## 4 Experimental Results and Analysis
### Evaluation Indicators  
Since the training data samples of the CSCL-Siamese network are positive-negative pairwise relations, it can be regarded as a binary classification task, i.e., the correctly predicted positive-negative pairwise relations are treated as the positive class, and vice versa as the negative class. Therefore, the predictors are defined as shown in Table 3.  
The experiment mainly uses Accuracy, Precision, Recall, and F1 value as the evaluation metrics. _Accuracy_ measures the accuracy of the overall forecast, _Precision_ measures the correctness of positive class predictions, _Recall_ measures the predic  
\begin{table}
\begin{tabular}{c l l l} \hline \hline
**Symbol** & **Definition** & **Symbol** & **Definition** \\ \hline \(P_{0}\) & Set of positive sample pairs & \(Ne\) & Set of negative sample pairs \\ \hline \(W\) & Network weight parameters & \(e_{a},e_{b}\) & 256D embedding vectors of samples \(a\) and \(b\) \\ \hline \(y_{ab}\) & Label for sample pair (1-same class, 0-different class) & \(\lambda_{pa}\) & Weighting factor for positive pairs: \(\frac{n_{pa}}{n_{pa}+n_{pa}}\) \\ \hline \(\lambda_{ae}\) & Weighting factor for negative pairs: \(\frac{n_{pa}}{n_{pa}+n_{pa}}\) & \(d_{av}\) & Manhattan distance: \(\sum\|e_{ai}-e_{b}\|\) \\ \hline \(m\) & Minimum acceptable distance threshold for negative pairs & \(L\) & CSCL loss function \\ \hline \(ED\) & Embedded vector database for known devices & \(NED\) & Embedded vectors of new traffic data \\ \hline \(d_{\min}\) & Minimum Euclidean distance & \(\theta\) & Similarity threshold for filtering unknown devices \\ \hline \(A,B,G\) & Clustering results (Agglomerative/BIRCH/GaussianMixture) & \(W_{A},W_{B},W_{G}\) & Weights for clustering algorithms (0.2/0.2/0.6) \\ \hline \(a_{\mathbb{R}}\) & Cluster label of sample \(i\) by Agglomerative & \(\hat{Y}_{u}\) & Final identification results of unknown devices \\ \hline \(TP,TN\) & True positive/negative counts & \(FP,FN\) & False positive/negative counts \\ \hline G-mean & Geometric mean of TPR and TNR & \(n_{pe},n_{mc}\) & Number of positive/negative sample pairs \\ \hline \hline \end{tabular}
\end{table}
Table 1: \(Cont.\)  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset Name** & **Number of Positive Samples** & **Number of Negative Pair Samples** \\ \hline Aalto & 8008 & 25,968 \\ UNSW & 11,000 & 46,000 \\ LSIF & 8800 & 28,800 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental datasets.  
\begin{table}
\begin{tabular}{c l l} \hline \hline  & **Positive Class** & **Negative Class** \\ \hline Predicted & TP (Positive class predicted to be positive) & FP (Negative class predicted to be positive) \\ Unpredicted & FN (Positive class predicted to be negative) & TN (Negative class predicted to be negative) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Definitions of forecasting indicators.  
positive class, and the F1 value integrates the trade-off between Precision and Recall. Their respective formulas are expressed below:  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{10}\]  
\[Precision=\frac{TP}{TP+FP} \tag{11}\]  
\[Recall=\frac{TP}{TP+FN} \tag{12}\]  
\[F1=\frac{2\times\text{Pr}\,ecision\times\text{Recall}}{\text{Pr}\,ecision+ \text{Recall}} \tag{13}\]  
Since the number of negative pair samples is much larger than positive pair samples, and the above metrics mainly focus on the prediction performance of positive pair samples, the G-mean metric is further introduced as an evaluation metric. The G-mean metric measures not only the ability to correctly identify positive pairs of samples (true positive rate TF), but also takes into account the ability to correctly identify negative pairs of samples (true negative rate TN), thus providing a more comprehensive assessment of the unbalanced set of positive and negative pairs of samples. The formula is expressed as follows:  
\[G-mean=\sqrt{\frac{TP}{TP+FN}\frac{TN}{TN+FP}} \tag{14}\]