The CAN bus is widely used in industrial data acquisition, Internet of Vehicles, and other fields, making its security intrusion detection very important.To comprehensively enhance the performance of the detection method, a bilinear self-attention mechanism for CAN bus intrusion detection is proposed. Firstly, based on the idea of stacked integration, DNN, CNN, and LSTM models are used to extract and generate deep learning layer feature data;Then, bilinear layers are used to generate self-attention mechanism and FNet feature data separately, which are then fused with deep learning layer feature data through a residual connection layer, and intrusion detection prediction is performed through a fully connected layer, demonstrating high accuracy, detection rate, and good generalization characteristics.Experiments on the Car_Hacking public dataset show that the accuracy, precision, recall, F1 score, and AUC values are 0.951, 0.996, 0.997, 0.960, and 0.984, respectively, and as the number of training epochs increases, the accuracy and loss value error remain within 5% and 10%, respectively, indicating that this method outperforms other comparison methods.Application to IoT experimental devices evaluation shows that this method achieves a detection rate of 99.23% for abnormal attack identification, which has significant promotion value for enhancing the security performance of monitoring and control systems.