# Multi-WiIR: Multi-User Identity Legitimacy Authentication Based on WiFi Device
## 5 Experiment Results
### Performance Evaluation
#### 5.2.3 Comparison of Different Models  
(1) Branch model comparison  
Our Multi-WiIR model utilizes a convolutional neural network (CNN) as a backbone for extracting initial features, followed by the extraction of individual user features through a multi-branch BiLSTM mechanism, considering both past and future information. To demonstrate the superiority of our approach using the BiLSTM model, we compared our model with multi-branch LSTM, GRU, and traditional CNN architectures.  
As depicted in Figure 12, the average accuracy achieved by our BiLSTM model in multi-user scenarios was 88.54%, representing an improvement of nearly 5 percentage points over LSTM's 83.45% and GRU's 82.56%. This notable difference in performance substantiated the superiority of our selection of a BiLSTM architecture.  
(2) Comparison of Running Efficiency  
To assess the enhanced running efficiency of Multi-WiIR in contrast to multiple binary classification models, we conducted a comparative analysis of their training times and test set outcomes on the same computing platform. Figure 13 illustrates the comparison of the time consumption by both systems across various training epochs, ranging from 10 to 40 epochs. The average training time for Multi-WiIR was calculated to be 0.5088 s per  
Figure 11: Overall performance of Multi-WiIR with different numbers of users.  
Figure 12: Comparison of the performance of the different models.  
epoch, whereas the average training time for the multiple models amounted to 0.7924 s per epoch. This indicates that Multi-WiIR reduced the training time by 35.8% compared to the multi-model training duration. Moreover, it is noteworthy that the initialization time of the multi-model system significantly exceeded that of Multi-WiIR. In summary, our findings suggest that Multi-WiIR effectively enhanced the operational efficiency of the system when compared to the multi-model multi-user legitimacy authentication system.  
(3) Comparison with Previous Legitimacy Authentication Systems  
Table 3 provides a concise overview of other mature WiFi-based IoT applications. WiAU [16] necessitates the acquisition of information from untrained and unauthorized personnel, a practical infeasibility in real-world scenarios. Furthermore, the thresholds for identifying legitimate individuals necessitate adjustments with fluctuations in their numbers. Multiauth [27] requires the information of unauthorized individuals and imposes location constraints. GaitSense [30], similarly, requires adjustments to the threshold for identifying legitimate individuals with changes in the number of experimental subjects. Wi-Sniffer [31] extracts phase features, where the presence of Fresnel zones significantly influences phase variations, thus imposing location constraints. Moreover, all the aforementioned systems are tailored for single-user scenarios. In contrast, our Multi-WiIR system leverages the extraction of individual characteristics to ascertain legitimacy, unaffected by fluctuations in the number of authorized persons and capable of concurrently handling multiple individuals, irrespective of their positions.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**System**} & \multicolumn{2}{c}{**Illlegitimate Users\({}^{*}\) Information**} & \multirow{2}{*}{**Location Requirement**} & \multirow{2}{*}{**Adjust Threshold**} & **Applicable Scenarios** \\ \hline WiAU & yes & no & yes & single-user \\ \hline MultiAuth & yes & yes & no & single-user \\ \hline GaitSense & no & yes & yes & single-user \\ \hline Wi-Sniffer & no & yes & yes & single-user \\ \hline Multi-WiIR & no & no & no & multi-user \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison among works on legitimacy authentication.  
Figure 13: Comparison of model elapsed time.