# Tor Anonymous Traffic Identification Based on Parallelizing Dilated Convolutional Network
## 2 Related Work
### Overview of Website Fingerprinting Attacks
Convolutional neural network-based classification methods are divided into two categories, including one-dimensional convolutional which is used on one-dimensional traffic data and two-dimensional convolutional which converts network traffic into two-dimensional images for input into a convolutional neural network. [PERSON] et al. [11] used deep learning methods for the first time to perform website fingerprinting attacks, using only cell orientation features and applying stacked denoised autoencoder to reduce the dimensionality of the data vector, reducing the risk of model overfitting and improving accuracy. He experimented with the highest accuracy achieved by both three-layer and two-layer stacked denoising autoencoders in a closed world of 88%, but the attack model is not pre-trained and both cell orientation data and labels are used as input for classification, leaving room for further improvement in the scalability of the model. [PERSON] et al. [12] used the LSTM (Long Short-Term Memory) temporal network structure for reference in the fingerprint attack model building and used only the first 150 cells of each traffic data as input data in order to reduce the time complexity of their model, and finally achieved an accuracy of 94.02% in a closed world. The use of the LSTM model solves the RNN long-term dependency problem, which essentially takes into account the existence of a certain temporal relationship between packets communicated during the access to the same web page, and thus also indirectly argues for the feasibility of recurrent neural networks in website fingerprinting attacks. [PERSON] et al. [13] proposed a VAR-CNN method for website fingerprinting attacks based on ResNet, combining cell timestamp, direction and seven cumulative statistical features to achieve the purpose of using a weak prediction of cumulative statistical features to strengthen the strong prediction of neural networks, and finally reduce the impact of a possible data overshooting problem in anonymous traffic by smaller training samples. [PERSON] et al. [14] proposed a 2 ch-TCN approach, which exploited the properties of TCN (temporal convolutional network) to effectively address the gradient explosion/disappearance problem by using dual channels for cell directional features and timestamp features processing, and achieved an accuracy of 93.73% in a closed world. [PERSON] et al. [15] proposed a DBF model for an attack, using burst features alone as unique features and three modules, namely the serial burst extraction module, burst abstract learning module and burst depth analysis module to effectively improve the model performance. [PERSON] et al. [16] proposed a CNN-based method for website fingerprinting (deep fingerprinting) which uses cell orientation as the feature input. A one-dimensional convolutional architecture with eight layers and two fully connected layers was constructed for classification, adding multiple Maxpooling layers during convolution and using 0.1 and 0.5 dropout rates in the convolutional and fully connected layers, respectively. They reduced the loss of data information by ELU and RELU functions at the output level, eventually achieving better results in both closed-world and open-world scenarios. However, the model network structure is too deep and the number of parameters is too large, which results in a long training time and affects its further extension and update. In sum, the accuracy of the Tor anonymous traffic identification method based on deep learning has been greatly improved and there is no need to manually select features, but the following problems still exist: (1) The structure of most models is too deep, which results in the large amount of parameters and too long training time. The high requirements for model training resources lead to slow model training and difficulty in actual use, which affects its further expansion and update. (2) Most of the existing studies have been based on the stability of the network environment in which the traffic data are collected.