5,516 research outputs found
Forgery-Resistant Touch-based Authentication on Mobile Devices
Mobile devices store a diverse set of private user data and have gradually
become a hub to control users' other personal Internet-of-Things devices.
Access control on mobile devices is therefore highly important. The widely
accepted solution is to protect access by asking for a password. However,
password authentication is tedious, e.g., a user needs to input a password
every time she wants to use the device. Moreover, existing biometrics such as
face, fingerprint, and touch behaviors are vulnerable to forgery attacks.
We propose a new touch-based biometric authentication system that is passive
and secure against forgery attacks. In our touch-based authentication, a user's
touch behaviors are a function of some random "secret". The user can
subconsciously know the secret while touching the device's screen. However, an
attacker cannot know the secret at the time of attack, which makes it
challenging to perform forgery attacks even if the attacker has already
obtained the user's touch behaviors. We evaluate our touch-based authentication
system by collecting data from 25 subjects. Results are promising: the random
secrets do not influence user experience and, for targeted forgery attacks, our
system achieves 0.18 smaller Equal Error Rates (EERs) than previous touch-based
authentication.Comment: Accepted for publication by ASIACCS'1
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
Implicit Smartphone User Authentication with Sensors and Contextual Machine Learning
Authentication of smartphone users is important because a lot of sensitive
data is stored in the smartphone and the smartphone is also used to access
various cloud data and services. However, smartphones are easily stolen or
co-opted by an attacker. Beyond the initial login, it is highly desirable to
re-authenticate end-users who are continuing to access security-critical
services and data. Hence, this paper proposes a novel authentication system for
implicit, continuous authentication of the smartphone user based on behavioral
characteristics, by leveraging the sensors already ubiquitously built into
smartphones. We propose novel context-based authentication models to
differentiate the legitimate smartphone owner versus other users. We
systematically show how to achieve high authentication accuracy with different
design alternatives in sensor and feature selection, machine learning
techniques, context detection and multiple devices. Our system can achieve
excellent authentication performance with 98.1% accuracy with negligible system
overhead and less than 2.4% battery consumption.Comment: Published on the IEEE/IFIP International Conference on Dependable
Systems and Networks (DSN) 2017. arXiv admin note: substantial text overlap
with arXiv:1703.0352
Implicit Sensor-based Authentication of Smartphone Users with Smartwatch
Smartphones are now frequently used by end-users as the portals to
cloud-based services, and smartphones are easily stolen or co-opted by an
attacker. Beyond the initial log-in mechanism, it is highly desirable to
re-authenticate end-users who are continuing to access security-critical
services and data, whether in the cloud or in the smartphone. But attackers who
have gained access to a logged-in smartphone have no incentive to
re-authenticate, so this must be done in an automatic, non-bypassable way.
Hence, this paper proposes a novel authentication system, iAuth, for implicit,
continuous authentication of the end-user based on his or her behavioral
characteristics, by leveraging the sensors already ubiquitously built into
smartphones. We design a system that gives accurate authentication using
machine learning and sensor data from multiple mobile devices. Our system can
achieve 92.1% authentication accuracy with negligible system overhead and less
than 2% battery consumption.Comment: Published in Hardware and Architectural Support for Security and
Privacy (HASP), 201
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