# Tor Anonymous Traffic Identification Based on Parallelizing Dilated Convolutional Network
## 2 Related Work
### Overview of Website Fingerprinting Attacks  
There are three main types of website fingerprint recognition methods, namely similarity discrimination methods, traditional machine learning methods and deep learning methods [1]. The concept of website fingerprinting attack was first introduced in 2002 by [PERSON] [2] to address the Safe Web security vulnerability problem, demonstrating the feasibility of obtaining user information through website fingerprinting attacks. Features such as the number of TCP connections, the length of the response message have been used and similarity discrimination methods such as the Jaccard coefficient have been applied [3; 4]. The disadvantage is that similarity discrimination methods can cause errors when distinguishing small perturbations of features. The early HTTP protocol leaked features such as resource length, HTML length and total object length, which can be used in identifying static web pages. Therefore, these methods are no longer applicable under the current web technology conditions. With the further spread of the HTTP 1.1 protocol and the increasing maturity of anonymity techniques, the second-generation onion routing system Tor has gradually become the most widely used anonymous communication system on the web. Machine learning algorithms such as the plain Bayesian algorithm, support vector machine, random forest algorithm and hidden Markov model have been used extensively in website fingerprinting. [PERSON] et al. [5] extracted the size, direction and time characteristics of anonymous traffic and ran a support vector machine to classify it, achieving an accuracy of 55% for Tor traffic. [PERSON] et al. [6] generated fingerprints using only the length of  
Figure 1: Tor website fingerprinting attack architecture. messages generated during web access. They used Naive Bayes as a classifier and finally achieved 73% accuracy. [PERSON] et al. [7] used four machine learning methods, namely Naive Bayes, Bayes Net, C4.5 and random forest, to demonstrate good experimental results in a closed-world environment but poor results were found in an open-world environment at both the circuit level and the flow level. [PERSON] et al. [8] proposed an effective anonymity network traffic identification model called Anon, combining improved mutual information and random forest algorithms to quickly filter irrelevant and redundant features, using the XGboost model for training, achieving better results than previous models. [PERSON] et al. [9] proposed to use Tor cells instead of TCP/IP packets for a better understanding of data and achieved 91% classification accuracy in the closed world and over 95% recall in the open world by comparing experiments to remove the interference of \"SENDME\" cells in the features and using a traffic instance distance metric in SVM. In sum, the Tor anonymous traffic identification using traditional machine learning methods has generally achieved good results, but the following problems still exist: (1) using the Naive Bayesian classifier as an example, due to the attribute independence requirements of the Naive Bayesian classifier, the dependence between the features selected by each study becomes one of the reasons that affect the performance of the classifier and this limits our feature selection; (2) the complex types of features used lead to more feature extraction workload and there is a problem that the complex types of features used instead lead to low recognition accuracy. [PERSON] et al. [10] provided the opening introduction to the concept of deep learning in 2006, and since then deep learning methods have become a popular area for machine learning research, taking advantage of its ability to automatically extract features from raw data. Three main types of deep learning methods used in website fingerprinting include the following: classification methods based on stacked denoised autoencoder, recurrent neural network classification methods based on temporal features and methods based on convolutional neural networks.