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Author Ngugi, Benjamin ♦ Kahn, Beverly K ♦ Tremaine, Marilyn
Source ACM Digital Library
Content type Text
Publisher Association for Computing Machinery (ACM)
File Format PDF
Copyright Year ©2011
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Biometric ♦ Keypads ♦ Learning ♦ Typing patterns
Abstract The use of stolen personal-identifying information, like Social Security numbers, to commit identity fraud continues to be a major problem. The fact that an impostor can pass as the genuine user by possession of stolen identification information is a weakness in current authentication systems. Adding a biometric layer to the traditional knowledge and token-based authentication systems is one way to counter this problem. Physical biometrics, such as fingerprint systems, are highly accurate; hence, they would be the first choice for such applications but are often inappropriate. Behavioral biometrics, like biometric typing patterns, have the potential to fill this gap as another level of security but this research identified some deficiencies in performance quality. Two research streams for improvements have emerged. The first approach attempts to improve performance by building better classifiers, while the second attempts to attain the same goal by using richer identifying inputs. Both streams assume that the typing biometric patterns are stable over time. This study investigates the validity of this assumption by analyzing how students’ typing patterns behave over time. The results demonstrate that typing patterns change over time due to learning resulting in several performance quality challenges. First, the changing patterns lead to deteriorating authentication accuracy. Second, the relevancy of the reference biometric template created during training becomes questionable. Third, the deterioration in accuracy compromises the security of the whole system and fourth, the net effect brings to question whether the biometric keypad is no longer “fit for use” as an authentication system. These are critical data quality issues that need to be addressed if behavioral biometrics are to play a significant role in minimizing authentication fraud. Possible solutions to the problem, including biometric template updating and choice of uncorrelated PIN combinations, are suggested as potential topics for future research.
ISSN 19361955
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2011-02-01
Publisher Place New York
e-ISSN 19361963
Journal Journal of Data and Information Quality (JDIQ)
Volume Number 2
Issue Number 2
Page Count 21
Starting Page 1
Ending Page 21

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Source: ACM Digital Library