## 4 Experimental Results and Analysis
### WVE-UDI Algorithm Ablation Experiments  
In order to validate the effectiveness of the WVE-UDI algorithm (in Section 3.5.3) in integrating multiple clustering algorithms, a series of unknown IoT device ablation comparison experiments were designed on the classic UNSW dataset, covering the presence of one, two, and three different unknown IoT device recognition scenarios, respectively, in an open environment. These experiments assess the effectiveness of the WVE-UDI algorithm in the unknown IoT device recognition task by comparing the difference in recognition accuracy of the WVE-UDI algorithm before and after the integration of the three clustering algorithms. In this experiment, the similarity threshold \(\theta\) was set to 0.8. In addition, the WVE-UDI algorithm did not use labeled data in the clustering process, and only used real labels in calculating the recognition accuracy. (1) The results of the ablation comparison experiments in the presence of an unknown IoT device in an open environment are shown in Table 6.  
As can be seen from Table 6, the recognition accuracy of the WVE-UDI algorithm is approximately the same before and after integration when only a single unknown IoT device exists in the open environment. First, for the Belkin Wemo Switch device and the Insteon Camera device, their recognition accuracies are 100% with or without integration, which is because when only one new unknown IoT device is added to the open environment, a single clustering algorithm is accurate enough to make the clustering labels of each clustering algorithm the same, which results in the WVE-UDI algorithm having the same recognition accuracy as the remaining three clustering algorithms after integration; Second, for the Canary Camera device, although the recognition accuracy of the individual clustering algorithms is 90%, 90%, and 100%, respectively, the WVE-UDI algorithm has an accuracy of 90%, which is not 100%, mainly because the recognition results of the Agglomerative algorithm and the Birch algorithm have larger values of incorrect labels, which leads to its weighted value being higher than the weighted value of the corresponding label of the WVE-UDI algorithm, which affects the final judgment of the WVE-UDI algorithm and makes the final integrated label tend to be the wrong label, thus pulling down the Accuracy rate. Nevertheless, the recognition accuracy of the WVE-UDI algorithm still reaches 90% and above for a single unknown IoT device, which indicates that the WVE-UDI algorithm is able to effectively integrate the clustering capabilities of multiple clustering algorithms in a single unknown IoT device environment. (2) The results of the ablation comparison experiment for the presence of two unknown IoT devices in the open environment are shown in Figure 15. In order to ensure the comprehensiveness of this comparison experiment, each unknown IoT device was separately identified for comparison between two and two. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithms** & **Unknown IoT Devices** & **Belkin Wemo Switch** & **Canary Camera** & **Insteon Camera** \\ \hline Agglomerative algorithm & 100\% & 90\% & 100\% \\ \hline Birch algorithm & 100\% & 90\% & 100\% \\ \hline GaussianMixture algorithm & 100\% & 100\% & 100\% \\ \hline WVE-UDI algorithm & 100\% & 90\% & 100\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison results of recognition accuracy of individual unknown IoT devices. Figure 15: Comparison results of recognition accuracy of two unknown IoT devices. (**a**) Presence of Belkin Wemo Switch devices and Canary Camera devices in the open environment; (**b**) Presence of Belkin Wemo Switch devices and Insteon Camera devices in the open environment; (**c**) Presence of Canary Camera devices and Insteon Camera devices in the open environment.