Current methods for identifying unknown Internet of Things (IoT) devices are relatively limited. Most approaches can identify only one type of the unknown IoT devices at a time and with a relatively low accuracy. Herein, we propose a method for unknown IoT device identification (UDI) based on cost-sensitive contrastive loss (CSCL)-Siamese networks and a weighted-voting clustering ensemble (WVE). First, we integrate data visualization techniques with a permutation sample-pairing strategy to generate a complete and nonredundant set of positive–negative sample pairs. Then, we present an algorithm to generate permutation positive–negative sample pairs to provide a rich set of contrastive training data. To overcome the bias in the decision boundary caused by an insufficient number of positive sample pairs, we developed a Siamese network based on CSCL. The CSCL-Siamese network is used to identify known IoT devices and establish an embedded vector database for known IoT devices. Next, we extract the embedding vectors of unknown IoT devices using the trained CSCL-Siamese network and the embedded vector database. Finally, combining weighting factors with a voting ensemble strategy, we develop a UDI algorithm based on a WVE. This presented algorithm integrates the clustering capabilities of multiple unsupervised clustering algorithms to perform clustering on the extracted embedding vectors of unknown IoT devices, thereby enhancing the identification capability of the CSCL-WVE-UDI method. Experimental results demonstrate that the CSCL-WVE-UDI method can effectively identify multiple types of unknown IoT devices at the same time.