V2XFormer: Transformer-Based Anomaly Detection for Vehicle-to-Everything Communication

The Internet of Vehicles (IoV) has transformed intelligent transportation systems through vehicle-to-everything (V2X) communication, improving road safety and traffic efficiency. However, the dynamic nature of vehicular networks, with high mobility and shared wireless resources, makes them vulnerable to attacks like Denial of Service (DoS). Anomaly detection (AD) has proven effective in detecting such threats. Yet, V2X communication occurs in diverse environments with varying network coverage and vehicle speeds, leading to domain shifts and variations in feature distributions that can hinder the generalization performance of traditional anomaly detection models. To address these challenges, this paper presents V2XFormer, an unsupervised anomaly detection system based on transformer neural networks, designed to identify anomalies in V2X communication. Additionally, we introduce TV2XFormer, which integrates transfer learning to enhance adaptability across diverse network conditions and environmental variations in V2X communication. We assess the performance of the proposed approaches using the VDoS-LRS V2X dataset, employing precision, recall, and $\mathbf{F 1}$ score metrics. A comparison is made with five state-of-the-art unsupervised AD algorithms. Experimental results demonstrate that both V2XFormer and TV2XFormer outperform the competing algorithms, achieving the highest $\mathbf{F 1}$ scores (1.0). Furthermore, TV2XFormer exhibits notable robustness and generalizability to dynamic vehicular environments.