Abstract
To sustain a wide range of applications, to attract app developers, and to foster innovation, smartphones come bundled with a plethora of sensors and peripherals that apps can use: an accelerometer for sensing movements, a gyroscope for sensing orientation, a magnetometer for sensing magnetic fields, a camera for taking pictures, and so on. Although initially considered innocuous, researchers have repeatedly demonstrated that sensors leak unintended information. These leaks could help malicious apps reconstruct banking PINs, Dual-Tone Multi-Frequency tones or spoken secrets. In this chapter, we provide an introduction to such attacks by reviewing their principles and effectiveness. We then illustrate them through two recent research papers. Such attacks are currently in their early stage. But we hope to raise awareness of their existence amongst practitioners, as they are likely to become more effective and prevalent on smart mobile platforms in the future.
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Notes
- 1.
Arthur Lee Samuel, “Some Studies in Machine Learning Using the Game of Checkers”, IBM Journal of Research and Development, 44, 1.2 (1959).
- 2.
Mitchell Tom, Machine Learning, (McGraw Hill, 1997).
Further Reading
Roman, S., Kehuan, Z., Xiaoyong, Z., Mehool, I., Apu, K., & Xiaofeng, W. (2011). Soundcomber: A stealthy and context-aware sound trojan for smartphones. Proceedings of NDSS, 11, 17–33.
Sarfraz, N., & Cecilia, M. (2014). Mining users’ significant driving routes with low-power sensors. Proceedings of the 12th ACM Conference on Embedded Network Sensor Systems, ACM, pp. 236–250.
Yan, M., Aaaron, S., Gunaa, A. V., Dan, B., & Gabi, N. (2015). Powerspy: Location tracking using mobile device power analysis. Proceedings of the 24th USENIX Security Symposium, USENIX Association, August, pp. 785–800.
Bibliography
Laurent, S., & Anderson, R. (2013). 3rd Annual ACM CCS workshop on security and privacy in smartphones and mobile devices.
Liang, C., & Hao, C. (2011). 6th USENIX workshop on hot topics in security.
Mitchell, T. (1997). Machine learning. McGraw Hill: New York.
Samuel, A. L. (1959). Some studies in machine learning using the game of checkers. IBM Journal of Research and Development, 44(1.2).
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Simon, L. (2016). A Gentle Introduction to Side Channel Attacks on Smartphones. In: Batiz-Lazo, B., Efthymiou, L. (eds) The Book of Payments. Palgrave Macmillan, London. https://doi.org/10.1057/978-1-137-60231-2_26
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DOI: https://doi.org/10.1057/978-1-137-60231-2_26
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