Diffusion Multimodal Distillation Collaboration: A Generative Equilibrium Framework for Efficient Vehicle Networking Intrusion Detection

Real-time intrusion detection with millisecond response is critical for Internet of Vehicles (IoV) security but is challenged by extreme class imbalance and high computational costs. This paper proposes a novel multimodal framework integrating Denoising Diffusion Probabilistic Models (DDPM) and Knowledge Distillation (KD). First, multi-source data is transformed into RGB images. A conditional DDPM with timestep and class embeddings balances datasets by generating minority-class samples. The teacher model (DiffuGuardian) fuses text-image features for training. Subsequently, a lightweight student model, LiteSentinel, is designed employing depthwise separable convolutions and inverted residual blocks to reduce parameters. Results on three datasets demonstrate that DiffuGuardian consistently achieves around 98–100% precision, accuracy, recall, and F1-score under 5-fold evaluation, while LiteSentinel maintains approximately 95–99% across all metrics with substantially reduced complexity. DiffuGuardian reaches an inference time of 3.80ms with a model size of 0.10 MB, whereas LiteSentinel further reduces latency to 0.79ms with a size of 0.07 MB, enabling efficient edge deployment for IoV security.