Identity authentication based on keystroke latencies
Communications of the ACM
Authentication via keystroke dynamics
Proceedings of the 4th ACM conference on Computer and communications security
Password hardening based on keystroke dynamics
CCS '99 Proceedings of the 6th ACM conference on Computer and communications security
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Proceedings of the 2004 ACM workshop on Visualization and data mining for computer security
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Keystroke statistical learning model for web authentication
ASIACCS '07 Proceedings of the 2nd ACM symposium on Information, computer and communications security
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An evolutionary keystroke authentication based on ellipsoidal hypothesis space
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Proceedings of the Seventh Symposium on Usable Privacy and Security
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In this paper we focus our research on user identification rather than user verification by analyzing handwritten signature and haptic information such as pressure. For analysis, a multilayer perception (MLP) neural network is adopted. In order to verify the proposed method, 16 users' signatures were measured with haptic information. We successfully identified users at an average success rate of 81%.