With the development of intelligent transportation systems, vehicles are exposed to a complex network environment. As the main network of in-vehicle networks, the controller area network (CAN) has many potential security hazards, resulting in higher requirements for intrusion detection systems to ensure safety. Among intrusion detection technologies, methods based on deep learning work best without prior expert knowledge. However, they all have a large model size and rely on cloud computing, and are therefore not suitable to be installed on the in-vehicle network. Therefore, we propose a lightweight parallel neural network structure, LiPar, to allocate task loads to multiple electronic control units (ECU). The LiPar model consists of multi-dimensional branch convolution networks, spatial and temporal feature fusion learning, and a resource adaptation algorithm. Through experiments, we prove that LiPar has great detection performance, running efficiency, and lightweight model size, which can be well adapted to the in-vehicle environment practically and protect the in-vehicle CAN bus security.

翻译：随着智能交通系统的发展，车辆暴露于复杂的网络环境中。作为车载网络的主要网络，控制器局域网络（CAN）存在诸多潜在安全隐患，这对入侵检测系统保障安全性提出了更高要求。在入侵检测技术中，基于深度学习的方法无需先验专家知识且效果最佳。然而，这些模型体积庞大且依赖云计算，因此不适用于车载网络环境。为此，我们提出了一种轻量级并行神经网络结构LiPar，将任务负载分配至多个电子控制单元（ECU）。LiPar模型由多维度分支卷积网络、时空特征融合学习以及资源自适应算法构成。实验证明，LiPar具有优异的检测性能、运行效率及轻量化模型规模，能够实际适配车载环境，有效保障车载CAN总线安全。