Securing Autonomous Vehicles Using AI-Blockchain Architecture

Abstract

Autonomous vehicles (AVs) promise safer and more efficient mobility but are exposed to an evolving attack surface spanning on-board sensors, in-vehicle networks, over-the-air (OTA) software updates, and vehicle-to-everything (V2X) communications. Traditional perimeter defenses and centralized public-key infrastructures (PKIs) struggle to keep pace with adversarial machine learning (ML), Sybil attacks in vehicular networks, and supply-chain compromises. This manuscript proposes an end-to-end security architecture that fuses artificial intelligence (AI) with a permissioned blockchain to deliver verifiable identity, tamper-evident telemetry, decentralized policy enforcement, and adaptive intrusion detection. The architecture binds device and model identities to decentralized identifiers (DIDs), anchors data and update artifacts to an immutable ledger, and coordinates a privacy-preserving federated-learning loop to continuously refine anomaly detectors. We describe the threat model and system components (edge validators, roadside units, OEM validators, on-vehicle guardians), define a trust and attestation workflow, and integrate AI modules for CAN-bus anomaly detection and V2X trust scoring. A simulation study using realistic traffic patterns evaluates detection efficacy, latency, and ledger throughput under benign and adversarial conditions (false-data injection, spoofing, replay, and Sybil attacks). Statistical analysis across 30 independent runs shows significant improvements in true positive rate (TPR), false positive rate (FPR), and end-to-end decision latency compared with a PKI-only baseline, while maintaining transaction throughput suitable for V2X policy events. We conclude that AI-Blockchain co-design can harden AV ecosystems by coupling adaptive detection with cryptographic accountability, and we discuss deployment considerations, limitations, and paths to certification-grade assurance.