Resilient Security Frameworks for 6g Vehicular Iot: Integrating Blockchain, Ai-Driven Ids and Ethical Governance

Abstract

The fast development of 6G-enabled vehicular Internet of Things (IoT) brings new opportunities of autonomous mobility, cooperative perception, and ultra-reliable communication, but also subjects vehicles to multi-layered, multi-faceted cyber threats. This paper is a cohesive investigation of security, privacy, and trust issues of next-generation vehicular networks and how blockchain, artificial intelligence (AI), and distributed edge architectures can work together to improve resilience. It initially presents the categorization of vehicular threats in terms of authentication, data integrity, availability, privacy, and physical tier, using real-life examples, including the Jeep Cherokee hack and GPS spoofing, to explain how severe the attacks can be practical. It next discusses blockchain-based trust models, which play roles in the decentralized management of identities, the sharing of data without any tampering, and the use of smart contracts to enable automation. Moreover, it examines the AI-driven intrusion detection systems that are based on machine learning, deep learning, federated learning, and reinforcement learning to identify the changing attacks in real-time. The combination of blockchain and AI is discussed as the synergistic model that can be used to secure trust, maintain integrity, and enhance adaptive threat identification. To make sure of responsible deployment, ethical and regulatory factors, such as privacy protection, liability, fairness, transparency, and global standardization, are addressed. This article presents a comprehensive background to the creation of a secure, trustful, and future-resistant vehicular ecosystem during the 6G era with its insights on architecture, case studies, and a pseudo-coded IDS framework.