Mobile applications identification using autoencoder based electromagnetic side channel analysis.

Various applications are deployed on mobile smart devices in almost every situations of our life, while in some of these situations sensitive applications are strictly prohibited, such as cameras in cinemas and browsers in examination halls. Real-time recognition of applications running on mobile smart devices is of great significance in these cases. However, most of the existing technologies have the limitation that they require system permissions to obtain the running application list which is banned by mainstream mobile operating systems. Noting that the launch of a certain application will emit a unique pattern of magnetic field, we introduce magnetic field side channel analysis to recognize running applications. However, magnetic field side channel analysis is challenging since it is hard to extract features from magnetic field data without domain experts. Besides, real-time applications identification demands accurate detection of applications launching. To overcome these challenges, we extract robust depth features using autoencoder and implement online application recognition by introducing finite-state machine to identify the application launch window from raw data. The proposed method is evaluated by recognizing 1000 different applications in real environment. The experiment results show that the proposed method is feasible and effective in real-time application identification.