## 1 Introduction
This network is trained to identify known IoT devices and build an embedded vector database for them. Next, the embedding vectors of unknown IoT devices are extracted using the trained CSCL-Siamese network and checked against the embedded vector database. Finally, to integrate the clustering capabilities of multiple unsupervised clustering algorithms, we improve the voting ensemble strategy by employing weighting factors. This approach combines multiple unsupervised clustering algorithms to perform the clustering-ensemble identification of unknown IoT device embedding vectors, thereby helping identify various types of unknown IoT devices. In summary, we propose a method for UDI based on CSCL-Siamese networks and a weighted-voting clustering ensemble (WVE). The CSCL-WVE-UDI method generates contrastive training data by combining data visualization techniques with a permutation sample-pairing strategy and uses cost-sensitive factors and a contrastive loss function to design the CSCL-Siamese network for training on the contrastive data, thus achieving known IoT device identification and realizing an embedded vector database. Next, the CSCL-Siamese network and database were used to extract the embedding vectors of unknown IoT devices. Finally, a clustering-ensemble learning algorithm was proposed using weighting factors and a voting ensemble strategy to cluster the embedding vectors of unknown IoT devices, thus enabling the identification of various unknown IoT devices. The main contributions of this paper are as follows:  
1. An algorithm for generating PNSPs, which establishes a set of PNSPs by combining data visualization techniques with a permutation sample-generation strategy, is proposed. This algorithm ensures that each sample is fully utilized, thus mitigating the issue of reduced identification performance caused by underutilized datasets. 2. A Siamese network based on cost-sensitive contrastive loss (CSCL-Siamese) is presented. By improving the classical contrastive loss function, using the Manhattan distance as the similarity metric, and leveraging the disparity between counts of positive and negative sample pairs, this approach assigns greater weighting factors to the positive sample pairs to increase their loss cost, thus addressing the issue of decision boundary bias toward negative sample pairs caused by an insufficient number of positive sample pairs. 3. A UDI algorithm based on a WVE (WVE-UDI) is developed. By combining weighting factors with a voting ensemble strategy, this algorithm integrates the clustering results of multiple unsupervised clustering algorithms, thus overcoming the limitations of individual clustering algorithms and improving the capability for identifying unknown IoT devices. 4. Finally, the algorithms above are integrated into the CSCL-WVE-UDI method for UDI. Experimental results show that the CSCL-WVE-UDI method can effectively identify multiple types of unknown IoT devices. The rest of this paper is organized as follows. Section 2 reviews the existing methods for IoT device identification, discusses their limitations, and highlights opportunities for improvement through collaborative learning and clustering ensembles. Section 3 shows the CSCL-WVE-UDI scheme, detailing its logical architecture and workflow. Section 4 performs an evaluation of the prototype on benchmark datasets (Aalto, UNSW, LSIF) and compares it with the other methods. Section 5 derives the conclusions.