To address the common issues of complex architecture, high resource consumption, and significant latency in existing deep learning-based CAN bus intrusion detection methods, this paper proposes a lightweight CAN bus intrusion detection model based on an improved SqueezeNet architecture. First, CAN message data is converted into color images to enhance spatial and channel feature representation. Second, an efficient channel attention (ECA) mechanism is introduced to enable fine-grained modeling of anomalous communication patterns. Third, the network architecture is optimized by replacing standard convolutions with deep separable convolutions and Ghost modules, while pruning redundant layers to reduce computational overhead and parameter count. Finally, the Hardswish activation function is uniformly applied to enhance nonlinear expressiveness and inference efficiency. Experimental results on the Car-Hacking public dataset demonstrate that the proposed method achieves 100% detection accuracy with a model size of only 0.35 MB and an average response time of 1.6 ms, offering deployment advantages of high performance, low latency, and minimal resource consumption.