Abstract：A vehicle network intrusion detection method is proposed based on CBAMTL-MobileNet V3. The lightweight MobileNet V3 model is used, and reduced its layers to improve both training and detection speeds. The squeeze-and-excitation (SE) modules in the model are replaced with convolutional block attention module (CBAM) to focus the model more on specific features, enhancing feature extraction capabilities and consequently improving the accuracy of attack detection. Transfer learning is introduced to fine-tune the model weights, reducing parameter and memory resource consumption, thereby shortening the training time and improving the computational speed of the model. Simulation results indicate that the proposed model is better than the MobileNet V3 model in various detection indexes. Compared with other models,the proposed model exhibits both the efficiency of a lightweight model and higher detection accuracy than other complex models, making it the optimal performer in recognizing various types of attacks.
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2024, Vol. 45 
