Sniffing the Application Usage Information With the Leakage Current of Laptops

Dian Ding, Yijie Li, Yongzhao Zhang, Yi-Chao Chen, Xiaoyu Ji, Guangtao Xue

Abstract: Smart devices are proliferating in every aspect of our lives, providing convenience but also exposing us to the risk of information leakage at any moment. Attackers can monitor the user and infer private information such as personality and preferences by stealing the behavioral information. In this paper, we investigated the potential threat of information stealing via the leakage current of laptops and electrodes in wearable devices (e.g., smart watches and bracelets). Specifically, the leakage current in the laptop adapter can flow from the metal casing into the human body and be collected by electrodes in wearable devices when the user is using a laptop with a metal casing (e.g., MacBook). We verified the correlation between leakage current and the working states of the laptop, where different operations corresponding to different CPU instructions can generate different leakage currents. Based on this, we propose LeakThief, a system that consists of three components: leakage current detection, application operation detection, and application recognition. The experiments in a real-world environment demonstrated that the proposed system can recognize 25 common applications with high accuracy, including launching-based (96.4% ) and in-application operation-based recognition (81.2%).

External IDs:dblp:journals/tmc/DingLZCJX26