Continuous mobile authentication using touchscreen gestures

Abstract

Securing the sensitive data stored and accessed from mobile devices makes user authentication a problem of paramount importance. The tension between security and usability renders however the task of user authentication on mobile devices a challenging task. This paper introduces FAST (Fingergestures Authentication System using Touchscreen), a novel touchscreen based authentication approach on mobile devices. Besides extracting touch data from touchscreen equipped smartphones, FAST complements and validates this data using a digital sensor glove that we have built using off-the-shelf components. FAST leverages state-of-the-art classification algorithms to provide transparent and continuous mobile system protection. A notable feature is FAST 's continuous, user transparent postlogin authentication. We use touch data collected from 40 users to show that FAST achieves a False Accept Rate (FAR) of 4.66% and False Reject Rate of 0.13% for the continuous post-login user authentication. The low FAR and FRR values indicate that FAST provides excellent post-login access security, without disturbing the honest mobile users.

The Effectiveness of Dynamic Features of Finger Based Gestures on Smartphones’ Touchscreens for User Identification

“…Many researchers are working on the concept of user identification using finger based gesture; some of them also introduced new ideas to provide more secure approach related to text or signature passwords. One of the studies that examined finger based gesture for user identification was conducted by [1]. The researchers Tao Feng and Nguyen in [1] use touch data collected from 40 users based on sensor gloves in order to collect information of finger movement for six types of gestures: down to up swipe, up to down swipe, left to right swipe, right to left swipe, zooming, and zoom-out.…”

“…One of the studies that examined finger based gesture for user identification was conducted by [1]. The researchers Tao Feng and Nguyen in [1] use touch data collected from 40 users based on sensor gloves in order to collect information of finger movement for six types of gestures: down to up swipe, up to down swipe, left to right swipe, right to left swipe, zooming, and zoom-out. The two features used in the study are: the length of touch input sequences and the authentication threshold (i.e., number of accepted touch inputs during one sequence).…”

“…As smartphone devices are easily lost or stolen, the problem of securing the user access to this data considers one of paramount importance [1].…”

“…The need for strong authentication is influenced by the input methodology of touchscreen devices and the different expectations of user for interaction models [1]. As shown in a study [3] over 3.3 million leaked passwords, number of their list was still "123456".…”

“…As shown in a study [3] over 3.3 million leaked passwords, number of their list was still "123456". Moreover, the additional cost makes biometric authentication techniques to be still unpopular on mobile devices [1].…”