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SSG: Sensor Security Guard for Android Smartphones

Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST,volume 163)


The smartphone sensors provide extraordinary user experience in various Android apps, e.g. sport apps, gravity sensing games. Recent works have been proposed to launch powerful sensor-based attacks such as location tracing and sound eavesdropping. The use of sensors does not require any permission in Android apps, so these attacks are very difficult to be noticed by the app users. Furthermore, the combination of various kinds of sensors generates numerous types of attacks which are hard to be systematically studied.

To better address the attacks, we have developed a taxonomy on sensor-based attacks from five aspects. In this work, we propose a sensor API hooking and information filtering framework, Sensor Security Guard (SSG). Unlike any rough hooking framework, this system provides fine-grained processing for different security levels set by the users, or by default. The sensor data is blocked, forged or processed under different mode strategies and then returned to the apps. In addition, according to the taxonomy, SSG develops fine-grained corresponding countermeasures. We evaluate the usability of SSG on 30 popular apps chosen from Google Market. SSG does not cause any crash of either the Android system or the apps while working. The result indicated that SSG could significantly preserve the users’ privacy with acceptable energy lost.


  • Hook
  • Sensor API
  • Android
  • Security

Major program of Shanghai Science and Technology Commission (Grant No: 15511103002): Research on Mobile Smart Device Application Security Testing and Evaluating.

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  • DOI: 10.1007/978-3-319-28910-6_20
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Correspondence to Bodong Li .

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© 2016 Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Li, B., Zhang, Y., Lyu, C., Li, J., Gu, D. (2016). SSG: Sensor Security Guard for Android Smartphones. In: Guo, S., Liao, X., Liu, F., Zhu, Y. (eds) Collaborative Computing: Networking, Applications, and Worksharing. CollaborateCom 2015. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 163. Springer, Cham.

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  • Print ISBN: 978-3-319-28909-0

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