A novel attack to track users based on the behavior patterns

摘要

Currently, people around the world daily use the Internet to access various services, such as e-mail and onlinernshopping. However, the behavior-based tracking attacks have posed a considerable threat to users’ privacy.rnRelying on characteristic patterns within the Internet activities, this attack can link a user’s multiplernsessions. In this paper, we investigate the behavior-based tracking attack and propose some countermeasuresrnto mitigate the threat. We preprocess the raw traffic data and then extract features ranging from lower layerrnnetwork packets to high-level application-related traffic. Specifically, we focus on four types of applicationlevelrntraffic to infer users’ habits, including HTTP, IM, e-mail, and P2P. In addition, we extract the webrnqueries entered into shopping websites and classify them to infer users’ preferences. Then, we constructrnthe preference models and propose an improved method. For evaluation, we collect traffic in the realworldrnenvironment to construct a large-scale dataset. Five hundred and nine users are selected in terms ofrnthe user’s active degree. When the term frequency–inverse document frequency transformation is used,rnthe improved method can identify an average of 93.79% instances correctly. Our extensive empirical experimentsrndemonstrate the effectiveness and efficiency of our approaches. Finally, we discuss and evaluate severalrncountermeasures. Copyright © 2016 John Wiley & Sons, Ltd.