東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Authenticating Smart Device Users wi...

Li, Yanyan.

Linked to FindBook

Google Book

Amazon

博客來

Authenticating Smart Device Users with Behavioral Biometrics.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Authenticating Smart Device Users with Behavioral Biometrics./
Author:	Li, Yanyan.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
Description:	123 p.
Notes:	Source: Dissertations Abstracts International, Volume: 80-03, Section: B.
Contained By:	Dissertations Abstracts International80-03B.
Subject:	Computer science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10828400
ISBN:	9780438394759

Authenticating Smart Device Users with Behavioral Biometrics.
Li, Yanyan.

Authenticating Smart Device Users with Behavioral Biometrics. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 123 p.

Source: Dissertations Abstracts International, Volume: 80-03, Section: B.

Thesis (Ph.D.)--University of Arkansas at Little Rock, 2018.

This item must not be sold to any third party vendors.

Designed as personal smart assistant, smartphones and smartwatches have dramatically changed people's lives in every aspect from social network communication, navigation to both online and offline shopping. A large amount of personal data such as messages, emails and payment information is stored in such a device. Such data, if lost, can lead to privacy leakage and financial loss. Therefore, protecting such devices from unauthorized access is crucial. Despite the existence of personal identification numbers (PIN) and unlock patterns for user authentication, they have well known drawbacks. Although physiological biometrics such as fingerprints and facial recognition has become popular, they suffer from privacy and usability issues. Hence, new and user-friendly authentication schemes are needed. In this direction, behavioral biometrics authentication has attracted a lot of attentions. We have conducted a series of studies towards authenticating smart device users using behavioral biometrics. Beginning with signature, we first propose a signature dynamics based electronic consent model to enhance the verification of a remote signer's identity. Secondly, we conduct a comparative study on user behaviors in PIN typing and pattern drawing, and find that the same level of accuracy can be achieved using behaviors only. Thirdly, we propose a 3D in-air gesture based behavioral biometric authentication system for wrist worn smart devices to overcome the PIN typing challenge on a small-screen or no-screen device. Fourthly, we conduct an in-depth analysis of signature, PIN, pattern and 3D gesture behaviors, and then propose a behavior segment based approach for improving the accuracy of behavioral biometric authentication. Lastly, we systematically study the performance of user-created 3D motion gestures and then propose a guideline for creating a secure and usable 3D motion gesture. Through those studies, we demonstrate the viability and effectiveness of the behavioral biometric authentication schemes that we have developed.

ISBN: 9780438394759Subjects--Topical Terms:

523869
Computer science.

Authenticating Smart Device Users with Behavioral Biometrics.
LDR:03083nmm a2200313 4500 001 2205575
005 20190828120318.5
008 201008s2018 ||||||||||||||||| ||eng d
020 $a 9780438394759
035 $a (MiAaPQ)AAI10828400
035 $a (MiAaPQ)ualr:10781
035 $a AAI10828400
040 $a MiAaPQ $c MiAaPQ
100 1 $a Li, Yanyan. $3 2130860
245 1 0 $a Authenticating Smart Device Users with Behavioral Biometrics.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2018
300 $a 123 p.
500 $a Source: Dissertations Abstracts International, Volume: 80-03, Section: B.
500 $a Publisher info.: Dissertation/Thesis.
500 $a Xie, Mengjun.
502 $a Thesis (Ph.D.)--University of Arkansas at Little Rock, 2018.
506 $a This item must not be sold to any third party vendors.
520 $a Designed as personal smart assistant, smartphones and smartwatches have dramatically changed people's lives in every aspect from social network communication, navigation to both online and offline shopping. A large amount of personal data such as messages, emails and payment information is stored in such a device. Such data, if lost, can lead to privacy leakage and financial loss. Therefore, protecting such devices from unauthorized access is crucial. Despite the existence of personal identification numbers (PIN) and unlock patterns for user authentication, they have well known drawbacks. Although physiological biometrics such as fingerprints and facial recognition has become popular, they suffer from privacy and usability issues. Hence, new and user-friendly authentication schemes are needed. In this direction, behavioral biometrics authentication has attracted a lot of attentions. We have conducted a series of studies towards authenticating smart device users using behavioral biometrics. Beginning with signature, we first propose a signature dynamics based electronic consent model to enhance the verification of a remote signer's identity. Secondly, we conduct a comparative study on user behaviors in PIN typing and pattern drawing, and find that the same level of accuracy can be achieved using behaviors only. Thirdly, we propose a 3D in-air gesture based behavioral biometric authentication system for wrist worn smart devices to overcome the PIN typing challenge on a small-screen or no-screen device. Fourthly, we conduct an in-depth analysis of signature, PIN, pattern and 3D gesture behaviors, and then propose a behavior segment based approach for improving the accuracy of behavioral biometric authentication. Lastly, we systematically study the performance of user-created 3D motion gestures and then propose a guideline for creating a secure and usable 3D motion gesture. Through those studies, we demonstrate the viability and effectiveness of the behavioral biometric authentication schemes that we have developed.
590 $a School code: 1204.
650 4 $a Computer science. $3 523869
690 $a 0984
710 2 $a University of Arkansas at Little Rock. $b Computer Science. $3 1286758
773 0 $t Dissertations Abstracts International $g 80-03B.
790 $a 1204
791 $a Ph.D.
792 $a 2018
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10828400