## 1 Introduction  
According to the International Data Corporation (IDC), the world's total data volume will reach 175 zettabytes (zettabytes) by 2025. About 80% of these data is generated by smart sensors, mobile terminals, industrial devices, etc. [1]. This development trend highlights the immense potential of the IoT device market and suggests that IoT technology will revolutionize multiple industries. However, both academia and industry agree that IoT devices have weak security defense capabilities owing to their low computing power and limited storage capacity [2]. This inherent limitation of IoT devices is one of the main causes of information and network security risks in IoT systems, particularly in areassuch as cross-network attacks targeting IoT devices connected to the Internet, IoT-botnet attack detection, and network-attack traceability. Accurate identification of IoT devices is critical in these contexts because appropriate network defense strategies and technical methods can only be selected through the accurate identification of device types and their communication protocols. Nevertheless, most current IoT device identification methods focus on recognizing known IoT devices in closed environments and struggle to adapt to the continuously evolving open environments. Furthermore, these methods fail to automatically update their models to identify unknown IoT devices [3], thus highlighting the need for conducting studies on the identification of unknown IoT devices. Researchers have conducted a series of studies in recent years to identify unknown IoT devices in open environments. The authors of [4; 5] adopted Siamese networks as the training model. They first identified known IoT devices and constructed an embedded vector database for them. Subsequently, the embedding vectors of unknown IoT devices were checked against the database, with identification performed based on a predefined similarity distance. The author of [6] demonstrated the capability of Siamese networks in reducing model retraining. To identify unknown IoT devices, the author of [7] employed a convolutional neural network (CNN) model and a triplet loss-minimization function to generate embedding vectors of unknown IoT devices; subsequently, they classified the vectors using the k-nearest neighbors (KNN) algorithm. The author of [8] proposed creating a separate identification model for each type of known IoT device, which was matched against unknown IoT devices to filter out their traffic data. The filtered data were further clustered using the ordering points to identify the clustering structure (OPTICS) algorithm to identify unknown IoT devices. However, the methods above for unknown IoT device identification (UDI) present several limitations. In [4; 5], the reliance on similarity distances for identifying unknown IoT devices limits the ability to distinguish multiple types of unknown IoT devices in one attempt. Although the KNN algorithm employed in [7] can identify multiple types of unknown IoT devices, it requires prior knowledge regarding the number of unknown devices and their corresponding data, which is typically unavailable in open environments. In [8], considering the identification model was trained for each IoT device individually, the training overhead increased exponentially with the number of IoT devices. Moreover, these methods generally overlook the learning potential of multiple clustering algorithms and fail to effectively integrate them for the clustering-ensemble identification of unknown IoT devices. To effectively identify various unknown IoT devices in open environments, we first combined data visualization techniques with a permutation sample-pairing strategy to generate a set of permutation positive-negative sample pairs (PNPS). Then, to mitigate the bias in the decision boundary caused by the insufficient number of positive sample pairs, we cast cost-sensitive factors into the contrastive loss function to establish a Siamese network based on cost-sensitive contrastive loss (CSCL).