# Tor Anonymous Traffic Identification Based on Parallelizing Dilated Convolutional Network
###### Abstract  
The widespread use of the onion browser (Tor) has provided a breeding ground for the proliferation of cybercriminal activities and the Tor anonymous traffic identification method has been used to fingerprint anonymous web traffic and identify the websites visited by illegals. Despite the considerable progress in existing methods, problems still exist, such as high training resources required for the identification model, bias in fingerprint features due to the fast iteration of anonymous traffic and singularity in the definition of traffic direction features. On this basis, a Tor anonymous traffic identification model based on parallelizing dilated convolutions multi-feature analysis has been proposed in this paper in order to address these problems and perform better in website fingerprinting. A single-sample augmentation of the traffic data and a model combining multi-layer RBMs and parallelizing dilated convolutions are performed, and binary classification and multi-classification of websites are conducted for different scenarios. Our experiment shows that the proposed Tor anonymous traffic recognition method achieves 94.37% accuracy and gains a significant drop in training time in both closed-world and open-world scenarios. At the same time, the enhanced traffic data enhance the robustness and generalization of our model. With our techniques, our training efficiency has been improved and we are able to achieve the advantage of bi-directional deployability on the communication link. Tor; anonymous traffic identification; parallelizing dilated convolutions; bi-directional deployability +
Footnote †: journal: Medical  
techniques are applied in both open-world and closed-world attack scenarios. Closed world means that anonymous Tor users only visit a specific number of sensitive web pages, and a web fingerprinting attacker needs to identify the specific sensitive web pages from already known traffic characteristics. An open-world scenario, which is closer to a real web scenario, means that Tor Anonymous users can access a variety of pages through their browsers, and the attacker needs to determine whether the pages they visit are sensitive or not. In this paper, we propose a Tor anonymous traffic identification model based on a parallelizing dilated convolutional network for the continuity, bi-directionality and frequent iteration of anonymous communication links in Tor anonymous traffic. The main contributions of this paper are as follows:  
1. In the data processing stage, the directional features are enhanced through cropping and adding data. We then combine the methods used in previous studies to identify anonymous traffic using a single directional feature or timestamp feature. 2. In the model training stage, we use parallelizing dilated convolutional networks and multi-layer RBMs to extract features and classify web pages. A multi-core convolutional network parallelizing architecture is built to extract the sequential features of cell packets and the expanded convolutions make it possible to expand the sensory field of the model without pooling operations, enhancing the sensory range of a single cell packet for continuous positive/negative traffic data with neighboring timestamps, compared to the traditional CNN architecture. 3. In the model implementation stage, we provide the possibility of bi-directional deployment of the traffic identification model over the Tor communication link and improve the robustness of the model. Experiments are conducted on the collected dataset to compare with the DF model, LSTM model, SDAE model, etc. The experiments show that our method alleviates the problem of insufficient generalization of the model due to irregular updates of web content in real-life situations. The reduced number of parameters speeds up the convergence of the model and the proposed method improves the recognition accuracy compared to previous Tor anonymous traffic identification methods.