This is the companion website for A Comparative Evaluation of Implicit Authentication Schemes, by Hassan Khan, Aaron Atwater, and Urs Hengartner. Downloads of our implicit authentication schemes are available on the Downloads page.
Title: A Comparative Evaluation of Implicit
Authentication Schemes
Abstract: Implicit authentication (IA) schemes use behavioural biometrics to
continuously and transparently authenticate mobile device users. Several
IA schemes have been proposed by researchers which employ different
behavioural features and provide reasonable detection accuracy. While
these schemes work in principle, it is difficult to comprehend from
these individual efforts which schemes work best (in terms of detection
accuracy, detection delay and processing complexity) under different
operating conditions (in terms of attack scenarios and availability of
training and classification data). Furthermore, it is critical to
evaluate these schemes on unbiased, real-world datasets to determine
their efficacy in realistic operating conditions. In this paper, we
evaluate six diverse IA schemes on four independently collected datasets
from over 300 participants. We first evaluate these schemes in terms of:
accuracy; training time and delay on real-world datasets; detection
delay; processing and memory complexity for feature extraction, training
and classification operations; vulnerability to mimicry attacks; and
deployment issues on mobile platforms. We also leverage our real-world
device usage traces to determine the proportion of time these schemes
are able to afford protection to device owners. Based on our
evaluations, we identify: 1) promising IA schemes with high detection
accuracy, low performance overhead, and near real-time detection delays,
2) common pitfalls in contemporary IA evaluation methodology, and 3)
open challenges for IA research. Finally, we provide an open source
implementation of the IA schemes evaluated in this work that can be used
for performance benchmarking by future IA research.
