More Realistic Website Fingerprinting Using Deep Learning

Website fingerprinting (WF) allows a passive local eavesdropper to monitor the encrypted channel where users search the Internet and determine which website the user is visiting from the recorded traffic. The effectiveness of using deep learning (DL) in WF attacks has been explored in recent work. However, they all are built and evaluated on one-page traces. Our goal is to explore whether deep learning can be used to handle the situations when the captured traces are not best-case for an adversary, such as partial traces and two-page traces. We aim to reduce the distance between the lab experiments and the realistic conditions. We evaluate our proposed method in both closed-world and open-world settings and found that Convolutional Neural Network (CNN) outperforms Long-Short Term Memory network (LSTM) in all scenarios. CNN also shows a great potential in predicting on a smaller number of packets. For partial trace missing 20% packets in the beginning of the trace, the accuracy is improved from 8.28% to 86.93% compared to the original DL model by adding the head detection. We then show the accuracy of predicting on two-page traces. With an overlap of 80% between two websites, we are able to achieve an accuracy of 89.25% and 74.2% for the first and second website in the closed-world evaluation, and 95.5% and 75% in the open world from our simulation. To verify our simulation results, we set up a crawler to collect both training and testing data and gathered the largest two-page traces testing dataset ever used. The results shown in the real world experiment is consistent with the simulation.