Blockchain driven trust management for intelligent transportation systems in VANETs.

Area of Science:

• Computer Science
• Network Security
• Intelligent Transportation Systems

Background:

• Vehicular Ad Hoc Networks (VANETs) are crucial for Intelligent Transportation Systems (ITS), enabling vehicle-to-everything (V2X) communication.
• Robust security is essential for VANETs to ensure traffic efficiency and road safety while protecting user privacy.
• Existing blockchain-VANET solutions often address security aspects in isolation, lacking a comprehensive approach.

Purpose of the Study:

• To propose a unified trust management framework for VANETs that integrates lightweight authentication, efficient consensus, and advanced intrusion detection.
• To enhance the security, resilience, and efficiency of VANETs against malicious attacks and network vulnerabilities.
• To validate the proposed framework's performance through simulations and comparisons with existing models.

Main Methods:

• Integration of Physical Unclonable Functions (PUFs) for lightweight node authentication.
• Implementation of an Enhanced Quantified role-based Practical Byzantine Fault Tolerance (EQPBFT) consensus mechanism for secure block creation.
• Development of a Deep Neural Network (DNN)-based intrusion detection system for enhanced threat classification.

Main Results:

• The EQPBFT algorithm demonstrated improved network resilience by efficiently identifying and mitigating malicious nodes.
• The DNN-based intrusion detection system achieved high threat classification and attack detection accuracy (95%).
• Simulations showed reduced computation time (up to 25% lower than PBFT) and faster block creation (5.2 ms vs. 6.6 ms for 200 nodes).

Conclusions:

• The proposed unified framework effectively enhances VANET security and efficiency by combining PUFs, EQPBFT, and DNN-based intrusion detection.
• The system offers a robust and efficient solution for trust management in VANETs, outperforming baseline models in key performance metrics.
• The findings validate the framework's potential for improving the overall safety and performance of intelligent transportation systems.