Pattern Password Authentication Based on Touching Location

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F15%3A50004177" target="_blank" >RIV/62690094:18450/15:50004177 - isvavai.cz</a>

Výsledek na webu

<a href="http://link.springer.com/chapter/10.1007/978-3-319-24834-9_46" target="_blank" >http://link.springer.com/chapter/10.1007/978-3-319-24834-9_46</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-24834-9_46" target="_blank" >10.1007/978-3-319-24834-9_46</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Pattern Password Authentication Based on Touching Location

Popis výsledku v původním jazyce

Pattern passwords are one of the embedded authentication method of touchscreen devices, however it has some major drawbacks which briefly are identifiability and imitability. The password of the user is noticeable when entering the pattern due to shiningcircles. Therefore, what we put forward in this paper is a novel biometric implementation of a hidden system to pattern password authentication for increasing password security. As opposed to general research concept which extracts touch or keystroke durations, we focused on the touching coordinates calculated the distance of the line between the constant pattern node and the touched place as well as the angle. Using these inputs, we trained the neural network by Gauss-Newton and Levenberg-Marquardt algorithms and conducted the experiments with these trained classifiers.

Název v anglickém jazyce

Pattern Password Authentication Based on Touching Location

Popis výsledku anglicky

Pattern passwords are one of the embedded authentication method of touchscreen devices, however it has some major drawbacks which briefly are identifiability and imitability. The password of the user is noticeable when entering the pattern due to shiningcircles. Therefore, what we put forward in this paper is a novel biometric implementation of a hidden system to pattern password authentication for increasing password security. As opposed to general research concept which extracts touch or keystroke durations, we focused on the touching coordinates calculated the distance of the line between the constant pattern node and the touched place as well as the angle. Using these inputs, we trained the neural network by Gauss-Newton and Levenberg-Marquardt algorithms and conducted the experiments with these trained classifiers.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2015

Kód důvěrnosti údajů

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

Název statě ve sborníku

Intelligent data engineering and automated learning (IDEAL 2015)

ISBN

978-3-319-24833-2

ISSN

0302-9743

e-ISSN

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Počet stran výsledku

9

Strana od-do

395-403

Název nakladatele

Springer

Místo vydání

Berlin

Místo konání akce

Wroclaw

Datum konání akce

14. 10. 2015

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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