Undermining User Privacy on Mobile Devices Using AI

Date

2019-01-01

Author

Gulmezoglu, Berk
Zankl, Andreas
Tol, M. Caner
Islam, Saad
Eisenbarth, Thomas
Sunar, Berk

Metadata

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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Over the past years, literature has shown that attacks exploiting the microarchitecture of modern processors pose a serious threat to user privacy. This is because applications leave distinct footprints in the processor, which malware can use to infer user activities. In this work, we show that these inference attacks can greatly be enhanced with advanced AI techniques. In particular, we focus on profiling the activity in the last-level cache (LLC) of ARM processors. We employ a simple Prime+Probe based monitoring technique to obtain cache traces, which we classify with deep learning methods including convolutional neural networks. We demonstrate our approach on an off-the-shelf Android phone by launching a successful attack from an unprivileged, zero-permission app in well under a minute. The app detects running applications, opened websites, and streaming videos with up to 98% accuracy and a profiling phase of at most 6 seconds. This is possible, as deep learning compensates measurement disturbances stemming from the inherently noisy LLC monitoring and unfavorable cache characteristics. In summary, our results show that thanks to advanced AI techniques, inference attacks are becoming alarmingly easy to execute in practice. This once more calls for countermeasures that confine microarchitectural leakage and protect mobile phone applications, especially those valuing the privacy of their users.

Subject Keywords

Artificial intelligence, Machine learning, User privacy, Activity inference, Cache attack, Microarchitecture, ARM, Mobile device

URI

https://hdl.handle.net/11511/68219

DOI

https://doi.org/10.1145/3321705.3329804

Conference Name

ACM Asia Conference on Computer and Communications Security (Asia CCS)

Collections

Department of Computer Engineering, Conference / Seminar

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Citation Formats

B. Gulmezoglu, A. Zankl, M. C. Tol, S. Islam, T. Eisenbarth, and B. Sunar, “Undermining User Privacy on Mobile Devices Using AI,” Auckland, New Zealand, 2019, p. 214, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/68219.