## 4 Experimental Results and Analysis
### Experiments on the Efficiency of the CSCL-WVE-UDI Methodology  
In order to verify the efficiency of the CSCL-WVE-UDI method, it is compared with the literature [4; 5; 7]'s methods, which are more effective in recognizing unknown IoT devices. This experiment is conducted in an environment where three unknown IoT devices coexist, where it is difficult for the literature [4; 5]'s method to recognize multiple unknown IoT devices at one time; therefore, in order to achieve the recognition of three unknown IoT devices by the literature [4; 5]'s method, separate recognition models need to be trained for each unknown IoT device. Considering that the network traffic in the IoT device-user interaction scenario is more representative [25], the UNSW dataset is chosen for this experiment. In addition, three unknown IoT devices are selected as the experimental objects for this experiment; firstly, because the literature [7] has referred to the practice of randomly selecting three unknown IoT devices from the UNSW dataset, and secondly, because the three unknown IoT devices not only ensure that the experiments are moderately complex, but also are sufficient to effectively compare the recognition effects of different methods [7]. The results of the comparison of the recognition accuracy of each method are shown in Figure 10.  
As can be seen from Figure 10, in the scenario where the three unknown IoT devices coexist, the overall performance of the CSCL-WVE-UDI method is relatively better, and it is able to achieve a high recognition effect. In particular, it achieves 89.6% and 100% recognition accuracy for Belkin Wemo Switch device and Inteon Camera device, respectively, but there are still deficiencies, as follows: (1) Compared with literature [4; 5], which uses the shortest similarity distance as a recognition method for unknown IoT devices, the recognition accuracy of the CSCL-WVE-UDI method is higher than that of both methods, with a maximum improvement of 97.5 percentage points. This is because literature [4; 5] relies on the embedding vectors and similarity metrics generated by the traditional Siamese network, and if the traditional Siamese network is poorly trained, the similarity distances  
Figure 10: Recognition results of different methods on three unknown IoT devices [4; 5; 7]. between the embedding vectors are too large, which affects the recognition effect; at the same time, there is a certain degree of similarity between the Canary Camera device and the Insteon Camera device itself, which further reduces the accuracy of the recognition. In addition, when facing N unknown IoT devices, the method proposed in literature [4, 5] needs to train N recognition models, which will increase the computational overhead; (2) Compared to the KNN algorithm-based unknown IoT device identification method in literature [7], the CSCL-WVE-UDI method exhibits higher accuracy in identifying Insteon Camera devices, but is slightly less accurate in identifying Belkin Wemo Switch devices and Canary Camera devices, which are lower by 10 percentage points and 19 percentage points. The main reason for this difference is the supervised classification implementation based on the KNN algorithm in literature [7], which therefore has prior knowledge of the real labels and number of unknown IoT devices and uses this information for model training to achieve a high recognition accuracy. In contrast, the CSCL-WVE-UDI method is implemented by integrating multiple unsupervised clustering algorithms, which performs the recognition without real labels, thus resulting in lower recognition accuracies than literature [7] for Belkin Wemo Switch devices and Canary Camera devices. However, in open environments, the inability to obtain the real tags and the number of devices of unknown IoT devices beforehand makes it difficult to apply the method in literature [7] directly. In contrast, the CSCL-WVE-UDI method does not require prior knowledge of the true labels of unknown IoT devices, which makes it more adaptable.