IoT-SCNet: Semi-Supervised Contrastive Network Traffic Images Learning for IoT Device Identification

The widespread deployment of Internet of Things (IoT) devices has made them vulnerable targets for cyber attacks, highlighting the great significance of IoT device identification for network security management. Existing studies have primarily focused on either manual extraction of excessive network traffic features or heavy reliance on labeled data. To address these limitations, we propose a novel IoT device identification approach (named IoT-SCNet) via semi-supervised contrastive learning of network traffic visual representations. Specifically, IoT-SCNet converts two packet-level features and raw traffic into network traffic images of each device, and applies three augmentation strategies designed for network traffic to construct semantically meaningful positive/negative image pairs. By deep neural networks automatically capturing discriminative patterns and a contrastive task, IoT-SCNet achieves effective identification of diverse IoT device types. Comprehensive evaluations across three benchmark datasets demonstrate the superior performance of IoT-SCNet, achieving remarkable identification accuracies of 99.83% on UNSW, 97.50% on Yourthings, and 99.34% on CIC IoT datasets.