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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

  1. Clone the Repository 
    git clone https://github.com/yourusername/Byzantine-Fault-Tolerant-System.git
cd Byzantine-Fault-Tolerant-System