# Tor Anonymous Traffic Identification Based on Parallelizing Dilated Convolutional Network
## 4 Experimental Analysis
### Closed-World Performance  
In this section, we train on the Wang dataset using unenhanced data to compare our anonymous traffic recognition model with state-of-the-art approaches in terms of accuracy: the CUMUL proposed by [PERSON] et al. [23], SDAE and CNN by [PERSON] et al. [12], \begin{table}
\begin{tabular}{c c c} \hline \hline
**Name of Hyperparameters** & **Range** & **Size** \\ \hline Input dimension & 500, 1000, 1500, 2000, 3000, 5000 & 2000 \\ Optimizer & Adam, Adamax, RMSProp, SGD & Adam \\ Number of RBM layers & [2,5] & 3 \\ Learning rate & 0.0005, 0.001, 0.002, 0.005, 0.01 & 0.002 \\ Training Epoch & [30,200] & 100 \\ decay & [0,0.8] & 0 \\ Hidden units of RBM & [5,30] & 20, 10, 5 \\ Number of out\_channels & [5,15] & 10 \\ Dilation coefficient & [1,10] & (1, 2, 3) \\ Kernel\_size(Conv1d) & [5,20] & 10, 20, 30 \\ Kernel\_size(Conv2d) & [1,30] & (5, 10), (1, 1) \\ Activation function & Sigmoid, Relu, Leaky ReLU, tanh & Leaky ReLU \\ Dropout & [0,05,0.5] & 0.2 \\ Batch\_size & [16;24;32;64;128] & 32 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Hyperparameters Configuration. deep fingerprinting by [PERSON] et al. [16], Var-CNN by [PERSON] et al. [13] and 2 ch-TCN by [PERSON] et al. [14]. As can be seen from Table 4, the Tor anonymous traffic identification method we use achieves an accuracy improvement over state-of-the-art approaches by using parallelizing dilated convolutions for multi-featured large field of view extraction. To demonstrate the feasibility of deploying our proposed model in both directions over the communication link, we trained the model with unenhanced data and used the data with the cell direction reversed as the test set. While on the other hand, we trained the model with enhanced data and used the data with the cell direction reversed as the test set. Accuracy and loss for the two scenarios are shown in Table 5.  
The results show that the model trained on cell directionally enhanced data achieves high recognition accuracy for reverse-captured data, while the model trained on unenhanced data has a lower recognition rate for reverse-captured data. This provides the possibility of implementing a bi-directional deployment of the Tor anonymous traffic recognition model. Next, we combined the cell direction enhancement with the crop approach enhancement and compared the accuracy curves of the model trained on the combined data with the accuracy curves of the model trained on the unenhanced data, as shown in Figure 13.  
Where \(\text{tain\_acc}\) represents the classification accuracy of the model on the training dataset during each training round, \(\text{val\_loss}\) represents the classification accuracy of the model on the validation set. The four curves represent the variation curves of \(\text{train\_acc}\) and \(\text{val\_acc}\) with training rounds for data without data enhancement; the variation curves of \(\text{train\_acc}\) and \(\text{val\_acc}\) with training rounds for data enhanced by both methods. We introduce the indicator error rate \"\(\text{abs}\)\" to represent the ability of the training model to fit the new data. \[\text{abs}=|train\_loss-val\_loss| \tag{18}\]  
The results show that the two training models achieve essentially equivalent accuracy on the validation set after the models have achieved fit; at the same time, the convergence time of the model trained on the unenhanced data (convergence at 36 EPOCH) is slightly longer than that of the unenhanced data (convergence at 20 EPOCH), but its abs value is approximately twice that of the enhanced data. This suggests that the model trained on the augmented data by both cell direction and crop operations does not reduce the test accuracy while sacrificing some model convergence speed for better fitting of the newly captured anonymous traffic data, avoiding the possibility of overfitting the model and enhancing the robustness of the model.