GPU-Accelerated RL for CyberBattle Network Security

A high-performance framework combining CyberBattleSim (Microsoft's cybersecurity environment) with GPU-accelerated DQN for training agents on large-scale network penetration testing.

Key Capabilities:

• Train agents on networks with 100-50,000 nodes
• 100-200 environment steps/second with GPU acceleration
• Deep Q-Network (DQN) with dense reward shaping
• Handles complex credential systems and network topologies
• Full Docker containerization for reproducibility

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Quick Start (5 minutes)

1. Prerequisites

# Ensure you have:
docker --version          # Docker 20.10+
docker run --rm --gpus all nvidia/cuda:11.8.0-base nvidia-smi  # Verify GPU

2. Build Docker Image

cd Docker
docker-compose build

3. Start Container & Build C++ Code

docker-compose up -d pentest-dev
docker-compose exec pentest-dev bash

# Inside container:
cd /app && mkdir -p build && cd build
cmake .. && make -j$(nproc)

4. Run Training

# Quick test (100 nodes, 100 episodes)
bash /app/quick_start.sh test

# Or full training
bash /app/quick_start.sh small    # 1000 nodes, 5K episodes (~5 min)
bash /app/quick_start.sh medium   # 2000 nodes, 10K episodes (~15 min)

5. Monitor Training

# In another terminal:
nvidia-smi dmon -s puc    # Real-time GPU usage
nvtop                      # Interactive GPU monitor

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How It Works

System Architecture

The system has 3 main layers:

┌─────────────────────────────────────────┐
│  Python Training (gpu_rl_engine.py)     │  ← Main training loop with DQN
├─────────────────────────────────────────┤
│  C++ Wrapper (cyberbattle_wrapper.cpp)  │  ← Bridges Python ↔ CyberBattleSim
├─────────────────────────────────────────┤
│  CyberBattleSim + GPU Kernels           │  ← Network simulation + CUDA
└─────────────────────────────────────────┘

Training Flow (One Episode)

1. Environment Reset: Network initialized with hidden nodes
2. Agent Action: DQN selects action (which node to attack/scan)
3. Simulation: CyberBattleSim simulates the attack, updates network state
4. Observation: Agent receives observation (discovered nodes, owned nodes)
5. Reward Shaping:
   • +1.0 for discovering a new node
   • +5.0 for compromising a node
   • -0.01 per step (time penalty)
6. Learning: Store experience in replay buffer, train on GPU mini-batches
7. Repeat until agent owns 60% of network (success) or max steps reached

Key Components

Component	Purpose
env_wrapper.py	Converts CyberBattleSim to Gymnasium format + reward shaping
gpu_rl_engine.py	DQN training loop: experience collection, mini-batch learning
cyberbattle_wrapper.cpp	C++ bridge to CyberBattleSim (low-latency)
gpu_kernels.cu	CUDA kernels for Q-value computation
CyberBattleSim	Microsoft's network security simulation environment

---

Example Training Output

[CyberBattle] Initialized with 100 nodes, obs_dim=110, action_dim=50

Episode 1/100
[PY-STEP] step=1, shaped_reward=4.98, owned=1/3, disc=8, success=False
[PY-STEP] step=2, shaped_reward=-0.01, owned=1/3, disc=8, success=False
[PY-STEP] step=3, shaped_reward=5.99, owned=2/3, disc=9, success=False
[PY-STEP] step=4, shaped_reward=5.99, owned=3/3, disc=10, success=True ✓
Episode 1 completed: 4 steps, total_reward=16.95

Episode 2/100
[PY-STEP] step=1, shaped_reward=0.99, owned=0/3, disc=3, success=False
...

Episode 100/100
[PY-STEP] step=1, shaped_reward=5.98, owned=1/5, disc=12, success=False
Episode 100 completed: 22 steps, avg_reward=8.5, success_rate=87%

Interpreting Output:

• owned=1/3: Agent owns 1 node, needs 3 to win
• disc=8: 8 nodes discovered
• success=True: Episode succeeded (owned ≥ target)
• success_rate=87%: 87 out of 100 episodes succeeded

---

Results & Performance

Training Results

After training on 1000-node network for 5000 episodes:

Metric	Result
Episode Length	20-50 steps (average)
Success Rate	70-90% (owns 60% nodes)
Training Time	5-10 minutes (A100)
Steps/Second	100-200 (GPU accelerated)
Convergence	Episode 500-1000

Scalability

Network Size	Episodes	Time	GPU Memory
100 nodes	100	30 sec	2GB
1K nodes	5K	5 min	4GB
5K nodes	10K	20 min	8GB
10K nodes	20K	1 hour	12GB
50K nodes	SIR only	15 sec	6GB

Agent Behavior

The trained agent learns to:

• Scan progressively: Discover nodes methodically rather than randomly
• Exploit credentials: Use discovered credentials to own nodes faster
• Prioritize targets: Focus on high-value nodes (more connections)
• Adapt strategy: Change approach based on network topology

Example learned strategy:

Early game (episodes 1-100): Explore randomly
Mid game (episodes 100-500): Focus on discovering nodes with valid credentials
Late game (episodes 500+): Exploit multi-hop attacks to own isolated subnets

---

Configuration

Training Parameters

Modify in quick_start.sh or directly in training script:

# Network config
NODES=1000           # Network size (100-50000)
EPISODES=5000        # Total training episodes

# RL config
BATCH_SIZE=128       # GPU batch size
LEARNING_RATE=0.0001
GAMMA=0.99           # Discount factor

# Reward shaping
DISCOVERY_BONUS=1.0  # Per discovered node
COMPROMISE_BONUS=5.0 # Per owned node
STEP_PENALTY=-0.01   # Per step

Hardware Requirements

• GPU: NVIDIA (A10, A100, RTX series) with 6GB+ VRAM
• CPU: 8+ cores
• RAM: 16GB minimum, 32GB recommended
• Storage: 20GB free

---

Testing

Run Tests

cd /app/build
ctest -V                    # Run all tests
ctest -R "GPUKernels" -V   # GPU kernel tests
ctest -R "CyberBattle1k" -V # Production-scale test

Test Coverage

• ✓ GPU kernel correctness (matrix ops, activations)
• ✓ CUDA environment simulation (SIR propagation)
• ✓ Integration tests (GPU + environment)
• ✓ Large-scale tests (50K nodes)
• ✓ End-to-end training (1K nodes, 100 episodes)

---

Common Commands

# Build inside container
cd /app/build && cmake .. && make -j$(nproc)

# Run quick training
bash /app/quick_start.sh test

# Run tests
ctest --output-on-failure

# Monitor GPU
nvidia-smi dmon -s puc

# View logs
tail -f /app/logs/training.log

# Check results
cat /app/outputs/test_summary.txt

---

Project Structure

.
├── Docker/                  # Docker config (Dockerfile, docker-compose.yml)
├── src/
│   ├── cpu/environment/     # CyberBattle C++ wrapper
│   ├── gpu/                 # GPU kernels & policy network
│   └── cuda_env/            # Network simulation
├── python/
│   ├── cyberbattle_env/
│   │   ├── env_wrapper.py          # Gymnasium interface + reward shaping
│   │   └── gpu_rl_engine.py        # DQN training loop
│   └── train_large_scale.py
├── tests/                   # C++ & Python tests
├── CMakeLists.txt           # C++ build config
├── quick_start.sh           # Quick training script
└── README.md                # This file

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Key Libraries

Library	Version	Purpose
CyberBattleSim	latest	Network security simulation
Gymnasium	0.29.1	RL environment API
CUDA	11.8	GPU computing
pybind11	2.11.1	C++ ↔ Python bindings
CMake	3.18+	Build system
NumPy	1.24.3	Numerical computing

---

Troubleshooting

Problem: GPU not detected in container

docker run --rm --gpus all nvidia/cuda:11.8.0-base nvidia-smi
# Should show GPU info. If not, reinstall nvidia-docker2

Problem: Out of memory

# Reduce batch size or network size
# In quick_start.sh or training script:
BATCH_SIZE=64    # from 128
NODES=500        # from 1000

Problem: Python module not found

export PYTHONPATH=/app/python:$PYTHONPATH

Problem: CMake not finding Python

# Inside container, ensure Python dev headers are installed
apt-get install -y python3.10-dev pybind11-dev

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References

• CyberBattleSim: https://github.com/microsoft/CyberBattleSim
• Gymnasium: https://gymnasium.farama.org/
• CUDA Toolkit: https://docs.nvidia.com/cuda/
• pybind11: https://pybind11.readthedocs.io/
• Docker: https://docs.docker.com/

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Performance Tips

1. GPU utilization: Monitor with nvidia-smi dmon -s puc
2. Batch size: Increase for better GPU utilization (watch memory)
3. Network size: Start small (100 nodes) for testing, scale up gradually
4. Training stability: If rewards are erratic, reduce learning rate
5. Convergence: Success rate should improve after 500-1000 episodes

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Version: 1.0.0 Last Updated: December 2024