ACCessory: password inference using accelerometers on smartphones
@inproceedings{Owusu2012ACCessoryPI, title={ACCessory: password inference using accelerometers on smartphones}, author={Emmanuel Owusu and Jun Han and Sauvik Das and Adrian Perrig and Joy Ying Zhang}, booktitle={Workshop on Mobile Computing Systems and Applications}, year={2012} }
We show that accelerometer readings are a powerful side channel that can be used to extract entire sequences of entered text on a smart-phone touchscreen keyboard. This possibility is a concern for two main reasons. First, unauthorized access to one's keystrokes is a serious invasion of privacy as consumers increasingly use smartphones for sensitive transactions. Second, unlike many other sensors found on smartphones, the accelerometer does not require special privileges to access on current…
386 Citations
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References
SHOWING 1-10 OF 25 REFERENCES
TouchLogger: Inferring Keystrokes on Touch Screen from Smartphone Motion
- Computer ScienceHotSec
- 2011
This work describes a new side channel, motion, on touch screen smartphones with only soft keyboards, and developed TouchLogger, an Android application that extracts features from device orientation data to infer keystrokes.
ACComplice: Location inference using accelerometers on smartphones
- Physics, Computer Science2012 Fourth International Conference on Communication Systems and Networks (COMSNETS 2012)
- 2012
It is demonstrated that accelerometers can be used to locate a device owner to within a 200 meter radius of the true location and are comparable to the typical accuracy for handheld global positioning systems.
(sp)iPhone: decoding vibrations from nearby keyboards using mobile phone accelerometers
- Computer ScienceCCS '11
- 2011
It is demonstrated that an application with access to accelerometer readings on a modern mobile phone can use such information to recover text entered on a nearby keyboard, and the potential to recover significant information from the vicinity of a mobile device without gaining access to resources generally considered to be the most likely sources of leakage.
Defending against sensor-sniffing attacks on mobile phones
- Computer ScienceMobiHeld '09
- 2009
This work explores the vulnerability where attackers snoop on users by sniffing on their mobile phone sensors, such as the microphone, camera, and GPS receiver, and proposes a general framework for such solutions.
Soundcomber: A Stealthy and Context-Aware Sound Trojan for Smartphones
- Computer ScienceNDSS
- 2011
This work presents Soundcomber, a Trojan with few and innocuous permissions, that can extract a small amount of targeted private information from the audio sensor of the phone, and performs efficient, stealthy local extraction, thereby greatly reducing the communication cost for delivering stolen data.
Timing Analysis of Keystrokes and Timing Attacks on SSH
- Computer ScienceUSENIX Security Symposium
- 2001
A statistical study of users' typing patterns is performed and it is shown that these patterns reveal information about the keys typed, and that timing leaks open a new set of security risks, and hence caution must be taken when designing this type of protocol.
Keyboard acoustic emanations
- Computer ScienceIEEE Symposium on Security and Privacy, 2004. Proceedings. 2004
- 2004
We show that PC keyboards, notebook keyboards, telephone and ATM pads are vulnerable to attacks based on differentiating the sound emanated by different keys. Our attack employs a neural network to…
"Are You with Me?" - Using Accelerometers to Determine If Two Devices Are Carried by the Same Person
- Computer SciencePervasive
- 2004
A method to determine if two devices are carried by the same person, by analyzing walking data recorded by low-cost MEMS accelerometers using the coherence function, a measure of linear correlation in the frequency domain, is presented.
uWave: Accelerometer-based personalized gesture recognition and its applications
- Computer Science2009 IEEE International Conference on Pervasive Computing and Communications
- 2009
Stealthy video capturer: a new video-based spyware in 3G smartphones
- Computer ScienceWiSec '09
- 2009
This work designs a new video-based spyware, called Stealthy Video Capturer (SVC), which can secretly record video information for the third party, greatly compromising Smartphone users' privacy.