## 4 Experimental Results and Analysis
### EVDC-CSCL-Siamese Algorithm Comparison Experiments  
In order to validate the effectiveness of the EVDC-CSCL-Siamese algorithm in Section 3.4.5, this paper conducts a comparison experiment of known IoT device recognition on the classical UNSW dataset. Since the EVDC-CSCL-Siamese algorithm is completely dependent on the recognition accuracy of the proposed CSCL-Siamese network in constructing the database of known IoT device embedding vectors, this experiment only focuses on analyzing the experimental results of the CSCL-Siamese network. The results of the comparison experiments between the CSCL-Siamese network and the IoTSense [26], IoTDevID [27], Kotak [10], and SmartRecon [25] in the multiclassification recognition network are shown in Figure 14.  
As can be seen in Figure 14a, the recognition accuracy of the CSCL-Siamese network reaches 90.1%, which is higher than that of the other four multiclassification networks. The main reason for this advantage is that the CSCL-Siamese network is essentially a kind of metric learning network structure, which requires only a small number of pairwise samples to achieve effective recognition, and the 50 data samples taken in this paper to construct a set of PNSPs. In this paper, 50 pairs of data samples are taken to construct the full arrangement of positive and negative samples, which is just in line with the characteristics of the CSCL-Siamese network; on the contrary, the remaining four recognition networks adopt the structure of multiclassification network, which usually requires a large number of training samples to achieve a higher accuracy, and theoretically, the more the amount of data, the more abundant the information, and the higher the accuracy of the recognition. Thus, the sample size of 50 data is relatively small for the large number of samples required for the structure of the multiclassification networks, leading to insufficient training and making the probability of misclassification of these multiclassification networks increase. Further, by comparing Figure 14b,c, it can be seen that compared to the multiclassification networks, the recognition Precision rate of the CSCL-Siamese network decreases, but the Recall rate is relatively high at 82.8%, which is higher than that of the multiclassification networks by up to 15.8 percentage points. This is due to the difference in recognition mechanisms between CSCL-Siamese networks and multiclassification networks. CSCL-Siamese networks focus on learning the similarity between data samples, whereas multiclassification networks focus on modeling through data samples to make classification decisions. This difference in mechanism results in CSCL-Siamese networks having a slightly lower recognition Accuracy than multiclassification networks. However, the advantage of higher Recall of CSCL-Siamese networks ensures that more IoT devices are correctly recognized. Therefore, although the recognition Precision rate of CSCL-Siamese network has decreased, its good Recall rate and high Accuracy rate can ensure reliable recognition performance in the field of IoT device recognition, and also verifies the effectiveness of the EVDC-CSCL-Siamese algorithm.  
Figure 14: Comparative experimental results of different network models.