Touchalytics: On the Applicability of Touchscreen Input as a Behavioral Biometric for Continuous Authentication

By Mario Frank, R. Biedert, Eugene Ma, I. Martinovic, Dawn Song. IEEE Transactions on Information Forensics and Security, 2012

894 citations recorded by the source corpus.

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.

Subfields: Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/7f42d54971352678ef25ec411fc96cdcc3fdae30

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Information Systems

1. Vishal M. Patel
2. He Zhang
3. Ramalingam Chellappa
4. Prateek Mittal
5. Supriyo Chakraborty
6. Changchang Liu
7. Deepak Chandra
8. Brandon Barbello

In Signal Processing

1. Vishal M. Patel
2. Ramalingam Chellappa
3. He Zhang
4. Sumit Shekhar
5. Prateek Mittal
6. Supriyo Chakraborty
7. Changchang Liu
8. Deepak Chandra

Citations in the corpus

• Zero Trust Architecture (ZTA): A Comprehensive Survey (2022, background): “For example, [33], [34] use the touch dynamics on the screen and extract features such as orientation of the finger, pressure exerted on the screen, area occluded and time instances.”
• DEEProtect: Enabling Inference-based Access Control on Mobile Sensing Applications (2017, methodology): “We consider the following case studies that further illustrate this privacy-utility tradeoff: • Case Study 1: A user shares accelerometer data with an authentication app that performs keyless validation of user identity (useful inference) [31], [47], [58].”
• You Are How You Touch: User Verification on Smartphones via Tapping Behaviors (2014, background): “conducted a study on touch input analysis for smartphone user authentication, which is referred to as touch biometrics [10].”
• HMOG: New Behavioral Biometric Features for Continuous Authentication of Smartphone Users (2015, neutral): “, [6], [21]), and (2) touchscreen interaction (see, e.”
• Continuous User Authentication on Mobile Devices: Recent progress and remaining challenges (2016, methodology): “Swipes of eight different users while reading text [13].”
• Domain adaptive sparse representation-based classification (2015, methodology): “Examples of such systems include screen touch gesture-based recognition [25] and gait-based recognition [26].”