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DroidFP: A Zero-Permission Detection Framework for Android Devices Based on Gated Recurrent Unit

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Science of Cyber Security (SciSec 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13580))

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Abstract

The Android market is booming, and the unique identification of user devices by APPs helps track attackers. Although Google has designed unique identifiers such as IMEI, users’ permission is required to obtain such identifiers. There are currently some methods to forge such identifiers through the Android emulator. To resist identifier forgery attacks and accurately identify Android devices, in this paper, we propose a zero-permission detection framework for an Android device called DroidFP, which does not require any permissions to generate an Android fingerprint. DroidFP consists of emulator detection and Android device identification, which uses GRU to associate the fingerprint changes for the same device. We collected actual device data and emulators data using data enhancement to expand the dataset to 16200. Experiment results show that emulator detection is essential, and DroidFP has high robustness and accuracy in device identification.

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Notes

  1. 1.

    Our feature set is publicly available at https://github.com/ImCaviar/DroidFP.

References

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Acknowledgements

We thank the reviewers for their valuable comments and suggestions. This work is supported by the Youth Innovation Promotion Association CAS (No.2019163), the Strategic Priority Research Program of Chinese Academy of Sciences (No. XDC02040100), the Key Laboratory of Network Assessment Technology at Chinese Academy of Sciences and Beijing Key Laboratory of Network security and Protection Technology.

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Correspondence to Langping Chen .

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© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Liu, X., Zhao, W., Chen, L., Liu, Q. (2022). DroidFP: A Zero-Permission Detection Framework for Android Devices Based on Gated Recurrent Unit. In: Su, C., Sakurai, K., Liu, F. (eds) Science of Cyber Security. SciSec 2022. Lecture Notes in Computer Science, vol 13580. Springer, Cham. https://doi.org/10.1007/978-3-031-17551-0_24

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  • DOI: https://doi.org/10.1007/978-3-031-17551-0_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-17550-3

  • Online ISBN: 978-3-031-17551-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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