STAP Journal of Security Risk Management

ISSN: 3080-9444 (Online)

Adaptive and Context-Aware Authentication Framework Using Edge AI and Blockchain in Future Vehicular Networks

by 

Aitizaz Ali

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Published: 2024/11/16

Abstract

The rise of connected and autonomous vehicles (CAVs) within intelligent transportation systems has introduced new demands for real-time, scalable, and privacy-preserving authentication mechanisms. Traditional authentication methods, such as Public Key Infrastructure (PKI), are often insufficient in highly dynamic vehicular environments due to their reliance on static credentials and centralized control. This paper proposes an adaptive and context-aware authentication framework that integrates Edge Artificial Intelligence (AI) with blockchain technology to secure vehicular communication. The framework leverages edge- based AI models to assess driver behavior and contextual signals in real time, generating dynamic trust scores for authentication. These scores are verified and recorded through a permissioned blockchain, ensuring tamper-proof identity validation and decentralized access control. The proposed system addresses key challenges including low latency, dynamic trust evaluation, and conditional privacy. Through detailed architectural design and security analysis, this work highlights the potential of hybrid AI-blockchain models to enhance the security, scalability, and accountability of future vehicular networks.

Keywords

Vehicular networksFederated learningV2X securityEdge AIBlockchain authenticationcontext-aware systemsintelligent transportation systemsreal-time authenticationprivacy preservationdecentralized identityfuture mobility6G networks

Adaptive and Context-Aware Authentication Framework Using Edge AI and Blockchain in Future Vehicular Networks is licensed under CC BY 4.0CCBY

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