Biometric touchstroke authentication by fuzzy proximity of touch locations

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F18%3A50014730" target="_blank" >RIV/62690094:18450/18:50014730 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S0167739X17326055" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0167739X17326055</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.future.2018.03.030" target="_blank" >10.1016/j.future.2018.03.030</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Biometric touchstroke authentication by fuzzy proximity of touch locations

Popis výsledku v původním jazyce

Advancing touchscreen technologies lead to deployment of biometric passwords that could provide an additional security layer, therefore the presence of a secondary and hidden ghost password could restrain incoming fraud attacks even if the main password is stolen. Apart from the traditional keystroke methods dealing with the inter-key times, on-screen keyboards potentially have new features to extract. Provided that the touchscreen devices capture the touch locations, the users could intentionally create and save a ghost password by touching the predefined regions of the keys in enrollment session. However, while logging in, it is not easy to touch exactly the same points compared to the coordinates saved in enrollment; touching closer coordinates in each attempt is still possible though. From this viewpoint, we introduce a fuzzy classifier with the mathematical foundations of fuzzy proximity and inference for a novel location-based authentication system running on an emulated on-screen keyboard. We mainly focused on extracting the global coordinates that the user is touching in the two-step enrollment, which is so common in conventional registration interfaces. As the main classifier, a fuzzy inference system is implemented with proximity control of the touches for registration and login sessions. The results of the classification procedure are greatly encouraging that only one of sixty four fraud attacks was inadvertently granted; while the false reject rate is 0% and the false accept rate is 1.56% with the equal error rate is 1.61% which indeed represents one of the lowest among the classifiers introduced before (C) 2018 Elsevier B.V. All rights reserved.

Název v anglickém jazyce

Biometric touchstroke authentication by fuzzy proximity of touch locations

Popis výsledku anglicky

Advancing touchscreen technologies lead to deployment of biometric passwords that could provide an additional security layer, therefore the presence of a secondary and hidden ghost password could restrain incoming fraud attacks even if the main password is stolen. Apart from the traditional keystroke methods dealing with the inter-key times, on-screen keyboards potentially have new features to extract. Provided that the touchscreen devices capture the touch locations, the users could intentionally create and save a ghost password by touching the predefined regions of the keys in enrollment session. However, while logging in, it is not easy to touch exactly the same points compared to the coordinates saved in enrollment; touching closer coordinates in each attempt is still possible though. From this viewpoint, we introduce a fuzzy classifier with the mathematical foundations of fuzzy proximity and inference for a novel location-based authentication system running on an emulated on-screen keyboard. We mainly focused on extracting the global coordinates that the user is touching in the two-step enrollment, which is so common in conventional registration interfaces. As the main classifier, a fuzzy inference system is implemented with proximity control of the touches for registration and login sessions. The results of the classification procedure are greatly encouraging that only one of sixty four fraud attacks was inadvertently granted; while the false reject rate is 0% and the false accept rate is 1.56% with the equal error rate is 1.61% which indeed represents one of the lowest among the classifiers introduced before (C) 2018 Elsevier B.V. All rights reserved.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2018

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE

ISSN

0167-739X

e-ISSN

—

Svazek periodika

86

Číslo periodika v rámci svazku

SEPTEMBER

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

10

Strana od-do

71-80

Kód UT WoS článku

000437555800007

EID výsledku v databázi Scopus

2-s2.0-85044443578