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AppSense: Detecting Smartphone Usage via WiFi Signals

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Mobile Networks and Management (MONAMI 2021)

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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%.

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Author information

Authors and Affiliations

  1. The University of Aizu, Aizuwakamatsu, Japan

    Tao Liu & Peng Li

  2. Ibaraki University, Mito, Japan

    Cheng Zhang

Authors
  1. Tao Liu
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  2. Peng Li
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  3. Cheng Zhang
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Corresponding author

Correspondence to Peng Li .

Editor information

Editors and Affiliations

  1. Technical University of Valencia, Valencia, Spain

    Carlos T. Calafate

  2. VTT Technical Research Centre of Finland, Oulu, Finland

    Xianfu Chen

  3. University of Macau, Taipa, China

    Yuan Wu

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© 2022 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Liu, T., Li, P., Zhang, C. (2022). AppSense: Detecting Smartphone Usage via WiFi Signals. In: Calafate, C.T., Chen, X., Wu, Y. (eds) Mobile Networks and Management. MONAMI 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 418. Springer, Cham. https://doi.org/10.1007/978-3-030-94763-7_5

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  • DOI: https://doi.org/10.1007/978-3-030-94763-7_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-94762-0

  • Online ISBN: 978-3-030-94763-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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