857 research outputs found
Touchalytics: On the Applicability of Touchscreen Input as a Behavioral Biometric for Continuous Authentication
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 intra-session
authentication, 2%-3% for inter-session 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 multi-modal biometric authentication
system.Comment: to appear at IEEE Transactions on Information Forensics & Security;
Download data from http://www.mariofrank.net/touchalytics
DEFT: A new distance-based feature set for keystroke dynamics
Keystroke dynamics is a behavioural biometric utilised for user
identification and authentication. We propose a new set of features based on
the distance between keys on the keyboard, a concept that has not been
considered before in keystroke dynamics. We combine flight times, a popular
metric, with the distance between keys on the keyboard and call them as
Distance Enhanced Flight Time features (DEFT). This novel approach provides
comprehensive insights into a person's typing behaviour, surpassing typing
velocity alone. We build a DEFT model by combining DEFT features with other
previously used keystroke dynamic features. The DEFT model is designed to be
device-agnostic, allowing us to evaluate its effectiveness across three
commonly used devices: desktop, mobile, and tablet. The DEFT model outperforms
the existing state-of-the-art methods when we evaluate its effectiveness across
two datasets. We obtain accuracy rates exceeding 99% and equal error rates
below 10% on all three devices.Comment: 12 pages, 5 figures, 3 tables, conference pape
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