A Lightweight Intrusion Detection System for Vehicular Networks Based on an Improved ViT Model

As the connectivity between Electronic Control Units (ECUs) and the external environment intensifies, the security and safeguarding of In-Vehicle Networks (IVNs) have become pressing issues. The Controller Area Network (CAN) bus, the most commonly employed vehicular network protocol, is particularly vulnerable due to its inherent lack of security measures, making it susceptible to various attacks. In this paper, we introduce a novel intrusion detection model for CAN networks, named IVN-ViT. The proposed IVN-ViT model utilizes an enhanced Vision Transformer architecture (Edge-ViT), incorporating self-supervised learning with Cutout data augmentation, Particle Swarm Optimization (PSO) for feature selection, and a fine-tuning strategy that merges Parameter Instance Discrimination (PID) with supervised loss. This not only enhances the model's convergence speed and predictive accuracy but also meets the lightweight requirements of CAN networks. Experimental results on the "HCRL-car hacking" dataset and the "X-CANIDS" dataset demonstrate that our model achieves over 99% accuracy across all types of attacks. In a simulated vehicular environment, the detection latency for each message averages approximately 1.04ms, fully meeting the first-time requirements of CAN networks. The experiments validate that our proposed method significantly improves model accuracy while ensuring efficient operation within the limited computational resources available in vehicle systems.