LQAP: Lightweight Quantum-Resistant Authentication Protocol

A secure authentication framework for Vehicle-to-Grid (V2G) networks using post-quantum cryptography, Physical Unclonable Functions (PUFs), and Federated Learning.

Overview

LQAP provides a secure, efficient and quantum-resistant authentication solution for V2G environments. It combines several cutting-edge technologies:

• Post-Quantum Cryptography: XMSS and LMS signature schemes to resist quantum computing attacks
• Physical Unclonable Functions (PUF): Hardware-based authentication for tamper-resistant identity verification
• Zero-Knowledge Proofs (ZKP): Privacy-preserving cross-domain authentication
• Hierarchical Federated Learning (HFL): Distributed anomaly detection for enhanced security
• Blockchain Integration: Immutable logging of authentication events

System Components

The LQAP system consists of four main entity types:

1. Electric Vehicles (EVs): Mobile entities that request charging services
2. Charging Stations (CSs): Fixed infrastructure that provides charging services
3. Edge Nodes (ENs): Regional aggregation points that issue credentials and manage local federated learning
4. Electric Service Providers (ESPs): Top-level entities that coordinate edge nodes and maintain global models

Features

• Quantum-Resistant Authentication: Secure against future quantum computing threats
• Cross-Domain Authentication: Seamless authentication across different administrative domains
• Anomaly Detection: Federated learning-based detection of suspicious authentication patterns
• Privacy Preservation: Zero-knowledge proofs for minimal information disclosure
• Scalable Architecture: Hierarchical design for efficient large-scale deployment

Getting Started

Prerequisites

• Python 3.10 or later
• Required packages: numpy, matplotlib, cryptography, etc.

Installation

1. Clone the repository:

   git clone https://github.com/shafiqahmeddev/lqap-implementation.git
   cd lqap-implementation
   

2. Set up a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate
   

3. Install dependencies:

   pip install -r requirements.txt
   

4. Install the package in development mode:

   pip install -e .
   

Running the System:

1. Start the LQAP system:

   python run.py
   

2. Run the dashboard:

   python run_dashboard.py
   

3. Run a simulation:

   run_simulation.py
   

Testing and Evaluation

1. Run the system test:

   python test_system.py
   

2. Visualize anomaly detection:

   python visualize_anomalies.py
   

3. Benchmark performance:

   python benchmark.py
   

Acknowledgements

This project implements the architecture described in the paper "A Decentralized Blockchain-Based Federated Learning Architecture for Secure Multi-Domain V2G Networks" by Shafiq Ahmed and Mohammad Hossein Anisi.

Future Development Areas

Now that you have a complete working LQAP system, here are some directions for further development:

1. Performance Optimization: Identify and optimize bottlenecks in the system
2. Advanced UI: Develop a more sophisticated dashboard with additional visualizations
3. Hardware Integration: Explore options for integrating with real PUF hardware
4. Blockchain Enhancements: Implement more sophisticated consensus mechanisms
5. Advanced Federated Learning: Develop more sophisticated anomaly detection models
6. Mobile Application: Create a mobile app for EV users
7. Real-world Testing: Test the system in a real V2G environment

Which of these areas would you like to explore next?