DePL: Detecting Privacy Leakage in DNS-over-HTTPS Traffic

DNS attack is one of the main threats to the Internet. Aiming at detecting the privacy leakage of DoH (DNS-over-HTTPS), in this paper, we proposed a model called DePL based on n-shot learning. This model can analyze which websites the user visits by classifying DoH traffic, and then we evaluate the impact of DoH protocol on user privacy leakage risk. In our experiments, we only used 15 training samples to obtain an accuracy of 86.54% in a closed environment. In an open environment, when the threshold is set to 0.7, the model still has an accuracy of 78.86%. Compared with the existing algorithms, DePL solves the problem of insufficient samples in real applications. A small number of training samples can obtain high-accuracy recognition, which proves the possibility of the detection of privacy leakage in DoH traffic.