1,302 research outputs found
Mobiles and wearables: owner biometrics and authentication
We discuss the design and development of HCI models for authentication based on gait and gesture that can be supported by mobile and wearable equipment. The paper proposes to use such biometric behavioral traits for partially transparent and continuous authentication by means of behavioral patterns. © 2016 Copyright held by the owner/author(s)
Assentication: User Deauthentication and Lunchtime Attack Mitigation with Seated Posture Biometric
Biometric techniques are often used as an extra security factor in
authenticating human users. Numerous biometrics have been proposed and
evaluated, each with its own set of benefits and pitfalls. Static biometrics
(such as fingerprints) are geared for discrete operation, to identify users,
which typically involves some user burden. Meanwhile, behavioral biometrics
(such as keystroke dynamics) are well suited for continuous, and sometimes more
unobtrusive, operation. One important application domain for biometrics is
deauthentication, a means of quickly detecting absence of a previously
authenticated user and immediately terminating that user's active secure
sessions. Deauthentication is crucial for mitigating so called Lunchtime
Attacks, whereby an insider adversary takes over (before any inactivity timeout
kicks in) authenticated state of a careless user who walks away from her
computer. Motivated primarily by the need for an unobtrusive and continuous
biometric to support effective deauthentication, we introduce PoPa, a new
hybrid biometric based on a human user's seated posture pattern. PoPa captures
a unique combination of physiological and behavioral traits. We describe a low
cost fully functioning prototype that involves an office chair instrumented
with 16 tiny pressure sensors. We also explore (via user experiments) how PoPa
can be used in a typical workplace to provide continuous authentication (and
deauthentication) of users. We experimentally assess viability of PoPa in terms
of uniqueness by collecting and evaluating posture patterns of a cohort of
users. Results show that PoPa exhibits very low false positive, and even lower
false negative, rates. In particular, users can be identified with, on average,
91.0% accuracy. Finally, we compare pros and cons of PoPa with those of several
prominent biometric based deauthentication techniques
Investigating the impact of combining handwritten signature and keyboard keystroke dynamics for gender prediction
© 2019 IEEE. The use of soft-biometric data as an auxiliary tool on user identification is already well known. Gender, handorientation and emotional state are some examples which can be called soft-biometrics. These soft-biometric data can be predicted directly from the biometric templates. It is very common to find researches using physiological modalities for soft-biometric prediction, but behavioural biometric is often not well explored for this context. Among the behavioural biometric modalities, keystroke dynamics and handwriting signature have been widely explored for user identification, including some soft-biometric predictions. However, in these modalities, the soft-biometric prediction is usually done in an individual way. In order to fill this space, this study aims to investigate whether the combination of those two biometric modalities can impact the performance of a soft-biometric data, gender prediction. The main aim is to assess the impact of combining data from two different biometric sources in gender prediction. Our findings indicated gains in terms of performance for gender prediction when combining these two biometric modalities, when compared to the individual ones
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