Touchalytics: On the Applicability of Touchscreen Input as a Behavioral Biometric for Continuous Authentication
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TLDR
A classification framework that learns the touch behavior of a user during an enrollment phase and is able to accept or reject the current user by monitoring interaction with the touch screen is proposed.Abstract:
We investigate whether a classifier can continuously authenticate users based on the way they interact with the touchscreen of a smart phone. We propose a set of 30 behavioral touch features that can be extracted from raw touchscreen logs and demonstrate that different users populate distinct subspaces of this feature space. In a systematic experiment designed to test how this behavioral pattern exhibits consistency over time, we collected touch data from users interacting with a smart phone using basic navigation maneuvers, i.e., up-down and left-right scrolling. We propose a classification framework that learns the touch behavior of a user during an enrollment phase and is able to accept or reject the current user by monitoring interaction with the touch screen. The classifier achieves a median equal error rate of 0% for intrasession authentication, 2%-3% for intersession authentication, and below 4% when the authentication test was carried out one week after the enrollment phase. While our experimental findings disqualify this method as a standalone authentication mechanism for long-term authentication, it could be implemented as a means to extend screen-lock time or as a part of a multimodal biometric authentication system.read more
Citations
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I-Am: Implicitly Authenticate Me—Person Authentication on Mobile Devices Through Ear Shape and Arm Gesture
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An HMM-based behavior modeling approach for continuous mobile authentication
TL;DR: A Hidden Markov Model (HMM) based behavioral template training approach is presented, which does not require training data from other subjects other than the owner of the mobile to achieve continuous authentication for touch interface based mobile devices.
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Touch Well Before Use: Intuitive and Secure Authentication for IoT Devices
TL;DR: This work presents a virtual sensing technique that allows IoT devices to virtually sense user 'petting' (in the form of some very simple touches for about 2 seconds) on the devices and builds a secure and intuitive authentication method that authenticates device users by comparing the petting operations sensed by devices and those captured by the user wristband.
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Authenticating users through their arm movement patterns
TL;DR: Four continuous authentication designs by using the characteristics of arm movements while individuals walk are proposed by using four classifiers, namely, k nearest neighbors (k-NN) with Euclidean distance, Logistic Regression, Multilayer Perceptrons, and Random Forest resulting in a total of sixteen authentication mechanisms.
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