AI-Driven Threat Detection VANETs

Project Objectives

• Develop an AI-Based Threat Detection System
  • Design machine learning algorithms to detect anomalies in real-time, such as unauthorized access, spoofing, or data manipulation.
  • Implement a lightweight model suitable for processing data on limited-resource vehicular on-board units (OBUs).
• Implement a Blockchain-Enabled Trust Framework
  • Use blockchain to create a decentralized, immutable record of transactions within the network to prevent data tampering.
  • Enable trustless verification among vehicles, infrastructure nodes, and network servers to enhance message authenticity and reliability.
• Secure Inter-Vehicle Communication (IVC) and Vehicle-to-Infrastructure (V2I) Communication
  • Ensure secure communication protocols that can withstand attacks, particularly Man-in-the-Middle (MitM) and replay attacks.
  • Investigate the use of cryptographic mechanisms and secure routing protocols to maintain message integrity and confidentiality.

Methodology

• Data Collection and Preprocessing
  • Gather data from real-world VANET simulations and datasets, including traffic patterns, attack vectors, and normal driving behaviors.
• AI-Driven Threat Detection System
  • Use deep learning techniques such as Convolutional Neural Networks (CNNs) or Recurrent Neural Networks (RNNs) to analyze patterns and detect anomalies.
  • Implement lightweight AI algorithms optimized for resource-constrained vehicular hardware.
  • Employ federated learning to allow vehicles to collectively learn threat patterns without sharing raw data, preserving privacy.
• Blockchain-Based Trust Management System
  • Integrate a permissioned blockchain that allows verified nodes (e.g., vehicles and roadside units) to add new transactions.
  • Use smart contracts to automate trust verification between network nodes.
  • Apply consensus mechanisms optimized for VANETs, like Practical Byzantine Fault Tolerance (PBFT) or Proof of Authority (PoA), to reduce latency and energy consumption.
• Secure Communication Protocols
  • Implement secure message-passing protocols such as Elliptic Curve Cryptography (ECC) and Quantum-Resistant Cryptography (QRC) to future-proof communications.
  • Evaluate multi-hop communication protocols and detect routing misbehavior to ensure data integrity in relayed messages.

Future Trends and Challenges

• Scalability

  As the number of connected vehicles grows, VANETs must manage larger volumes of data while maintaining low latency. Optimizing AI and blockchain for scalability in highly dynamic environments is crucial.

• Quantum-Resistant Security

  With the rise of quantum computing, future VANET security must shift towards quantum-resistant cryptography to prevent future decryption of current encrypted communications.

• Edge Computing for Real-Time Processing

  Leveraging edge computing to process data locally on OBUs will reduce response times and minimize dependency on central servers, making VANETs more resilient to network failures.

• Interoperability and Standardization

  Ensuring seamless communication across diverse vehicular networks and adhering to emerging standards (such as IEEE 802.11p and 5G-based C-V2X) will be vital for wider VANET adoption.

• Privacy-Preserving Mechanisms

  As vehicles collect sensitive data, preserving driver privacy while sharing necessary information for safety remains a significant challenge. Techniques like differential privacy and homomorphic encryption can help address this.

• Energy-Efficient Solutions

  Given the limited energy resources of OBUs, designing low-power AI algorithms and lightweight blockchain frameworks is essential to extend device lifetimes and reduce operational costs.

Expected Outcomes

• Enhanced Security: The system should demonstrate improved detection and prevention of various VANET-specific attacks.
• Increased Trust: Blockchain will provide a reliable, decentralized trust model, reducing the likelihood of message tampering and unauthorized access.
• Scalable and Resilient VANETs: The project aims to contribute a scalable, real-time secure communication framework suitable for future VANET implementations.