Mobile devices typically rely on entry-point and other one-time authentication mechanisms such as a password,
PIN, fingerprint, iris, or face. But these authentication types are prone to a wide attack vector and worse
1 INTRODUCTION
Currently smartphones are predominantly protected
a patterned password is prone to smudge attacks, and
fingerprint scanning is prone to spoof attacks. Other
forms of attacks include video capture and shoulder
surfing. Given the increasingly important roles
smartphones play in e-commerce and other operations
where security is crucial, there lies a strong need
of continuous authentication mechanisms to complement
and enhance one-time authentication such that
even if the authentication at the point of login gets
compromised, the device is still unobtrusively protected
by additional security measures in a continuous
fashion.
The research community has investigated several
continuous authentication mechanisms based on
unique human behavioral traits, including typing,
swiping, and gait. To this end, we focus on investigating
physiological
traits. While interacting with hand-held devices,
individuals strive to achieve stability and precision.
This is because a certain degree of stability is
required in order to manipulate and interact successfully
with smartphones, while precision is needed for
tasks such as touching or tapping a small target on
the touch screen (Sitov´a et al., 2015). As a result,
to achieve stability and precision, individuals tend to
develop their own postural preferences, such as holding
a phone with one or both hands, supporting hands
on the sides of upper torso and interacting, keeping
the phone on the table and typing with the preferred
finger, setting the phone on knees while sitting crosslegged
and typing, supporting both elbows on chair
handles and typing. On the other hand, physiological
traits, such as hand-size, grip strength, muscles, age,
424
Ray, A., Hou, D., Schuckers, S. and Barbir, A.
Continuous Authentication based on Hand Micro-movement during Smartphone Form Filling by Seated Human Subjects.
DOI: 10.5220/0010225804240431
In Proceedings of the 7th International Conference on Information Systems Security and Privacy (ICISSP 2021), pages 424-431
ISBN: 978-989-758-491-6
Copyrightc 2021 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
still, once compromised, fail to protect the user’s account and data. In contrast, continuous authentication,
based on traits of human behavior, can offer additional security measures in the device to authenticate against
unauthorized users, even after the entry-point and one-time authentication has been compromised. To this end, we have collected a new data-set of multiple behavioral biometric modalities (49 users) when a user fills out an account recovery form in sitting using an Android app. These include motion events (acceleration and angular velocity), touch and swipe events, keystrokes, and pattern tracing. In this paper, we focus on authentication based on motion events by evaluating a set of score level fusion techniques to authenticate users based on the acceleration and angular velocity data. The best EERs of 2.4% and 6.9% for intra- and inter-session respectively, are achieved by fusing acceleration and angular velocity using Nandakumar et al.’s likelihood ratio (LR) based score fusion.
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A Scalable Solution for Signaling Face Touches to Reduce the Spread of Surface-based Pathogens
Hand-to-Face transmission has been estimated to be a minority, yet non-negligible, vector of COVID-19 transmission and a major vector for multiple other pathogens. At the same time, as it cannot be effectively addressed with mainstream protection measures, such as wearing masks or tracing contacts, it remains largely untackled. To help address this issue, we have developed Saving Face - an app that alerts users when they are about to touch their faces, by analyzing the distortion patterns in the ultrasound signal emitted by their earphones. The system only relies on pre-existing hardware (a smartphone with generic earphones), which allows it to be rapidly scalable to billions of smartphone users worldwide. This paper describes the design, implementation and evaluation of the system, as well as the results of a user study testing the solution's accuracy, robustness, and user experience during various day-to-day activities (93.7% Sensitivity and 91.5% Precision, N=10). While this paper focuses on the system's application to detecting hand-to-face gestures, the technique can also be applicable to other types of gestures and gesture-based applications.
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- Award ID(s):
- 2032704
- NSF-PAR ID:
- 10248892
- Date Published:
- Journal Name:
- Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
- Volume:
- 5
- Issue:
- 1
- ISSN:
- 2474-9567
- Page Range / eLocation ID:
- 1 to 22
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1145/3448121
