Research Article
AppSense: Detecting Smartphone Usage via WiFi Signals
@INPROCEEDINGS{10.1007/978-3-030-94763-7_5, author={Tao Liu and Peng Li and Cheng Zhang}, title={AppSense: Detecting Smartphone Usage via WiFi Signals}, proceedings={Mobile Networks and Management. 11th EAI International Conference, MONAMI 2021, Virtual Event, October 27-29, 2021, Proceedings}, proceedings_a={MONAMI}, year={2022}, month={1}, keywords={CSI Smartphone Wi-Fi}, doi={10.1007/978-3-030-94763-7_5} }- Tao Liu
Peng Li
Cheng Zhang
Year: 2022
AppSense: Detecting Smartphone Usage via WiFi Signals
MONAMI
Springer
DOI: 10.1007/978-3-030-94763-7_5
Abstract
Mobile usage reveals some of the user’s daily behavior habits and is essential. Efforts in this field of research have never stopped and have achieved a series of results. However, some active inspections often encounter difficulties in not getting specific data due to the obturated nature of the operating system. Universal passive detection often needs to compromise smartphone software which will face serious privacy breaches. In this paper, we propose AppSense, a non-invasive system that can detect smartphone usage via off-the-shelf WiFi devices by identifying various operations. The machine learning technique is utilized to divide smartphone operation actions into seven categories. These actions represents the usages of the device. A prototype was developed to evaluate the performance of AppSense and experimental results show that the average accuracy of seven operations recognition is 86.43%.
