Lightweight Identification of Malicious IoT Traffic via Cross-View Knowledge Distillation

Accurately identifying malicious traffic in heterogeneous IoT environments is critical for network security. Although deep learning-based methods can effectively extract multi-dimensional features and achieve high accuracy, deploying complex models on resource-constrained IoT devices remains challenging. To balance performance and efficiency, we propose IoT-CVKD, a novel malicious IoT traffic identification framework leveraging cross-view knowledge distillation. IoT-CVKD consists of a multi-view teacher model and a lightweight single-view student model. The teacher model characterizes heterogeneous traffic from different perspectives by capturing flow-level global and packet-level spatio-temporal local burst information, and efficiently fuses these features using a cross-attention mechanism. The student model, composed of lightweight and computationally efficient modules, takes only packet-level features as input. Multi-view knowledge from the teacher is then implicitly distilled into the student through cross-view knowledge distillation during training, thereby significantly enhancing the student’s classification capability. Extensive evaluations demonstrate that IoT-CVKD achieves superior classification performance compared to state-of-the-art methods while substantially reducing computational complexity, making it highly suitable for resource-constrained IoT deployments.