Abstract
Tor is widely used to protect users’ privacy, which is the most popular anonymous tool. Tor introduces multiple pluggable transports (PT) to help users avoid censorship. A number of traffic analysis methods have been devoted to de-anonymize these PT. Snowflake is the latest PT based on the WebRTC protocol and DTLS encryption protocol for peer-to-peer communication, differing from other PT, which defeat these traffic analysis methods. In this paper, we propose a Snowflake traffic identification framework, which can identify whether the user is accessing Tor and which hidden service he is visiting. Rule matching and DTLS handshake fingerprint features are utilized to classify Snowflake traffic. The linear interpolation of the accumulative payload length of the first n messages in the DTLS data transmission phase as additional features are extracted to identify the hidden service. The experimental results show that our identification framework F-ACCUMUL can effectively identify Tor-Snowflake traffic and Tor-Snowflake hidden service traffic.
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