Verifying identity via keystroke characteristics
International Journal of Man-Machine Studies
Verification of user identity via keyboard characteristics
Human factors in management information systems
Identity authentication based on keystroke latencies
Communications of the ACM
International Journal of Human-Computer Studies
Keystroke dynamics as a biometric for authentication
Future Generation Computer Systems - Special issue on security on the Web
User authentication through keystroke dynamics
ACM Transactions on Information and System Security (TISSEC)
Wearable Robotics as a Behavioral Interface - The Study of the Parasitic Humanoid
ISWC '02 Proceedings of the 6th IEEE International Symposium on Wearable Computers
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Biometrics and Network Security
Biometrics and Network Security
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Keystroke analysis of free text
ACM Transactions on Information and System Security (TISSEC)
Keyboard acoustic emanations revisited
Proceedings of the 12th ACM conference on Computer and communications security
Learning User Profile from Traces
SAINT-W '05 Proceedings of the 2005 Symposium on Applications and the Internet Workshops
Beyond accuracy: what data quality means to data consumers
Journal of Management Information Systems
Principles of Information Security
Principles of Information Security
Overview and Framework for Data and Information Quality Research
Journal of Data and Information Quality (JDIQ)
Towards a Method for Data Accuracy Assessment Utilizing a Bayesian Network Learning Algorithm
Journal of Data and Information Quality (JDIQ)
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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.