Website Fingerprinting Attack Through Persistent Attack of Student

Abstract

Illegal users usually use Tor to hide their malicious behavior for browsing website. Website fingerprinting (WF) attack can help local network administrator to prevent illegal behavior of anonymous users. Although a lot of researches have improved website fingerprinting attacks, they still cannot address the concept drift problem effectively. In this paper, we propose a novel WF attack framework, Persistent Attack of Student (PAS), by integrating self-training mechanism with advanced deep learning (DL) related WF attack. PAS can train new DL model by using concept drift dataset with pseudo label for alleviating concept drift issue. In addition, we present a new deep convolutional neural network (DCNN) attack with stable accuracy by using automatic and local feature extraction. Then, we evaluate PAS application with different advanced deep learning WF attacks for alleviating concept drift issue. The experimental results show that DCNN attack achieves 96.50%-98.88% accuracy with 0.7-0.8x time cost of DF attack in closed world of 95-900 monitored websites, and reaches 96.32% precision and 96.31% recall in open world of 400,000 unmonitored websites. The PAS attack framework with different deep learning methods achieves 87.56%-91.46% in concept drift dataset of 56 days for 200 monitored websites, which is 2.27% 2.36% better than each original deep learning attack. The experimental results demonstrate that PAS framework can help alleviate concept drift issue effectively and DCNN can perform WF attack with less time cost efficiently.