# Tor Anonymous Traffic Identification Based on Parallelizing Dilated Convolutional Network
## 3 Model Design of This Paper
### Tor Anonymous Traffic Identification Architecture
#### 3.1.2 Parallelizing Dilated Convolutions Networks  
In the Tor anonymous traffic identification process, timestamp features and directional features are treated as two feature sequences. For each anonymous traffic of client-web server interaction, cell directional features are a sequence of +1, \(-\)1 over the communication time range corresponding to each timestamp change, and thus our proposed network architecture requires the extraction of high-level features from both sequences that correspond to our classification task. The LSTM temporal network architecture proposed by [PERSON] et al. (2012) takes full account of the sequence feature extraction capabilities of their respective models when dealing with sequential problems but still suffers from some gradient disappearance problems and high model training resource requirements as bottlenecks. We construct the convolutional neural network from the perspective of the feature perceptual field, which is the size of the region on the input map corresponding to a point on the feature map at the output of each layer of the convolutional neural network, as shown in Figure 5. The growth in depth increases the number of layers and the growth in width increases the number of neurons in each layer, thus providing a better fit of the model to the training data. However, the resulting increase in the number of network parameters consumes the computational power of the machine to a large extent and increases the risk of overfitting. Previous work has used Maxpooling layers to perform parameter parsimony but there exists a problem of partial loss of sequence information. We introduce the dilated convolutions proposed by [PERSON] (2017) into our model. The dilated convolutions increase the perceptual field of the convolution kernel while keeping the number of parameters constant, so that each convolution output contains a larger range of information, while it ensures that the size of the output feature map remains constant. In two-dimensional convolution, a 3 \(\times\) 3 convolution kernel with a dilation of 2 has the same field of perception as a 5 \(\times\) 5 convolution kernel, but with only nine parameters, a 64% reduction in the number of parameters compared to a 5 \(\times\) 5 convolution. At the same time, we also use the concept of the Inception block proposed by [PERSON] et al. (2018) in GeogleNet for reference, combining convolutional layers of different convolutional kernel sizes through parallelism, and stitching the input direction and timestamp data of anonymous traffic through three one-dimensional convolutional layers with dilation coefficients of 1, 2 and 3 to form a deeper matrix in the dimension of the channel, while  
Figure 4: Detailed classification of Tor anonymous traffic. performing dimensionality reduction on the original data matrix and subsequently, two layers of two-dimensional convolution are performed. After the flattening operation, the data are mapped to the corresponding classification totals by full concatenation. The convolutional layer parameters use a dropout rate of 0.2 to reduce the complex co-adaptation relationships between neurons, preventing the situation where some features are only effective in other specific feature settings. In this way, the network is forced to learn more robust features, which in turn helps to solve the overfitting problem. In addition, we use the Linear layer, Leaky ReLU activation function and Layer Normalization [19] to add the self-extracted features of the three-layer stacked Boltzmann machine (RBM) to the output of parallelizing dilated convolutions, so as to further use the SoftMax layer to map model outputs to corresponding class labels. The parallelizing dilated convolutions layer is shown in Figure 6, and we also use the Leaky ReLU activation function after each layer of convolution. The Leaky ReLU activation function is a variant of the ReLU activation function, the output of which has a small slope to negative inputs.