This repository demonstrates a proof-of-concept (PoC) for an AI-Enhanced Byzantine Fault Tolerance (BFT) consensus mechanism. The project integrates Machine Learning (ML) with blockchain consensus to predict node behavior, optimize leader selection, and simulate a consensus round.

• AI-Driven Node Analysis: Predicts whether a node is honest or malicious based on behavior data.
• Leader Selection: Dynamically selects the most suitable leader using AI predictions and stake evaluation.
• Consensus Simulation: Simulates a simple consensus round to demonstrate the functionality.

• Python: Used for rapid prototyping and integrating AI/ML components.
• scikit-learn: For training and evaluating the AI model.
• Random Forest: Core AI model for node behavior prediction.

1. Clone the repository:

   git clone https://github.com/your-username/ai-bft-consensus.git
   cd ai-bft-consensus

2. Install depemndencies pip install -r requirements.txt

1. Train the AI model and run the consensus simulation: python main.py
2. The script outputs:

• AI model training accuracy
• Selected leader node based on AI predictions
• Results of the consensus round

The project includes a synthetic dataset (data/dataset.csv) for simulating node behaviors:

• Features: uptime, past_faults, response_time, stake
• Labels: 0 (Malicious), 1 (Honest)

This PoC aims to explore the potential of integrating AI into blockchain consensus mechanisms, focusing on:

• Enhanced Security: Detecting and mitigating malicious behavior in real-time.
• Improved Efficiency: Optimizing leader selection and reducing computational overhead.
• Future Applications: Serving as a foundation for integrating AI with production-grade blockchain frameworks like Tendermint.

Contributions are welcome! Feel free to open issues or submit pull requests.

This project is licensed under the MIT License.

Feel free to customize further based on your specific requirements or aspirations for the project! Let me know if you’d like me to modify or expand on this.