论文信息 - Capturing Cognitive Fingerprints from Keystroke Dynamics

Capturing Cognitive Fingerprints from Keystroke Dynamics

Conventional authentication systems identify a user only at the entry point. Keystroke dynamics can continuously authenticate users by their typing rhythms without extra devices. This article presents a new feature called cognitive typing rhythm (CTR) to continuously verify the identities of computer users. Two machine techniques, SVM and KRR, have been developed for the system. The best results from experiments conducted with 1,977 users show a false-rejection rate of 0.7 percent and a false-acceptance rate of 5.5 percent. CTR therefore constitutes a cognitive fingerprint for continuous. Its effectiveness has been verified through a large-scale dataset. This article is part of a special issue on security.

[1] Claudia Picardi,et al. Keystroke analysis of free text , 2005, TSEC.

[2] Michael K. Reiter,et al. Password hardening based on keystroke dynamics , 2002, International Journal of Information Security.

[3] D. McCutchen. A capacity theory of writing: Working memory in composition , 1996 .

[4] Annie Piolat,et al. Verbal, Visual, and Spatial Working Memory Demands during Text Composition. , 2008 .

[5] Thierry Olive,et al. Working Memory in Writing: Empirical Evidence From the Dual-Task Technique , 2004 .

[6] Claudia Picardi,et al. User authentication through keystroke dynamics , 2002, TSEC.

[7] C. Michael Levy,et al. The Science of Writing : Theories, Methods, Individual Differences and Applications , 1996 .

[8] D. McCutchen. Knowledge, Processing, and Working Memory: Implications for a Theory of Writing , 2000 .

[9] S. Kung. Kernel Methods and Machine Learning , 2014 .

[10] M. Akila,et al. Biometric personal authentication using keystroke dynamics: A review , 2011, Appl. Soft Comput..

[11] Chih-Jen Lin,et al. LIBSVM: A library for support vector machines , 2011, TIST.