LiPar: A Lightweight Parallel Learning Model for Practical In-Vehicle Network Intrusion Detection

Aiheng Zhang, Zhen Sun, Qiguang Jiang, Kai Wang, Ming Li, Bailing Wang

Abstract: With the development of intelligent transportation systems, vehicles are exposed to a complex network environment. As the mainstream in-vehicle network (IVN), the controller area network (CAN) has many potential security hazards. Existing deep learning-based intrusion detection methods have security performance advantages, however, they consume too much resources and are therefore not suitable to be directly implemented into the IVN. In this paper, we explore computational resource allocation schemes in the IVNs and propose the LiPar, which is a parallel neural network structure using lightweight multi-dimensional spatial and temporal feature fusion learning to perform intrusion detection tasks in the resource-constrained in-vehicle environment. In particular, LiPar adaptively allocates task loads to in-vehicle computing devices, such as multiple electronic control units, domain controllers, and computing gateways by evaluating whether a computing device is suitable to undertake the branch computing tasks according to its real-time resource occupancy. Experiment results show that LiPar achieves better detection performance, running efficiency, and optimized lightweight model size over existing methods, and can be well adapted to the resource-constrained in-vehicle environment and practically protect the in-vehicle CAN bus security. Code is available at https://github.com/wangkai-tech23/LiPar

External IDs:doi:10.1109/tits.2025.3605465