Anomaly Detection of VANET Vehicle Communication Channel Driven by Artificial Intelligence

This research introduces an AI‐powered adaptive spatio‐temporal anomaly detection framework (AST‐ADF) to address vulnerabilities in vehicular ad hoc networks (VANETs), which are susceptible to spoofing, message tampering, and denial‐of‐service attacks. The framework integrates three intelligence layers: (i) spatio‐temporal feature extraction using graph convolutional networks to process mobility traces, channel states, and packet statistics, (ii) an adaptive deep detection layer that uses a hybrid CNN‐BiLSTM model for capturing sequential dependencies and local fluctuations in communication channels, and (iii) a self‐learning anomaly refinement module using reinforcement learning for dynamic detection updates. AST‐ADF reduces false alarms, adapts to environmental changes, and provides robust detection against zero‐day anomalies, outperforming static models. Simulation experiments in real VANET environments show over 96% detection accuracy with only 3% false positives. The framework's minimal computational overhead makes it suitable for vehicular edge devices and roadside infrastructure. AST‐ADF demonstrates 97%–95% accuracy, 2.8%–3.5% false positives, 96%–93% precision, 97%–94% recall, and strong zero‐day adaptability. Unlike previous AI‐based detection frameworks, AST‐ADF unifies spatial and temporal correlations, significantly improving robustness against noise and adversarial interference. Additionally, it supports real‐time deployment with efficient inference through model pruning and edge optimization techniques, ensuring secure, reliable, and intelligent VANET communication for future transportation systems.