Transfer Learning for User Action Identication in Mobile Apps via Encrypted Trafc Analysis Academic Article uri icon


  • Recent academic studies have demonstrated the possibility of inferring user actions performed in mobile apps by analyzing the resulting encrypted network traffic. Due to the multitude of app versions, mobile operating systems, and device models (collectively referred to in this pa-per as configurations) previous approaches are not applicable to real life settings. In this work, we extend the ability of these approaches to generalize across different configurations. We treat the different configurations as a case for transfer learning, and adapt the co-training method to support the transfer learning process. Our approach leverages a small number of labeled in-stances of encrypted traffic from a source configuration, in order to construct a classifier capable of identifying a users actions in a different (target) configuration which is completely unlabeled. Experiments on real datasets collected from different applications on Android devices show that the proposed method achieves F1 measures over 0.8 for most of the considered user actions.

publication date

  • January 12, 2018