LAUSR

Accurately identifying IoT device types is crucial for IoT security and resource management. However, existing traffic-based device identification algorithms incur high measurement, storage, and computation costs, as they continuously need to capture, store, and parse device traffic. To overcome these challenges, we propose an innovative framework that employs a discontinuous traffic measurement strategy, reducing the number of packets captured, stored, and parsed. To ensure accurate identification, we introduce several novel techniques. First, we propose a graph neural network-based tensor completion model to estimate missing traffic features in unmeasured time slots. Our model can utilize historical information to flexibly and efficiently estimate missing features. Second, we propose a convolutional neural network-based classifier for device identification. The classifier utilizes traffic features and node embeddings learned from the tensor completion model to achieve precise device identification. Through extensive experiments on real IoT traffic traces, we demonstrate that our framework achieves high accuracy while significantly reducing costs. For instance, by capturing only 30% of the packets, our framework can identify devices with a high accuracy of 0.9558. Moreover, compared to current tensor completion methods, our method can estimate missing values with higher accuracy and achieve a 1.53-fold speedup over the next-fastest baseline.