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

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.