Byzantine Fault-Tolerant Distributed System with Machine Learning-Based Attack Detection
Overview
This project implements a Byzantine fault-tolerant distributed system in C, enhanced with machine learning-based attack detection mechanisms. It aims to ensure consensus among distributed nodes even in the presence of faulty or malicious actors.
Features
- Consensus Algorithms: Implements PBFT and Honey Badger BFT.
- Networking: Robust communication between nodes.
- Machine Learning: Detects and mitigates attacks using ML models.
- Attack Simulation: Simulates various attack scenarios to test system resilience.
- Formal Verification: Ensures the correctness of consensus mechanisms.
- Comprehensive Testing: Unit and integration tests to validate functionalities.
Directory Structure
[Provide a brief overview of the directory structure here.]
Setup Instructions
- Clone the Repository
git clone https://github.com/yourusername/Byzantine-Fault-Tolerant-System.git
cd Byzantine-Fault-Tolerant-System