## 2 Literature Review  
To identify unknown IoT devices in open environments, the author of [4] proposed an IoT-Siamese method for identifying unknown IoT devices. The IoT-Siamese method first reconstructs raw traffic packets into continuous flow data and removes all link-layer fields from the flow. Next, it extracts the first 100 bytes of each packet to construct device fingerprints. Subsequently, a Siamese network is trained on the fingerprints of known IoT devices, and the trained model is saved to establish an embedded vector database for known IoT devices. When new traffic data are received, they are input into the pretrained Siamese network to obtain embedding vectors, which are then checked against the known IoT device embedded vector database by calculating the shortest average similarity distance to determine if the device is unknown. Experimental results show that, even for small traffic volumes, the IoT-Siamese method can effectively identify IoT devices, although the identification accuracy for some unknown IoT devices is less than 50%. Furthermore, this method requires training a separate Siamese network for each new unknown IoT device, significantly increasing the training overhead. The author of [5] proposed a UDI method based on Siamese networks. Specifically, a Siamese network is utilized to learn the relationships between known IoT devices and generate an embedded vector databaseof known IoT devices. When an unknown IoT device joins the network, its traffic data are input into the pretrained Siamese network to generate embedding vectors, which are then matched with the database using the nearest distance, thus reducing the necessity for frequent model retraining due to device updates. The author of [6] evaluated the performance of the Siamese network on different datasets and verified its ability to identify IoT devices with different traffic patterns when new devices are added to the network. However, a significant drawback of the methods presented in [5,6] is their inability to determine the number of unknown IoT device types in the network under the participation of multiple unknown devices. Although these methods can identify unknown IoT devices, they require training separate network models for each device type to distinguish specific types. To identify different types of IoT devices, the author of [7] proposed a method for identifying unknown IoT devices based on the KNN algorithm. First, the network traffic of IoT devices was truncated into 784-bit session data and then converted into 28 \(\times\) 28 images as device fingerprints. Subsequently, an auxiliary classifier generative adversarial network was used for data augmentation on the image fingerprints. A CNN model was trained with a triplet loss-minimization function to generate embedding vectors. Finally, the KNN algorithm was employed to train the embedding vectors of multiple types of unknown IoT devices. This approach achieved an identification accuracy of up to 90% for some devices. However, the KNN algorithm requires prior knowledge of the true labels and number of unknown IoT devices for training before classification can be performed. In actual scenarios, administrators cannot determine the labels or number of unknown IoT devices in advance, thus limiting the method's effectiveness. The author of [8] introduced a hybrid deep learning method to identify unknown IoT devices. The core approach was classified into three steps: first, for each type of known IoT device, a separate network model was trained and saved appropriately; second, when new traffic data were encountered, they were matched with the saved models individually to filter out the embedding vectors of unknown IoT devices; third, for these unknown devices, the data were compressed using an autoencoder, and density-based clustering recognition was performed using the OPTICS algorithm. However, as this method requires training a separate identification model for each known device type, the training cost increases exponentially with the number of known IoT devices.