Intrusion detection is an important defensive measure for automotive communications security. Accurate frame detection models assist vehicles to avoid malicious attacks. Uncertainty and diversity regarding attack methods make this task challenging. However, the existing works have the limitation of only considering local features or the weak feature mapping of multifeatures. To address these limitations, we present a novel model for automotive intrusion detection by spatial–temporal correlation (STC) features of in-vehicle communication traffic (intrusion detection system [IDS]). Specifically, the proposed model exploits an encoding-detection architecture. In the encoder part, spatial and temporal relations are encoded simultaneously. To strengthen the relationship between features, the attention-based convolutional network still captures spatial and channel features to increase the receptive field, while attention-long short-term memory builds meaningful relationships from previous time series or crucial bytes. The encoded information is then passed to detector for generating forceful spatial–temporal attention features and enabling anomaly classification. In particular, single-frame and multiframe models are constructed to present different advantages, respectively. Under automatic hyperparameter selection based on Bayesian optimization, the model is trained to attain the best performance. Extensive empirical studies based on a real-world vehicle attack data set demonstrate that STC-IDS has outperformed baseline methods and obtains fewer false-alarm rates while maintaining efficiency.

The existing trajectory prediction is mainly for specific road sections, which is poor to adapt to complex and changing traffic scenarios. Meanwhile, decentralized trajectory data is hard to be fully utilized in the data silo environment. To solve data silos in the intelligent vehicle industry, introducing federated learning methods in vehicular edge computing has attracted extensive attention. But traditional federated learning still has the potential to suffer from the mining of training data in the case of model privacy leakage. In this paper, a vehicle trajectory prediction method based on federated learning and homomorphic encryption has been presented, which adopts a three-layer architecture with a vehicle cluster, edge computing server, and cloud core network. Compared with traditional centralized deep learning methods, this approach enables joint modeling of multiple parties to improve the model's generalization performance. We used a proxy re-encryption algorithm to implement key distribution and also designed an encrypted federated network algorithm FAHEFL, which uses FAHE1 homomorphic encryption to protect the privacy of the model in parameter transmission. Each local model includes a driving behavior recognition module and trajectory output module. The driving behavior recognition module uses a 1D convolutional neural network to recognize the driving behavior, then input recognition results and historical trajectories to the trajectory output module, which uses LSTM neural network to output predicted trajectories. The experiment results show that the model built with FAHEFL has no more 1% error in the driving behavior recognition module than concentrated learning, while the minimum mean square error of the trajectory prediction module increased by only 2.5%. This article also discusses the performance between FAHEFL and well-known cryptographic federation network algorithms.