<p>Ensuring the cybersecurity of modern vehicles is paramount as connected and autonomous systems become increasingly prevalent. However, existing intrusion detection systems (IDS) often face challenges such as imbalanced datasets and high computational demands, limiting their practical deployment in automotive environments. To address these limitations, we employ spectral normalization GAN to synthesize anomalous data, achieving a balanced distribution across four attack categories and normal traffic. We further propose a lightweight classification model, named Depthwise Separable Convolutional Kolmogorov–Arnold network (DSC-KAN), which incorporates Kolmogorov–Arnold (K–A) theorem to enhance efficiency while maintaining high classification performance. Experimental results demonstrate that our approach outperforms existing methods in accuracy and computational efficiency, offering a robust and practical IDS solution. The proposed method has the potential to significantly improve vehicle network security, ensuring safer and more reliable deployment of connected and autonomous driving technologies.</p>