Advances in kernel methods: support vector learning
Advances in kernel methods: support vector learning
Machine Learning
Securing passwords against dictionary attacks
Proceedings of the 9th ACM conference on Computer and communications security
Bayesian Averaging of Classifiers and the Overfitting Problem
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Ensemble Methods in Machine Learning
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Ensemble selection from libraries of models
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Proceedings of the 6th ACM conference on Embedded network sensor systems
Defending against sensor-sniffing attacks on mobile phones
Proceedings of the 1st ACM workshop on Networking, systems, and applications for mobile handhelds
Keyboard acoustic emanations revisited
ACM Transactions on Information and System Security (TISSEC)
SurroundSense: mobile phone localization via ambience fingerprinting
Proceedings of the 15th annual international conference on Mobile computing and networking
Combining predictions for accurate recommender systems
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Compromising electromagnetic emanations of wired and wireless keyboards
SSYM'09 Proceedings of the 18th conference on USENIX security symposium
Timing attacks on PIN input devices
Proceedings of the 17th ACM conference on Computer and communications security
A survey of mobile phone sensing
IEEE Communications Magazine
The Jigsaw continuous sensing engine for mobile phone applications
Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems
TaintDroid: an information-flow tracking system for realtime privacy monitoring on smartphones
OSDI'10 Proceedings of the 9th USENIX conference on Operating systems design and implementation
Using mobile phones to write in air
MobiSys '11 Proceedings of the 9th international conference on Mobile systems, applications, and services
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
A Tale of One City: Using Cellular Network Data for Urban Planning
IEEE Pervasive Computing
ACCessory: password inference using accelerometers on smartphones
Proceedings of the Twelfth Workshop on Mobile Computing Systems & Applications
Cardiovascular Monitoring Using Earphones and a Mobile Device
IEEE Pervasive Computing
Practicality of accelerometer side channels on smartphones
Proceedings of the 28th Annual Computer Security Applications Conference
Continuous Remote Mobile Identity Management Using Biometric Integrated Touch-Display
MICROW '12 Proceedings of the 2012 45th Annual IEEE/ACM International Symposium on Microarchitecture Workshops
SilentSense: silent user identification via touch and movement behavioral biometrics
Proceedings of the 19th annual international conference on Mobile computing & networking
ACM SIGMOBILE Mobile Computing and Communications Review
PIN skimmer: inferring PINs through the camera and microphone
Proceedings of the Third ACM workshop on Security and privacy in smartphones & mobile devices
ipShield: a framework for enforcing context-aware privacy
NSDI'14 Proceedings of the 11th USENIX Conference on Networked Systems Design and Implementation
Hi-index | 0.00 |
This paper shows that the location of screen taps on modern smartphones and tablets can be identified from accelerometer and gyroscope readings. Our findings have serious implications, as we demonstrate that an attacker can launch a background process on commodity smartphones and tablets, and silently monitor the user's inputs, such as keyboard presses and icon taps. While precise tap detection is nontrivial, requiring machine learning algorithms to identify fingerprints of closely spaced keys, sensitive sensors on modern devices aid the process. We present TapPrints, a framework for inferring the location of taps on mobile device touch-screens using motion sensor data combined with machine learning analysis. By running tests on two different off-the-shelf smartphones and a tablet computer we show that identifying tap locations on the screen and inferring English letters could be done with up to 90% and 80% accuracy, respectively. By optimizing the core tap detection capability with additional information, such as contextual priors, we are able to further magnify the core threat.