基于MobileViT轻量化网络的车载CAN入侵检测方法OA北大核心CSTPCD

The controller area network(CAN)bus in the vehicle is vulnerable to attacks due to the lack of security measures.Therefore,intrusion detection systems(IDS)play an important role in protecting the CAN bus in the vehicle from network attacks.The existing vehicle CAN bus intrusion detection methods based on deep learning have the problems of high resource consumption and high latency.To reduce detection latency and improve detection rate,an improved lightweight MobileViT model is proposed for intrusion detection on the vehicle CAN bus.First,visualize the attack traffic as a color map,and then use GELU to replace the regular ReLU6 in the MV2 module in MobileViT,which serves as the activation function of the MV2 module,effectively solving the problem of neuron death and improving the convergence speed of the MobileViT mode.Use exponential decay to automatically update the learning rate and accelerate the training process through transfer learning to implement color image classification so as to achieve intrusion detection.Experiments based on the CAR-HAKING DATASET show that the improved MobileViT has a detection accuracy of 100％for intrusion behavior with less computational power consumption,model parameters of only 2.12 MB,and an average response time of only 1.6 ms,saving training resources and ensuring detection accuracy.