Authors:
Vincenzo Gattulli
;
Donato Impedovo
;
Tonino Palmisano
and
Lucia Sarcinella
Affiliation:
Department of Computer Science, University of Studies of Bari “Aldo Moro”, Via Edoardo Orabona, 4, 70125 Bari, Italy
Keyword(s):
Android Smartphone, Machine Learning, Fixed Tasks, Shallow Learning, Continuous Authentication.
Abstract:
Mobile devices feature a variety of knowledge-based authentications such as PINs, passwords, and lock sequences. The weakness of these approaches is that once leaked and/or intercepted, the control over the device is lost and no more authentication steps are required. In this paper, the efficiency of a set of ML algorithms in authenticating users is evaluated with the aim of understanding which are the best tasks to use by submitting Fixed Tasks, which simulate the use of a device in daily life, through Touch Behaviour and motion sensors installed in the device itself. Next, a social problem is posed, in which an attempt is made to understand whether a group of subjects at a trial performed the assigned tasks correctly without permitting other people to do them instead.