Design and Optimization of a Blockchain‐Enabled Decentralized Security Framework for Anomaly Detection in <scp>VANETs</scp>

ABSTRACT Vehicular ad‐hoc networks (VANETs) are critical to intelligent transportation systems yet inherently vulnerable to security threats, including data falsification, message tampering, and denial‐of‐service attacks. Traditional centralized security mechanisms exhibit limitations in scalability, trustworthiness, and resilience against single points of failure. To address these challenges, this study proposes a blockchain‐enabled decentralized framework integrating machine learning for anomaly detection in VANETs. The proposed model employs a multi‐layer blockchain architecture to establish transparent and immutable trust among participating nodes, with smart contracts automating security policies, intrusion response, and vehicle reputation management. Machine learning algorithms are incorporated to analyze spatiotemporal traffic patterns, detect abnormal behaviors such as malicious message injection and position spoofing, and differentiate legitimate nodes from intruders. Furthermore, simulation results demonstrate that the proposed framework achieves dual optimization objectives: (1) consensus efficiency improvements reducing verification latency by ≥ 40% through Practical Byzantine Fault Tolerance enhancements; and (2) detection accuracy enhancements reaching 94.2% precision in identifying sophisticated attacks—while maintaining efficient scalability for large‐scale VANET environments. This research highlights the synergy of blockchain and machine learning in building a secure, decentralized, and intelligent vehicular network infrastructure, contributing to the safe deployment of next‐generation intelligent transportation systems.