Abstract: Zero-day attack detection in the Internet of Vehicles usually adopts anomaly-based methods due to the limited availabi-lity of attack data.Nevertheless,the complex and diverse driving environments that vehicles operate in,coupled with the variability of behavioral patterns,resulting in significant deviations in normal behavior.As a consequence,the utilization of anomaly-based methods tends to yield elevated false alarm rates.In the vehicular context,the attack principles of zero-day and known attacks exhibit similarities.Drawing inspiration from transfer learning,a zero-day attack detection method for the Internet of Vehicles is introduced,which is grounded in few-shot learning and employs conditional generative adversarial networks(CGANs).Specifically,a conditional adversarial generative network model is proposed featuring multiple generators and multiple discriminators.Within this framework,an adaptive sampling data augmentation method is developed to enhance the dataset with known attack samples.This augmentation is achieved through the optimization of input samples to effectively reduce the occurrence of false positives.Furthermore,to address the data imbalance issue stemming from a limited number of input attack samples,a collaborative focus loss function is incorporated into the discriminators,with an emphasis on distinguishing challenging-to-classify data.The effectiveness of the proposed method is rigorously assessed through comprehensive experiments conducted on the F2MD vehicle network simulation platform.The experimental results unequivocally establish the superiority of the proposed approach compared to existing methods,both in terms of detection efficacy and latency.As a result,this paper presents an effective solution for zero-day attack detection in the realm of the Internet of Vehicles.

Key words: Internet of Vehicles, Zero-day attack, Conditional generative adversarial network, Few-shot learning, Anomaly detection