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Although the anonymous communication network Tor can protect the security of users’ data and privacy during their visits to the Internet, it also facilitates illegal users to access illegal websites. Website fingerprinting attacks can identify the websites that users are visiting to discern whether they are performing illegal operations. Existing methods tend to manually extract the traffic features of users visiting websites and construct machine learning or deep learning models to classify the features. While these methods can be effective in classifying unknown website traffic, the effect of classification in the use of defensive measures or onion service scenarios is not yet ideal. This paper proposes a method to identify Tor users visiting websites based on frequency domain fingerprinting of network traffic (FDF). We extract the direction and length features of circuit sequences in access traffic and combine and transform them into the frequency domain. The classification of access traffic is accomplished by using a deep learning classification model combining CNN, FC, and Self-Attention. In this paper, the proposed FDF method is experimentally validated in common scenarios of Tor networks. The results show that FDF outperforms the existing methods for classification in different Tor scenarios. It can achieve 98.8% and 94.3% classification accuracy in undefended and WTF-PAD defense scenarios, respectively. In the onion service scenario, the accuracy is improved by 4.7% over the current state-of-the-art Tik-Tok method.

A Method for Identifying Tor Users Visiting Websites Based on Frequency Domain Fingerprinting of Network Traffic