── docker-compose.yml
├── requirements.txt
├── .gitignore
├── LICENSE
├── README.md
└── run.sh