Auflistung nach Schlagwort "continuous authentication"
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- KonferenzbeitragFast and Accurate Continuous User Authentication by Fusion of Instance-based, Free-text Keystroke Dynamics(BIOSIG 2019 - Proceedings of the 18th International Conference of the Biometrics Special Interest Group, 2019) Ayotte, Blaine; Banavar, Mahesh K.; Hou, Daqing; Schuckers, StephanieKeystroke dynamics study the way in which users input text via their keyboards, which is unique to each individual, and can form a component of a behavioral biometric system to improve existing account security. Keystroke dynamics systems on free-text data use n-graphs that measure the timing between consecutive keystrokes to distinguish between users. Many algorithms require 500, 1,000, or more keystrokes to achieve EERs of below 10%. In this paper, we propose an instancebased graph comparison algorithm to reduce the number of keystrokes required to authenticate users. Commonly used features such as monographs and digraphs are investigated. Feature importance is determined and used to construct a fused classifier. Detection error tradeoff (DET) curves are produced with different numbers of keystrokes. The fused classifier outperforms the state-of-the-art with EERs of 7.9%, 5.7%, 3.4%, and 2.7% for test samples of 50, 100, 200, and 500 keystrokes.
- KonferenzbeitragJekyll and Hyde: On The Double-Faced Nature of Smart-Phone Sensor Noise Injection(BIOSIG 2018 - Proceedings of the 17th International Conference of the Biometrics Special Interest Group, 2018) Matovu, Richard; Serwadda, Abdul; Irakiza, David; Griswold-Steiner, IsaacTo combat privacy attacks that exploit the motion and orientation sensors embedded in mobile devices, a number of recent works have proposed noise injection schemes that degrade the quality of sensor data. Much as these schemes have been shown to thwart the attacks, the impact of noise injection on continuous authentication schemes proposed for mobile and wearable devices has never been studied. In this paper, we empirically tackle this question based on two widely studied continuous authentication applications (i.e., gait and handwriting authentication). Through a series of machine learning and statistical techniques, we show that the thresholds of noise needed to overcome the attacks would significantly degrade the performance of the continuous authentication applications. The paper argues against noise injection as a defense against attacks that exploit motion and orientation sensor data on mobile and wearable devices.