TY - GEN
T1 - Exploiting Contactless Side Channels in Wireless Charging Power Banks for User Privacy Inference via Few-shot Learning
AU - Ni, Tao
AU - Li, Jianfeng
AU - Zhang, Xiaokuan
AU - Zuo, Chaoshun
AU - Wang, Wubing
AU - Xu, Weitao
AU - Luo, Xiapu
AU - Zhao, Qingchuan
PY - 2023/10
Y1 - 2023/10
N2 - Recently, power banks for smartphones have begun to support wireless charging. Although these wireless charging power banks appear to be immune to most reported vulnerabilities in either power banks or wireless charging, we have found a new contactless wireless charging side channel in these power banks that leaks user privacy from their wireless charging smartphones without compromising either power banks or victim smartphones. We have proposed BankSnoop to demonstrate the practicality of the newly discovered wireless charging side channel in power banks. Specifically, it leverages the coil whine and magnetic field disturbance emitted by a power bank when wirelessly charging a smartphone and adopts the few-shot learning to recognize the app running on the smartphone and uncover keystrokes. We evaluate the effectiveness of BankSnoop using commodity wireless charging power banks and smartphones, and the results show it achieves over 90% accuracy on average in recognizing app launching and keystrokes. It also presents high adaptability when apply to different smartphone models, power banks, etc., achieving over 85% accuracy with 10-shot learning.
AB - Recently, power banks for smartphones have begun to support wireless charging. Although these wireless charging power banks appear to be immune to most reported vulnerabilities in either power banks or wireless charging, we have found a new contactless wireless charging side channel in these power banks that leaks user privacy from their wireless charging smartphones without compromising either power banks or victim smartphones. We have proposed BankSnoop to demonstrate the practicality of the newly discovered wireless charging side channel in power banks. Specifically, it leverages the coil whine and magnetic field disturbance emitted by a power bank when wirelessly charging a smartphone and adopts the few-shot learning to recognize the app running on the smartphone and uncover keystrokes. We evaluate the effectiveness of BankSnoop using commodity wireless charging power banks and smartphones, and the results show it achieves over 90% accuracy on average in recognizing app launching and keystrokes. It also presents high adaptability when apply to different smartphone models, power banks, etc., achieving over 85% accuracy with 10-shot learning.
M3 - Conference article published in proceeding or book
SP - 1
EP - 15
BT - Proceedings of the 29th Annual International Conference on Mobile Computing and Networking
ER -