Abstract:

A novel Controller Area Network (CAN) bus anomaly detection algorithm characterized by its adaptability to low anomaly traffic and strong generalization capability was proposed to enhance the safety of Intelligent Connected Vehicles (ICVs). The algorithm aimed to address potential and hard-to-detect abnormalities that may arise in vehicles, significantly improving the detection accuracy of anomalous data. This study explored the theoretical significance of Generative Adversarial Networks (GANs) and collected four types of attack data and two types of rare alarm data from an intelligent connected bus. The anomaly degree was assessed based on the reconstruction error of the computed data to validate the algorithm's adaptability. The results show that the proposed algorithm achieves an F1 score of 98.31% and a false positive rate of 2.90% on the low-traffic dataset Data4, surpassing the baseline model and the Deep Convolutional GAN (DCGAN) algorithm. Moreover, the false positive rate for rare alarm data is reduced to 3%, indicating that the algorithm is well-suited for low-traffic anomaly detection and exhibits strong generalization capabilities.

Key words: intelligent connected vehicles (ICVs), controller area network (CAN)-bus, anomaly detection, low-traffic anomaly, latent space distance