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.

Automated summary

In almost every situation of our life, various applications are deployed on mobile smart devices. However, in some situations, sensitive applications are strictly prohibited. Real-time recognition of applications running on mobile smart devices is important in these cases. Most existing technologies require system permissions to obtain the running application list, which is banned by mainstream mobile operating systems. To overcome this limitation, a magnetic field side channel analysis method is introduced to recognize running applications. This method uses autoencoder to extract robust depth features and implements online application recognition through the introduction of a finite-state machine. The proposed method is evaluated by recognizing 1000 different applications in a real environment, showing its feasibility and effectiveness in real-time application identification.