To solve the problems that denial of service (DoS) attacks in the Internet of Vehicles are difficult to prevent and the existing supervised learning methods cannot effectively detect zero-day attacks, this study proposes a hybrid DoS attack intrusion detection system. Firstly, the dataset is preprocessed to improve data quality. Secondly, feature selection is used to filter out redundant features, which aims to obtain more representative features. Thirdly, the ensemble learning method is used to integrate five tree-based supervised classifiers through stacking to detect known DoS attacks. Finally, an unsupervised anomaly detection method is proposed, which combines the convolutional denoising autoencoder with the attention mechanism to establish a normal behavior model. It is used to detect unknown DoS attacks that are missed by stacking ensemble models. Experimental results show that for the detection of known DoS attacks, the detection accuracy of the proposed system on the Car-Hacking dataset and the CICIDS2017 dataset is 100% and 99.967%, respectively. For the detection of unknown DoS attacks, the detection accuracy of the proposed system on the above two datasets is 100% and 83.953%, respectively, and the average test time on the two datasets is 0.072 ms and 0.157 ms, respectively, which verifies the effectiveness and feasibility of the proposed system.