## 1 Introduction  
Tor has been proven better at protecting users' privacy than other anonymous communication tools. It establishes a random multi-hop communication circuit through relays provided by global volunteers. The user's browsing data is encrypted and contained in multiple layers in the process of transmitting, similar to an onion wrapped in layers. Random multi-hop communication circuits bring significant challenges to large-scale supervision. Onion encryption effectively prevents the attacker from analyzing the communication content to hide the user's identity and location, as well as the target website visited by the user. Nevertheless, website fingerprinting (WF) attacks have proven that there are characteristics explicitly or implicitly hidden in traffic patterns. The attacker can de-anonymize the target website a user is browsing by well-trained classifiers. Early on, some WF attacks were based on machine learning, such as SVM [1, 2], k-NN [3], and random forest [4]. They used handcrafted features to describe the difference between the traffic trace of different websites and achieved more than 90% accuracy in the closed-world setting. Manually designed features have been criticized for being vulnerable to protocol changes [5, 6, 7, 8], while deep learning can make up for this deficiency through automated feature engineering. Moreover, existing works show that features extracted by unsupervised DNNs are more effective [9]. Deep-learning-based WF attacks, such as Var-CNN [10], show great advantages with accuracy and a true positive rate of over 98% in both closed-world and open-world settings. Although deep learning methods demonstrate promising results, they often require massive data in the training phase. It would be costly for the attacker to collect sufficient traffic instances for each website. Moreover, the content of a website may change irregularly all the time causing the data distribution shift. The adversary must collect training data frequently and retrain new models to catch up with the variation. It is an unaffordable expense, especially when running into a large set of monitored websites. As a result, the data hunger problem severely restricts the application of WF attacks. To tackle this problem, many studies [11, 12, 13] were inspired by transfer learning, introducing pre-trained feature extractors in their attack process. The issue is that these attacks need a large amount of additional pre-training data. What is more, the effectiveness of the extractor will be lost when faced with target data with a different distribution than the pre-training data. Others [14] have tried to use data augmentation methods borrowed from the computer vision field to offer extra training data. However, website traffic and pictures are of a completely different nature. For example, the mixup can perturb the color and shading of a picture without changing the semantics. The same perturbation does not work for traffic traces which only contain the direction and timestamps of cells. In this study, we reviewed the loading process of web pages and proposed the send-and-receive pair (SRP) as the basic unit to characterize website traffic. We observed that traffic traces could be bionic, quite different from entities with unique semantics and strict spatial structure,e.g., people's faces. The bionic traces generated by our proposed method can be used as a supplementary training dataset of WF attacks and further enhance their capability. The main contributions of this paper are listed as follows:  
* We proposed a new concept called send-and-receive pair (SRP), which provides a microscopic perspective for us to study website traffic. * We demonstrated that website traffic could be bionic by reorganizing SRPs generated by web page loading. Furthermore, we proposed a bionic trace generation method based on the browser working mechanism and network state fluctuation simulation. * We further investigate the concept drift problem of website traffic in closed-world and open-world scenarios.