C4.5: programs for machine learning
C4.5: programs for machine learning
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Smart-Its Friends: A Technique for Users to Easily Establish Connections between Smart Artefacts
UbiComp '01 Proceedings of the 3rd international conference on Ubiquitous Computing
Keyboard acoustic emanations revisited
Proceedings of the 12th ACM conference on Computer and communications security
eWatch: A Wearable Sensor and Notification Platform
BSN '06 Proceedings of the International Workshop on Wearable and Implantable Body Sensor Networks
Stealthy video capturer: a new video-based spyware in 3G smartphones
Proceedings of the second ACM conference on Wireless network security
Inferring Identity Using Accelerometers in Television Remote Controls
Pervasive '09 Proceedings of the 7th International Conference on Pervasive Computing
Defending against sensor-sniffing attacks on mobile phones
Proceedings of the 1st ACM workshop on Networking, systems, and applications for mobile handhelds
Activity recognition from accelerometer data
IAAI'05 Proceedings of the 17th conference on Innovative applications of artificial intelligence - Volume 3
uWave: Accelerometer-based personalized gesture recognition and its applications
Pervasive and Mobile Computing
TouchLogger: inferring keystrokes on touch screen from smartphone motion
HotSec'11 Proceedings of the 6th USENIX conference on Hot topics in security
(sp)iPhone: decoding vibrations from nearby keyboards using mobile phone accelerometers
Proceedings of the 18th ACM conference on Computer and communications security
TapLogger: inferring user inputs on smartphone touchscreens using on-board motion sensors
Proceedings of the fifth ACM conference on Security and Privacy in Wireless and Mobile Networks
Tapprints: your finger taps have fingerprints
Proceedings of the 10th international conference on Mobile systems, applications, and services
On the practicality of motion based keystroke inference attack
TRUST'12 Proceedings of the 5th international conference on Trust and Trustworthy Computing
Fingerprint attack against touch-enabled devices
Proceedings of the second ACM workshop on Security and privacy in smartphones and mobile devices
Exploring user preferences for privacy interfaces in mobile sensing applications
Proceedings of the 11th International Conference on Mobile and Ubiquitous Multimedia
Practicality of accelerometer side channels on smartphones
Proceedings of the 28th Annual Computer Security Applications Conference
ScreenPass: secure password entry on touchscreen devices
Proceeding of the 11th annual international conference on Mobile systems, applications, and services
Sensing-enabled channels for hard-to-detect command and control of mobile devices
Proceedings of the 8th ACM SIGSAC symposium on Information, computer and communications security
ACM SIGMOBILE Mobile Computing and Communications Review
When kids' toys breach mobile phone security
Proceedings of the 2013 ACM SIGSAC conference on Computer & communications security
Identity, location, disease and more: inferring your secrets from android public resources
Proceedings of the 2013 ACM SIGSAC conference on Computer & communications security
Seeing double: reconstructing obscured typed input from repeated compromising reflections
Proceedings of the 2013 ACM SIGSAC conference on Computer & communications security
Proceedings of International Conference on Advances in Mobile Computing & Multimedia
Can Smartphone Users Turn Off Tracking Service Settings?
Proceedings of International Conference on Advances in Mobile Computing & Multimedia
ipShield: a framework for enforcing context-aware privacy
NSDI'14 Proceedings of the 11th USENIX Conference on Networked Systems Design and Implementation
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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 smartphone OSes. We show that accelerometer measurements can be used to extract 6-character passwords in as few as 4.5 trials (median).